[53/73] [abbrv] Rename documentation

[se...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/internal_add_operator.md
----------------------------------------------------------------------
diff --git a/docs/internal_add_operator.md b/docs/internal_add_operator.md
index dba34cd..b24df54 100644
--- a/docs/internal_add_operator.md
+++ b/docs/internal_add_operator.md
@@ -16,7 +16,7 @@ new functionality does require a new runtime operator, or it is much more effici
 
 Many operators can be implemented as a specialization of another operator, or by means of a UDF.
 
-The simplest example are the `sum()`, `min()`, and `max()` functions on the {% gh_link /stratosphere-java/src/main/java/eu/stratosphere/api/java/DataSet.java "DataSet" %}. These functions simply call other operations
+The simplest example are the `sum()`, `min()`, and `max()` functions on the {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/DataSet.java "DataSet" %}. These functions simply call other operations
 with some pre-defined parameters:
 ```
 public AggregateOperator<T> sum (int field) {
@@ -28,7 +28,7 @@ public AggregateOperator<T> sum (int field) {
 Some operations can be implemented as compositions of multiple other operators. An example is to implement a
 *count()* function through a combination of *map* and *aggregate*. 
 
-A simple way to do this is to define a function on the {% gh_link /stratosphere-java/src/main/java/eu/stratosphere/api/java/DataSet.java "DataSet" %} that calls *map(...)* and *reduce(...)* in turn:
+A simple way to do this is to define a function on the {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/DataSet.java "DataSet" %} that calls *map(...)* and *reduce(...)* in turn:
 ```
 public DataSet<Long> count() {
     return this.map(new MapFunction<T, Long>() {
@@ -54,9 +54,9 @@ public static <T>DataSet<Long> count(DataSet<T> data) {
 
 ### More Complex Operators
 
-A more complex example of an operation via specialization is the {% gh_link /stratosphere-java/src/main/java/eu/stratosphere/api/java/operators/AggregateOperator.java "Aggregation Operation" %} in the Java API. It is implemented by means of a *GroupReduce* UDF.
+A more complex example of an operation via specialization is the {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/operators/AggregateOperator.java "Aggregation Operation" %} in the Java API. It is implemented by means of a *GroupReduce* UDF.
 
-The Aggregate Operation comes with its own operator in the *Java API*, but translates itself into a {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/operators/base/GroupReduceOperatorBase.java "GroupReduceOperatorBase" %} in the *Common API*. (see [Program Life Cycle](program_life_cycle.html) for details of how an operation from the *Java API* becomes an operation of the *Common API* and finally a runtime operation.)
+The Aggregate Operation comes with its own operator in the *Java API*, but translates itself into a {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/operators/base/GroupReduceOperatorBase.java "GroupReduceOperatorBase" %} in the *Common API*. (see [Program Life Cycle](program_life_cycle.html) for details of how an operation from the *Java API* becomes an operation of the *Common API* and finally a runtime operation.)
 The Java API aggregation operator is only a builder that takes the types of aggregations and the field positions, and used that information to
 parameterize the GroupReduce UDF that performs the aggregations.
 
@@ -74,7 +74,7 @@ void setInput(DataSet<IN> inputData);
 DataSet<OUT> createResult();
 ```
 
-The {% gh_link /stratosphere-addons/spargel/src/main/java/eu/stratosphere/spargel/java/VertexCentricIteration.java "VertexCentricIteration" %} operator is implemented that way. Below is an example how to implement the *count()* operator that way.
+The {% gh_link /flink-addons/spargel/src/main/java/org/apache/flink/spargel/java/VertexCentricIteration.java "VertexCentricIteration" %} operator is implemented that way. Below is an example how to implement the *count()* operator that way.
 
 ``` java
 public class Counter<T> implements CustomUnaryOperation<T, Long> {
@@ -109,7 +109,7 @@ function, but invoked only once per parallel partition.
 
 **Runtime**
 
-Runtime Operators are implemented using the {% gh_link /stratosphere-runtime/src/main/java/eu/stratosphere/pact/runtime/task/PactDriver.java "Driver" %} interface. The interface defines the methods that describe the operator towards the runtime. The {% gh_link /stratosphere-runtime/src/main/java/eu/stratosphere/pact/runtime/task/MapDriver.java "MapDriver" %} serves as a simple example of how those operators work.
+Runtime Operators are implemented using the {% gh_link /flink-runtime/src/main/java/org/apache/flink/pact/runtime/task/PactDriver.java "Driver" %} interface. The interface defines the methods that describe the operator towards the runtime. The {% gh_link /flink-runtime/src/main/java/org/apache/flink/pact/runtime/task/MapDriver.java "MapDriver" %} serves as a simple example of how those operators work.
 
 The runtime works with the `MutableObjectIterator`, which describes data streams with the ability to reuse objects, to reduce pressure on the garbage collector.
 
@@ -132,8 +132,8 @@ To increase efficiency, it is often beneficial to implement a *chained* version
 operators run in the same thread as their preceding operator, and work with nested function calls.
 This is very efficient, because it saves serialization/deserialization overhead.
 
-To learn how to implement a chained operator, take a look at the {% gh_link /stratosphere-runtime/src/main/java/eu/stratosphere/pact/runtime/task/MapDriver.java "MapDriver" %} (regular) and the
-{% gh_link /stratosphere-runtime/src/main/java/eu/stratosphere/pact/runtime/task/chaining/ChainedMapDriver.java "ChainedMapDriver" %} (chained variant).
+To learn how to implement a chained operator, take a look at the {% gh_link /flink-runtime/src/main/java/org/apache/flink/pact/runtime/task/MapDriver.java "MapDriver" %} (regular) and the
+{% gh_link /flink-runtime/src/main/java/org/apache/flink/pact/runtime/task/chaining/ChainedMapDriver.java "ChainedMapDriver" %} (chained variant).
 
 
 **Optimizer/Compiler**
@@ -141,15 +141,15 @@ To learn how to implement a chained operator, take a look at the {% gh_link /str
 This section does a minimal discussion of the important steps to add an operator. Please see the [Optimizer](optimizer.html) docs for more detail on how the optimizer works.
 To allow the optimizer to include a new operator in its planning, it needs a bit of information about it; in particular, the following information:
 
-- *{% gh_link /stratosphere-runtime/src/main/java/eu/stratosphere/pact/runtime/task/DriverStrategy.java "DriverStrategy" %}*: The operation needs to be added to the Enum, to make it available to the optimizer. The parameters to the Enum entry define which class implements the runtime operator, its chained version, whether the operator accumulates records (and needs memory for that), and whether it requires a comparator (works on keys). For our example, we can add the entry
+- *{% gh_link /flink-runtime/src/main/java/org/apache/flink/pact/runtime/task/DriverStrategy.java "DriverStrategy" %}*: The operation needs to be added to the Enum, to make it available to the optimizer. The parameters to the Enum entry define which class implements the runtime operator, its chained version, whether the operator accumulates records (and needs memory for that), and whether it requires a comparator (works on keys). For our example, we can add the entry
 ``` java
 MAP_PARTITION(MapPartitionDriver.class, null /* or chained variant */, PIPELINED, false)
 ```
 
-- *Cost function*: The class {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/costs/CostEstimator.java "CostEstimator" %} needs to know how expensive the operation is to the system. The costs here refer to the non-UDF part of the operator. Since the operator does essentially no work (it forwards the record stream to the UDF), the costs are zero. We change the `costOperator(...)` method by adding the *MAP_PARTITION* constant to the switch statement similar to the *MAP* constant such that no cost is accounted for it.
+- *Cost function*: The class {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/costs/CostEstimator.java "CostEstimator" %} needs to know how expensive the operation is to the system. The costs here refer to the non-UDF part of the operator. Since the operator does essentially no work (it forwards the record stream to the UDF), the costs are zero. We change the `costOperator(...)` method by adding the *MAP_PARTITION* constant to the switch statement similar to the *MAP* constant such that no cost is accounted for it.
 
-- *OperatorDescriptor*: The operator descriptors define how an operation needs to be treated by the optimizer. They describe how the operation requires the input data to be (e.g., sorted or partitioned) and that way allows the optimizer to optimize the data movement, sorting, grouping in a global fashion. They do that by describing which {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/dataproperties/RequestedGlobalProperties.java "RequestedGlobalProperties" %} (partitioning, replication, etc) and which {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/dataproperties/RequestedLocalProperties.java "RequestedLocalProperties" %} (sorting, grouping, uniqueness) the operator has, as well as how the operator affects the existing {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/dataproperties/GlobalProperties.java "GlobalProperties" %} and {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/dataprop
 erties/LocalProperties.java "LocalProperties" %}. In addition, it defines a few utility methods, for example to instantiate an operator candidate.
-Since the *mapPartition()* function is very simple (no requirements on partitioning/grouping), the descriptor is very simple. Other operators have more complex requirements, for example the {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/operators/GroupReduceProperties.java "GroupReduce" %}. Some operators, like *join* have multiple ways in which they can be executed and therefore have multiple descriptors ({% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/operators/HashJoinBuildFirstProperties.java "Hash Join 1" %}, {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/operators/HashJoinBuildSecondProperties.java "Hash Join 2" %}, {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/operators/SortMergeJoinDescriptor.java "SortMerge Join" %}).
+- *OperatorDescriptor*: The operator descriptors define how an operation needs to be treated by the optimizer. They describe how the operation requires the input data to be (e.g., sorted or partitioned) and that way allows the optimizer to optimize the data movement, sorting, grouping in a global fashion. They do that by describing which {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/RequestedGlobalProperties.java "RequestedGlobalProperties" %} (partitioning, replication, etc) and which {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/RequestedLocalProperties.java "RequestedLocalProperties" %} (sorting, grouping, uniqueness) the operator has, as well as how the operator affects the existing {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/GlobalProperties.java "GlobalProperties" %} and {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dataproperties/LocalProperties.j
 ava "LocalProperties" %}. In addition, it defines a few utility methods, for example to instantiate an operator candidate.
+Since the *mapPartition()* function is very simple (no requirements on partitioning/grouping), the descriptor is very simple. Other operators have more complex requirements, for example the {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/GroupReduceProperties.java "GroupReduce" %}. Some operators, like *join* have multiple ways in which they can be executed and therefore have multiple descriptors ({% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/HashJoinBuildFirstProperties.java "Hash Join 1" %}, {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/HashJoinBuildSecondProperties.java "Hash Join 2" %}, {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/operators/SortMergeJoinDescriptor.java "SortMerge Join" %}).
 The code sample below explains (with comments) how to create a descriptor for the *MapPartitionOperator*
 ``` java
     public DriverStrategy getStrategy() {
@@ -185,16 +185,16 @@ The code sample below explains (with comments) how to create a descriptor for th
     }
 ```
 
-- *OptimizerNode*: The optimizer node is the place where all comes together. It creates the list of *OperatorDescriptors*, implements the result data set size estimation, and assigns a name to the operation. It is a relatively small class and can be more or less copied again from the {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/dag/MapNode.java "MapNode" %}.
+- *OptimizerNode*: The optimizer node is the place where all comes together. It creates the list of *OperatorDescriptors*, implements the result data set size estimation, and assigns a name to the operation. It is a relatively small class and can be more or less copied again from the {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/dag/MapNode.java "MapNode" %}.
 
 
 **Common API**
 
 To make the operation available to the higher-level APIs, it needs to be added to the Common API. The simplest way to do this is to add a
-base operator. Create a class `MapPartitionOperatorBase`, after the pattern of the {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/operators/base/MapOperatorBase.java "MapOperatorBase" %}.
+base operator. Create a class `MapPartitionOperatorBase`, after the pattern of the {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/operators/base/MapOperatorBase.java "MapOperatorBase" %}.
 
 In addition, the optimizer needs to know which OptimizerNode how to create an OptimizerNode from the OperatorBase. This happens in the class
-`GraphCreatingVisitor` in the {% gh_link /stratosphere-compiler/src/main/java/eu/stratosphere/compiler/PactCompiler.java "Optimizer" %}.
+`GraphCreatingVisitor` in the {% gh_link /flink-compiler/src/main/java/org/apache/flink/compiler/PactCompiler.java "Optimizer" %}.
 
 *Note:* A pending idea is to allow to skip this step by unifying the OptimizerNode and the Common API operator. They essentially fulfill the
 same function. The Common API operator exists only in order for the `flink-java` and `flink-scala` packages to not have a dependency on the
@@ -203,7 +203,7 @@ optimizer.
 
 **Java API**
 
-Create a Java API operator that is constructed in the same way as the {% gh_link /stratosphere-java/src/main/java/eu/stratosphere/api/java/operators/MapOperator.java "MapOperator" %}. The core method is the `translateToDataFlow(...)` method, which creates the Common API operator for the Java API operator.
+Create a Java API operator that is constructed in the same way as the {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/operators/MapOperator.java "MapOperator" %}. The core method is the `translateToDataFlow(...)` method, which creates the Common API operator for the Java API operator.
 
 The final step is to add a function to the `DataSet` class:
 ``` java

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/iterations.md
----------------------------------------------------------------------
diff --git a/docs/iterations.md b/docs/iterations.md
index c56e0f5..0517322 100644
--- a/docs/iterations.md
+++ b/docs/iterations.md
@@ -4,7 +4,7 @@ title:  "Iterations"
 
 Iterative algorithms occur in many domains of data analysis, such as *machine learning* or *graph analysis*. Such algorithms are crucial in order to realize the promise of Big Data to extract meaningful information out of your data. With increasing interest to run these kinds of algorithms on very large data sets, there is a need to execute iterations in a massively parallel fashion.
 
-Stratosphere programs implement iterative algorithms by defining a **step function** and embedding it into a special iteration operator. There are two  variants of this operator: **Iterate** and **Delta Iterate**. Both operators repeatedly invoke the step function on the current iteration state until a certain termination condition is reached.
+Flink programs implement iterative algorithms by defining a **step function** and embedding it into a special iteration operator. There are two  variants of this operator: **Iterate** and **Delta Iterate**. Both operators repeatedly invoke the step function on the current iteration state until a certain termination condition is reached.
 
 Here, we provide background on both operator variants and outline their usage. The [programming guides](java_api_guide.html) explain how to implement the operators in both [Scala](scala_api_guide.html) and [Java](java_api_guide.html#iterations). We also provide a **vertex-centric graph processing API** called [Spargel](spargel_guide.html).
 

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/java_api_examples.md
----------------------------------------------------------------------
diff --git a/docs/java_api_examples.md b/docs/java_api_examples.md
index 8bd5a22..0fffbf9 100644
--- a/docs/java_api_examples.md
+++ b/docs/java_api_examples.md
@@ -2,11 +2,11 @@
 title:  "Java API Examples"
 ---
 
-The following example programs showcase different applications of Stratosphere 
+The following example programs showcase different applications of Flink 
 from simple word counting to graph algorithms. The code samples illustrate the 
-use of [Stratosphere's Java API](java_api_guide.html). 
+use of [Flink's Java API](java_api_guide.html). 
 
-The full source code of the following and more examples can be found in the __stratosphere-java-examples__ module.
+The full source code of the following and more examples can be found in the __flink-java-examples__ module.
 
 # Word Count
 WordCount is the "Hello World" of Big Data processing systems. It computes the frequency of words in a text collection. The algorithm works in two steps: First, the texts are splits the text to individual words. Second, the words are grouped and counted.
@@ -42,7 +42,7 @@ public static final class Tokenizer extends FlatMapFunction<String, Tuple2<Strin
 }
 ```
 
-The {% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/wordcount/WordCount.java  "WordCount example" %} implements the above described algorithm with input parameters: `<text input path>, <output path>`. As test data, any text file will do.
+The {% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/wordcount/WordCount.java  "WordCount example" %} implements the above described algorithm with input parameters: `<text input path>, <output path>`. As test data, any text file will do.
 
 # Page Rank
 
@@ -118,7 +118,7 @@ public static final class EpsilonFilter
 }
 ```
 
-The {% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/graph/PageRankBasic.java "PageRank program" %} implements the above example.
+The {% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/graph/PageRankBasic.java "PageRank program" %} implements the above example.
 It requires the following parameters to run: `<pages input path>, <links input path>, <output path>, <num pages>, <num iterations>`.
 
 Input files are plain text files and must be formatted as follows:
@@ -209,7 +209,7 @@ public static final class ComponentIdFilter
 }
 ```
 
-The {% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/graph/ConnectedComponents.java "ConnectedComponents program" %} implements the above example. It requires the following parameters to run: `<vertex input path>, <edge input path>, <output path> <max num iterations>`.
+The {% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/graph/ConnectedComponents.java "ConnectedComponents program" %} implements the above example. It requires the following parameters to run: `<vertex input path>, <edge input path>, <output path> <max num iterations>`.
 
 Input files are plain text files and must be formatted as follows:
 - Vertices represented as IDs and separated by new-line characters.
@@ -233,7 +233,7 @@ WHERE l_orderkey = o_orderkey
 GROUP BY l_orderkey, o_shippriority;
 ```
 
-The Stratosphere Java program, which implements the above query looks as follows.
+The Flink Java program, which implements the above query looks as follows.
 
 ```java
 // get orders data set: (orderkey, orderstatus, orderdate, orderpriority, shippriority)
@@ -280,10 +280,10 @@ DataSet<Tuple3<Integer, Integer, Double>> priceSums =
 priceSums.writeAsCsv(outputPath);
 ```
 
-The {% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/relational/RelationalQuery.java "Relational Query program" %} implements the above query. It requires the following parameters to run: `<orders input path>, <lineitem input path>, <output path>`.
+The {% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/relational/RelationalQuery.java "Relational Query program" %} implements the above query. It requires the following parameters to run: `<orders input path>, <lineitem input path>, <output path>`.
 
 The orders and lineitem files can be generated using the [TPC-H benchmark](http://www.tpc.org/tpch/) suite's data generator tool (DBGEN). 
-Take the following steps to generate arbitrary large input files for the provided Stratosphere programs:
+Take the following steps to generate arbitrary large input files for the provided Flink programs:
 
 1.  Download and unpack DBGEN
 2.  Make a copy of *makefile.suite* called *Makefile* and perform the following changes:

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/java_api_guide.md
----------------------------------------------------------------------
diff --git a/docs/java_api_guide.md b/docs/java_api_guide.md
index 516636b..0f45c01 100644
--- a/docs/java_api_guide.md
+++ b/docs/java_api_guide.md
@@ -9,9 +9,9 @@ Java API
 Introduction
 ------------
 
-Analysis programs in Stratosphere are regular Java programs that implement transformations on data sets (e.g., filtering, mapping, joining, grouping). The data sets are initially created from certain sources (e.g., by reading files, or from collections). Results are returned via sinks, which may for example write the data to (distributed) files, or to standard output (for example the command line terminal). Stratosphere programs run in a variety of contexts, standalone, or embedded in other programs. The execution can happen in a local JVM, or on clusters of many machines.
+Analysis programs in Flink are regular Java programs that implement transformations on data sets (e.g., filtering, mapping, joining, grouping). The data sets are initially created from certain sources (e.g., by reading files, or from collections). Results are returned via sinks, which may for example write the data to (distributed) files, or to standard output (for example the command line terminal). Flink programs run in a variety of contexts, standalone, or embedded in other programs. The execution can happen in a local JVM, or on clusters of many machines.
 
-In order to create your own Stratosphere program, we encourage you to start with the [program skeleton](#skeleton) and gradually add your own [transformations](#transformations). The remaining sections act as references for additional operations and advanced features.
+In order to create your own Flink program, we encourage you to start with the [program skeleton](#skeleton) and gradually add your own [transformations](#transformations). The remaining sections act as references for additional operations and advanced features.
 
 
 <section id="toc">
@@ -27,7 +27,7 @@ In order to create your own Stratosphere program, we encourage you to start with
 Example Program
 ---------------
 
-The following program is a complete, working example of WordCount. You can copy &amp; paste the code to run it locally. You only have to include Stratosphere's Java API library into your project (see Section [Linking with Stratosphere](#linking)) and specify the imports. Then you are ready to go!
+The following program is a complete, working example of WordCount. You can copy &amp; paste the code to run it locally. You only have to include Flink's Java API library into your project (see Section [Linking with Flink](#linking)) and specify the imports. Then you are ready to go!
 
 ```java
 public class WordCountExample {
@@ -62,21 +62,21 @@ public class WordCountExample {
 [Back to top](#top)
 
 <section id="linking">
-Linking with Stratosphere
+Linking with Flink
 -------------------------
 
-To write programs with Stratosphere, you need to include Stratosphere’s Java API library in your project.
+To write programs with Flink, you need to include Flink’s Java API library in your project.
 
 The simplest way to do this is to use the [quickstart scripts](java_api_quickstart.html). They create a blank project from a template (a Maven Archetype), which sets up everything for you. To manually create the project, you can use the archetype and create a project by calling:
 
 ```bash
 mvn archetype:generate /
-    -DarchetypeGroupId=eu.stratosphere /
+    -DarchetypeGroupId=org.apache.flink/
     -DarchetypeArtifactId=quickstart-java /
     -DarchetypeVersion={{site.FLINK_VERSION_STABLE }}
 ```
 
-If you want to add Stratosphere to an existing Maven project, add the following entry to your *dependencies* section in the *pom.xml* file of your project:
+If you want to add Flink to an existing Maven project, add the following entry to your *dependencies* section in the *pom.xml* file of your project:
 
 ```xml
 <dependency>
@@ -93,7 +93,7 @@ If you want to add Stratosphere to an existing Maven project, add the following
 
 In order to link against the latest SNAPSHOT versions of the code, please follow [this guide]({{site.baseurl}}/downloads.html/#nightly).
 
-The *stratosphere-clients* dependency is only necessary to invoke the Stratosphere program locally (for example to run it standalone for testing and debugging). 
+The *flink-clients* dependency is only necessary to invoke the Flink program locally (for example to run it standalone for testing and debugging). 
 If you intend to only export the program as a JAR file and [run it on a cluster](cluster_execution.html), you can skip that dependency.
 
 [Back to top](#top)
@@ -102,7 +102,7 @@ If you intend to only export the program as a JAR file and [run it on a cluster]
 Program Skeleton
 ----------------
 
-As we already saw in the example, Stratosphere programs look like regular Java
+As we already saw in the example, Flink programs look like regular Java
 programs with a `main()` method. Each program consists of the same basic parts:
 
 1. Obtain an `ExecutionEnvironment`,
@@ -112,9 +112,9 @@ programs with a `main()` method. Each program consists of the same basic parts:
 5. Execute your program.
 
 We will now give an overview of each of those steps but please refer
-to the respective sections for more details. Note that all {% gh_link /stratosphere-java/src/main/java/eu/stratosphere/api/java "core classes of the Java API" %} are found in the package `eu.stratosphere.api.java`.
+to the respective sections for more details. Note that all {% gh_link /flink-java/src/main/java/org/apache/flink/api/java "core classes of the Java API" %} are found in the package `org.apache.flinkapi.java`.
 
-The `ExecutionEnvironment` is the basis for all Stratosphere programs. You can
+The `ExecutionEnvironment` is the basis for all Flink programs. You can
 obtain one using these static methods on class `ExecutionEnvironment`:
 
 ```java
@@ -133,7 +133,7 @@ your program inside an IDE or as a regular Java program it will create
 a local environment that will execute your program on your local machine. If
 you created a JAR file from you program, and invoke it through the [command line](cli.html)
 or the [web interface](web_client.html),
-the Stratosphere cluster manager will
+the Flink cluster manager will
 execute your main method and `getExecutionEnvironment()` will return
 an execution environment for executing your program on a cluster.
 
@@ -206,9 +206,9 @@ how you created the execution environment.
 Lazy Evaluation
 ---------------
 
-All Stratosphere programs are executed lazily: When the program's main method is executed, the data loading and transformations do not happen directly. Rather, each operation is created and added to the program's plan. The operations are actually executed when one of the `execute()` methods is invoked on the ExecutionEnvironment object. Whether the program is executed locally or on a cluster depends on the environment of the program.
+All Flink programs are executed lazily: When the program's main method is executed, the data loading and transformations do not happen directly. Rather, each operation is created and added to the program's plan. The operations are actually executed when one of the `execute()` methods is invoked on the ExecutionEnvironment object. Whether the program is executed locally or on a cluster depends on the environment of the program.
 
-The lazy evaluation lets you construct sophisticated programs that Stratosphere executes as one holistically planned unit.
+The lazy evaluation lets you construct sophisticated programs that Flink executes as one holistically planned unit.
 
 <section id="types">
 Data Types
@@ -286,7 +286,7 @@ wordCounts.groupBy(new KeySelector<WordCount, String>() {
 
 #### Tuples
 
-You can use the `Tuple` classes for composite types. Tuples contain a fix number of fields of various types. The Java API provides classes from `Tuple1` up to `Tuple25`. Every field of a tuple can be an arbitrary Stratosphere type - including further tuples, resulting in nested tuples. Fields of a Tuple can be accessed directly using the fields `tuple.f4`, or using the generic getter method `tuple.getField(int position)`. The field numbering starts with 0. Note that this stands in contrast to the Scala tuples, but it is more consistent with Java's general indexing.
+You can use the `Tuple` classes for composite types. Tuples contain a fix number of fields of various types. The Java API provides classes from `Tuple1` up to `Tuple25`. Every field of a tuple can be an arbitrary Flink type - including further tuples, resulting in nested tuples. Fields of a Tuple can be accessed directly using the fields `tuple.f4`, or using the generic getter method `tuple.getField(int position)`. The field numbering starts with 0. Note that this stands in contrast to the Scala tuples, but it is more consistent with Java's general indexing.
 
 ```java
 DataSet<Tuple2<String, Integer>> wordCounts = env.fromElements(
@@ -311,16 +311,16 @@ wordCounts
     .reduce(new MyReduceFunction());
 ```
 
-In order to access fields more intuitively and to generate more readable code, it is also possible to extend a subclass of `Tuple`. You can add getters and setters with custom names that delegate to the field positions. See this {% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/relational/TPCHQuery3.java "example" %} for an illustration how to make use of that mechanism.
+In order to access fields more intuitively and to generate more readable code, it is also possible to extend a subclass of `Tuple`. You can add getters and setters with custom names that delegate to the field positions. See this {% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/relational/TPCHQuery3.java "example" %} for an illustration how to make use of that mechanism.
 
 
 #### Values
 
-*Value* types describe their serialization and deserialization manually. Instead of going through a general purpose serialization framework, they provide custom code for those operations by means implementing the `eu.stratosphere.types.Value` interface with the methods `read` and `write`. Using a *Value* type is reasonable when general purpose serialization would be highly inefficient. An example would be a data type that implements a sparse vector of elements as an array. Knowing that the array is mostly zero, one can use a special encoding for the non-zero elements, while the general purpose serialization would simply write all array elements.
+*Value* types describe their serialization and deserialization manually. Instead of going through a general purpose serialization framework, they provide custom code for those operations by means implementing the `org.apache.flinktypes.Value` interface with the methods `read` and `write`. Using a *Value* type is reasonable when general purpose serialization would be highly inefficient. An example would be a data type that implements a sparse vector of elements as an array. Knowing that the array is mostly zero, one can use a special encoding for the non-zero elements, while the general purpose serialization would simply write all array elements.
 
-The `eu.stratosphere.types.CopyableValue` interface supports manual internal cloning logic in a similar way.
+The `org.apache.flinktypes.CopyableValue` interface supports manual internal cloning logic in a similar way.
 
-Stratosphere comes with pre-defined Value types that correspond to Java's basic data types. (`ByteValue`, `ShortValue`, `IntValue`, `LongValue`, `FloatValue`, `DoubleValue`, `StringValue`, `CharValue`, `BooleanValue`). These Value types act as mutable variants of the basic data types: Their value can be altered, allowing programmers to reuse objects and take pressure off the garbage collector. 
+Flink comes with pre-defined Value types that correspond to Java's basic data types. (`ByteValue`, `ShortValue`, `IntValue`, `LongValue`, `FloatValue`, `DoubleValue`, `StringValue`, `CharValue`, `BooleanValue`). These Value types act as mutable variants of the basic data types: Their value can be altered, allowing programmers to reuse objects and take pressure off the garbage collector. 
 
 
 #### Hadoop Writables
@@ -332,11 +332,11 @@ You can use types that implement the `org.apache.hadoop.Writable` interface. The
 
 The Java compiler throws away much of the generic type information after the compilation. This is known as *type erasure* in Java. It means that at runtime, an instance of an object does not know its generic type any more. For example, instances of `DataSet<String>` and `DataSet<Long>` look the same to the JVM.
 
-Stratosphere requires type information at the time when it prepares the program for execution (when the main method of the program is called). The Stratosphere Java API tries to reconstruct the type information that was thrown away in various ways and store it explicitly in the data sets and operators. You can retrieve the type via `DataSet.getType()`. The method returns an instance of `TypeInformation`, which is Stratosphere's internal way of representing types.
+Flink requires type information at the time when it prepares the program for execution (when the main method of the program is called). The Flink Java API tries to reconstruct the type information that was thrown away in various ways and store it explicitly in the data sets and operators. You can retrieve the type via `DataSet.getType()`. The method returns an instance of `TypeInformation`, which is Flink's internal way of representing types.
 
 The type inference has its limits and needs the "cooperation" of the programmer in some cases. Examples for that are methods that create data sets from collections, such as `ExecutionEnvironment.fromCollection(),` where you can pass an argument that describes the type. But also generic functions like `MapFunction<I, O>` may need extra type information.
 
-The {% gh_link /stratosphere-java/src/main/java/eu/stratosphere/api/java/typeutils/ResultTypeQueryable.java "ResultTypeQueryable" %} interface can be implemented by input formats and functions to tell the API explicitly about their return type. The *input types* that the functions are invoked with can usually be inferred by the result types of the previous operations.
+The {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/typeutils/ResultTypeQueryable.java "ResultTypeQueryable" %} interface can be implemented by input formats and functions to tell the API explicitly about their return type. The *input types* that the functions are invoked with can usually be inferred by the result types of the previous operations.
 
 [Back to top](#top)
 
@@ -540,7 +540,7 @@ DataSet<Tuple2<Integer, String>> output =
                                  .reduceGroup(new DistinctReduce());
 ```
 
-**Note:** Stratosphere internally works a lot with mutable objects. Collecting objects like in the above example only works because Strings are immutable in Java!
+**Note:** Flink internally works a lot with mutable objects. Collecting objects like in the above example only works because Strings are immutable in Java!
 
 #### GroupReduce on DataSet grouped by KeySelector Function
 
@@ -991,7 +991,7 @@ DataSet<Tuple2<String, Integer>> unioned = vals1.union(vals2)
 Data Sources
 ------------
 
-Data sources create the initial data sets, such as from files or from Java collections. The general mechanism of of creating data sets is abstracted behind an {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/io/InputFormat.java "InputFormat" %}. Stratosphere comes with several built-in formats to create data sets from common file formats. Many of them have shortcut methods on the *ExecutionEnvironment*.
+Data sources create the initial data sets, such as from files or from Java collections. The general mechanism of of creating data sets is abstracted behind an {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/io/InputFormat.java "InputFormat" %}. Flink comes with several built-in formats to create data sets from common file formats. Many of them have shortcut methods on the *ExecutionEnvironment*.
 
 File-based:
 
@@ -1050,7 +1050,7 @@ DataSet<Tuple2<String, Integer> dbData =
       new TupleTypeInfo(Tuple2.class, STRING_TYPE_INFO, INT_TYPE_INFO)
     );
 
-// Note: Stratosphere's program compiler needs to infer the data types of the data items which are returned by an InputFormat. If this information cannot be automatically inferred, it is necessary to manually provide the type information as shown in the examples above.
+// Note: Flink's program compiler needs to infer the data types of the data items which are returned by an InputFormat. If this information cannot be automatically inferred, it is necessary to manually provide the type information as shown in the examples above.
 ```
 
 [Back to top](#top)
@@ -1060,7 +1060,7 @@ DataSet<Tuple2<String, Integer> dbData =
 Data Sinks
 ----------
 
-Data sinks consume DataSets and are used to store or return them. Data sink operations are described using an {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/io/OutputFormat.java "OutputFormat" %}. Stratosphere comes with a variety of built-in output formats that
+Data sinks consume DataSets and are used to store or return them. Data sink operations are described using an {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/io/OutputFormat.java "OutputFormat" %}. Flink comes with a variety of built-in output formats that
 are encapsulated behind operations on the DataSet type:
 
 - `writeAsText()` / `TextOuputFormat` - Writes for each element as a String in a line. The String are obtained by calling the *toString()* method.
@@ -1122,12 +1122,12 @@ Debugging
 Before running a data analysis program on a large data set in a distributed cluster, it is a good idea to make sure that the implemented algorithm works as desired. Hence, implementing data analysis programs is usually an incremental process of checking results, debugging, and improving. 
 
 <p>
-Stratosphere provides a few nice features to significantly ease the development process of data analysis programs by supporting local debugging from within an IDE, injection of test data, and collection of result data. This section give some hints how to ease the development of Stratosphere programs.
+Flink provides a few nice features to significantly ease the development process of data analysis programs by supporting local debugging from within an IDE, injection of test data, and collection of result data. This section give some hints how to ease the development of Flink programs.
 </p>
 
 ### Local Execution Environment
 
-A `LocalEnvironment` starts a Stratosphere system within the same JVM process it was created in. If you start the LocalEnvironement from an IDE, you can set breakpoint in your code and easily debug your program. 
+A `LocalEnvironment` starts a Flink system within the same JVM process it was created in. If you start the LocalEnvironement from an IDE, you can set breakpoint in your code and easily debug your program. 
 
 <p>
 A LocalEnvironment is created and used as follows:
@@ -1145,7 +1145,7 @@ env.execute();
 
 ### Collection Data Sources and Sinks
 
-Providing input for an analysis program and checking its output is cumbersome done by creating input files and reading output files. Stratosphere features special data sources and sinks which are backed by Java collections to ease testing. Once a program has been tested, the sources and sinks can be easily replaced by sources and sinks that read from / write to external data stores such as HDFS.
+Providing input for an analysis program and checking its output is cumbersome done by creating input files and reading output files. Flink features special data sources and sinks which are backed by Java collections to ease testing. Once a program has been tested, the sources and sinks can be easily replaced by sources and sinks that read from / write to external data stores such as HDFS.
 
 Collection data sources can be used as follows:
 
@@ -1184,7 +1184,7 @@ myResult.output(new LocalCollectionOutputFormat(outData));
 Iteration Operators
 -------------------
 
-Iterations implement loops in Stratosphere programs. The iteration operators encapsulate a part of the program and execute it repeatedly, feeding back the result of one iteration (the partial solution) into the next iteration. There are two types of iterations in Stratosphere: **BulkIteration** and **DeltaIteration**.
+Iterations implement loops in Flink programs. The iteration operators encapsulate a part of the program and execute it repeatedly, feeding back the result of one iteration (the partial solution) into the next iteration. There are two types of iterations in Flink: **BulkIteration** and **DeltaIteration**.
 
 This section provides quick examples on how to use both operators. Check out the [Introduction to Iterations](iterations.html) page for a more detailed introduction.
 
@@ -1225,7 +1225,7 @@ count.map(new MapFunction<Integer, Double>() {
 env.execute("Iterative Pi Example");
 {% endhighlight %}
 
-You can also check out the {% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/clustering/KMeans.java "K-Means example" %}, which uses a BulkIteration to cluster a set of unlabeled points.
+You can also check out the {% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/clustering/KMeans.java "K-Means example" %}, which uses a BulkIteration to cluster a set of unlabeled points.
 
 #### Delta Iterations
 
@@ -1343,7 +1343,7 @@ data.map(new MapFunction<String, String>() {
 ```
 
 Make sure that the names (`broadcastSetName` in the previous example) match when registering and accessing broadcasted data sets. For a complete example program, have a look at
-{% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/clustering/KMeans.java#L96 "KMeans Algorithm" %}.
+{% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/clustering/KMeans.java#L96 "KMeans Algorithm" %}.
 
 **Note**: As the content of broadcast variables is kept in-memory on each node, it should not become too large. For simpler things like scalar values you can simply make parameters part of the closure of a function, or use the `withParameters(...)` method to pass in a configuration.
 
@@ -1354,18 +1354,18 @@ Make sure that the names (`broadcastSetName` in the previous example) match when
 Program Packaging & Distributed Execution
 -----------------------------------------
 
-As described in the [program skeleton](#skeleton) section, Stratosphere programs can be executed on clusters by using the `RemoteEnvironment`. Alternatively, programs can be packaged into JAR Files (Java Archives) for execution. Packaging the program is a prerequisite to executing them through the [command line interface](cli.html) or the [web interface](web_client.html).
+As described in the [program skeleton](#skeleton) section, Flink programs can be executed on clusters by using the `RemoteEnvironment`. Alternatively, programs can be packaged into JAR Files (Java Archives) for execution. Packaging the program is a prerequisite to executing them through the [command line interface](cli.html) or the [web interface](web_client.html).
 
 #### Packaging Programs
 
-To support execution from a packaged JAR file via the command line or web interface, a program must use the environment obtained by `ExecutionEnvironment.getExecutionEnvironment()`. This environment will act as the cluster's environment when the JAR is submitted to the command line or web interface. If the Stratosphere program is invoked differently than through these interfaces, the environment will act like a local environment.
+To support execution from a packaged JAR file via the command line or web interface, a program must use the environment obtained by `ExecutionEnvironment.getExecutionEnvironment()`. This environment will act as the cluster's environment when the JAR is submitted to the command line or web interface. If the Flink program is invoked differently than through these interfaces, the environment will act like a local environment.
 
-To package the program, simply export all involved classes as a JAR file. The JAR file's manifest must point to the class that contains the program's *entry point* (the class with the `public void main(String[])` method). The simplest way to do this is by putting the *main-class* entry into the manifest (such as `main-class: eu.stratosphere.example.MyProgram`). The *main-class* attribute is the same one that is used by the Java Virtual Machine to find the main method when executing a JAR files through the command `java -jar pathToTheJarFile`. Most IDEs offer to include that attribute automatically when exporting JAR files.
+To package the program, simply export all involved classes as a JAR file. The JAR file's manifest must point to the class that contains the program's *entry point* (the class with the `public void main(String[])` method). The simplest way to do this is by putting the *main-class* entry into the manifest (such as `main-class: org.apache.flinkexample.MyProgram`). The *main-class* attribute is the same one that is used by the Java Virtual Machine to find the main method when executing a JAR files through the command `java -jar pathToTheJarFile`. Most IDEs offer to include that attribute automatically when exporting JAR files.
 
 
 #### Packaging Programs through Plans
 
-Additionally, the Java API supports packaging programs as *Plans*. This method resembles the way that the *Scala API* packages programs. Instead of defining a progam in the main method and calling `execute()` on the environment, plan packaging returns the *Program Plan*, which is a description of the program's data flow. To do that, the program must implement the `eu.stratosphere.api.common.Program` interface, defining the `getPlan(String...)` method. The strings passed to that method are the command line arguments. The program's plan can be created from the environment via the `ExecutionEnvironment#createProgramPlan()` method. When packaging the program's plan, the JAR manifest must point to the class implementing the `eu.stratosphere.api.common.Program` interface, instead of the class with the main method.
+Additionally, the Java API supports packaging programs as *Plans*. This method resembles the way that the *Scala API* packages programs. Instead of defining a progam in the main method and calling `execute()` on the environment, plan packaging returns the *Program Plan*, which is a description of the program's data flow. To do that, the program must implement the `org.apache.flinkapi.common.Program` interface, defining the `getPlan(String...)` method. The strings passed to that method are the command line arguments. The program's plan can be created from the environment via the `ExecutionEnvironment#createProgramPlan()` method. When packaging the program's plan, the JAR manifest must point to the class implementing the `org.apache.flinkapi.common.Program` interface, instead of the class with the main method.
 
 
 #### Summary
@@ -1373,8 +1373,8 @@ Additionally, the Java API supports packaging programs as *Plans*. This method r
 The overall procedure to invoke a packaged program is as follows:
 
   1. The JAR's manifest is searched for a *main-class* or *program-class* attribute. If both attributes are found, the *program-class* attribute takes precedence over the *main-class* attribute. Both the command line and the web interface support a parameter to pass the entry point class name manually for cases where the JAR manifest contains neither attribute.
-  2. If the entry point class implements the `eu.stratosphere.api.common.Program`, then the system calls the `getPlan(String...)` method to obtain the program plan to execute. The `getPlan(String...)` method was the only possible way of defining a program in the *Record API* (see [0.4 docs](http://stratosphere.eu/docs/0.4/)) and is also supported in the new Java API.
-  3. If the entry point class does not implement the `eu.stratosphere.api.common.Program` interface, the system will invoke the main method of the class.
+  2. If the entry point class implements the `org.apache.flinkapi.common.Program`, then the system calls the `getPlan(String...)` method to obtain the program plan to execute. The `getPlan(String...)` method was the only possible way of defining a program in the *Record API* (see [0.4 docs](http://stratosphere.eu/docs/0.4/)) and is also supported in the new Java API.
+  3. If the entry point class does not implement the `org.apache.flinkapi.common.Program` interface, the system will invoke the main method of the class.
 
 [Back to top](#top)
 
@@ -1385,12 +1385,12 @@ Accumulators & Counters
 
 Accumulators are simple constructs with an **add operation** and a **final accumulated result**, which is available after the job ended.
 
-The most straightforward accumulator is a **counter**: You can increment it using the ```Accumulator.add(V value)``` method. At the end of the job Stratosphere will sum up (merge) all partial results and send the result to the client. Since accumulators are very easy to use, they can be useful during debugging or if you quickly want to find out more about your data.
+The most straightforward accumulator is a **counter**: You can increment it using the ```Accumulator.add(V value)``` method. At the end of the job Flink will sum up (merge) all partial results and send the result to the client. Since accumulators are very easy to use, they can be useful during debugging or if you quickly want to find out more about your data.
 
-Stratosphere currently has the following **built-in accumulators**. Each of them implements the {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/accumulators/Accumulator.java "Accumulator" %} interface.
+Flink currently has the following **built-in accumulators**. Each of them implements the {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/accumulators/Accumulator.java "Accumulator" %} interface.
 
-- {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/accumulators/IntCounter.java "__IntCounter__" %}, {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/accumulators/LongCounter.java "__LongCounter__" %} and {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/accumulators/DoubleCounter.java "__DoubleCounter__" %}: See below for an example using a counter.
-- {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/accumulators/Histogram.java "__Histogram__" %}: A histogram implementation for a discrete number of bins. Internally it is just a map from Integer to Integer. You can use this to compute distributions of values, e.g. the distribution of words-per-line for a word count program.
+- {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/accumulators/IntCounter.java "__IntCounter__" %}, {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/accumulators/LongCounter.java "__LongCounter__" %} and {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/accumulators/DoubleCounter.java "__DoubleCounter__" %}: See below for an example using a counter.
+- {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/accumulators/Histogram.java "__Histogram__" %}: A histogram implementation for a discrete number of bins. Internally it is just a map from Integer to Integer. You can use this to compute distributions of values, e.g. the distribution of words-per-line for a word count program.
 
 __How to use accumulators:__
 
@@ -1411,15 +1411,15 @@ The overall result will be stored in the ```JobExecutionResult``` object which i
 
     myJobExecutionResult.getAccumulatorResult("num-lines")
 
-All accumulators share a single namespace per job. Thus you can use the same accumulator in different operator functions of your job. Stratosphere will internally merge all accumulators with the same name.
+All accumulators share a single namespace per job. Thus you can use the same accumulator in different operator functions of your job. Flink will internally merge all accumulators with the same name.
 
-A note on accumulators and iterations: Currently the result of accumulators is only available after the overall job ended. We plan to also make the result of the previous iteration available in the next iteration. You can use {% gh_link /stratosphere-java/src/main/java/eu/stratosphere/api/java/IterativeDataSet.java#L98 "Aggregators" %} to compute per-iteration statistics and base the termination of iterations on such statistics.
+A note on accumulators and iterations: Currently the result of accumulators is only available after the overall job ended. We plan to also make the result of the previous iteration available in the next iteration. You can use {% gh_link /flink-java/src/main/java/org/apache/flink/api/java/IterativeDataSet.java#L98 "Aggregators" %} to compute per-iteration statistics and base the termination of iterations on such statistics.
 
 __Custom accumulators:__
 
-To implement your own accumulator you simply have to write your implementation of the Accumulator interface. Feel free to create a pull request if you think your custom accumulator should be shipped with Stratosphere.
+To implement your own accumulator you simply have to write your implementation of the Accumulator interface. Feel free to create a pull request if you think your custom accumulator should be shipped with Flink.
 
-You have the choice to implement either {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/accumulators/Accumulator.java "Accumulator" %} or {% gh_link /stratosphere-core/src/main/java/eu/stratosphere/api/common/accumulators/SimpleAccumulator.java "SimpleAccumulator" %}. ```Accumulator<V,R>``` is most flexible: It defines a type ```V``` for the value to add, and a result type ```R``` for the final result. E.g. for a histogram, ```V``` is a number and ```R``` is a histogram. ```SimpleAccumulator``` is for the cases where both types are the same, e.g. for counters.
+You have the choice to implement either {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/accumulators/Accumulator.java "Accumulator" %} or {% gh_link /flink-core/src/main/java/org/apache/flink/api/common/accumulators/SimpleAccumulator.java "SimpleAccumulator" %}. ```Accumulator<V,R>``` is most flexible: It defines a type ```V``` for the value to add, and a result type ```R``` for the final result. E.g. for a histogram, ```V``` is a number and ```R``` is a histogram. ```SimpleAccumulator``` is for the cases where both types are the same, e.g. for counters.
 
 [Back to top](#top)
 
@@ -1428,11 +1428,11 @@ You have the choice to implement either {% gh_link /stratosphere-core/src/main/j
 Execution Plans
 ---------------
 
-Depending on various parameters such as data size or number of machines in the cluster, Stratosphere's optimizer automatically chooses an execution strategy for your program. In many cases, it can be useful to know how exactly Stratosphere will execute your program.
+Depending on various parameters such as data size or number of machines in the cluster, Flink's optimizer automatically chooses an execution strategy for your program. In many cases, it can be useful to know how exactly Flink will execute your program.
 
 __Plan Visualization Tool__
 
-Stratosphere 0.5 comes packaged with a visualization tool for execution plans. The HTML document containing the visualizer is located under ```tools/planVisualizer.html```. It takes a JSON representation of the job execution plan and visualizes it as a graph with complete annotations of execution strategies.
+Flink 0.5 comes packaged with a visualization tool for execution plans. The HTML document containing the visualizer is located under ```tools/planVisualizer.html```. It takes a JSON representation of the job execution plan and visualizes it as a graph with complete annotations of execution strategies.
 
 The following code shows how to print the execution plan JSON from your program:
 
@@ -1451,10 +1451,10 @@ To visualize the execution plan, do the following:
 
 After these steps, a detailed execution plan will be visualized.
 
-<img alt="A stratosphere job execution graph." src="http://stratosphere.eu/img/blog/plan_visualizer2.png" width="80%">
+<img alt="A flink job execution graph." src="{{site.baseurl}}/img/blog/plan_visualizer2.png" width="80%">
 __Web Interface__
 
-Stratosphere offers a web interface for submitting and executing jobs. If you choose to use this interface to submit your packaged program, you have the option to also see the plan visualization.
+Flink offers a web interface for submitting and executing jobs. If you choose to use this interface to submit your packaged program, you have the option to also see the plan visualization.
 
 The script to start the webinterface is located under ```bin/start-webclient.sh```. After starting the webclient (per default on **port 8080**), your program can be uploaded and will be added to the list of available programs on the left side of the interface.
 

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/java_api_quickstart.md
----------------------------------------------------------------------
diff --git a/docs/java_api_quickstart.md b/docs/java_api_quickstart.md
index c34cbec..dfcef7a 100644
--- a/docs/java_api_quickstart.md
+++ b/docs/java_api_quickstart.md
@@ -2,7 +2,7 @@
 title: "Quickstart: Java API"
 ---
 
-Start working on your Stratosphere Java program in a few simple steps.
+Start working on your Flink Java program in a few simple steps.
 
 
 # Requirements
@@ -18,13 +18,13 @@ Use one of the following commands to __create a project__:
 <div class="tab-content">
     <div class="tab-pane active" id="quickstart-script">
     {% highlight bash %}
-    $ curl https://raw.githubusercontent.com/apache/incubator-flink/master/stratosphere-quickstart/quickstart.sh | bash
+    $ curl https://raw.githubusercontent.com/apache/incubator-flink/master/flink-quickstart/quickstart.sh | bash
     {% endhighlight %}
     </div>
     <div class="tab-pane" id="maven-archetype">
     {% highlight bash %}
     $ mvn archetype:generate                             \
-      -DarchetypeGroupId=eu.stratosphere               \
+      -DarchetypeGroupId=org.apache.flink              \
       -DarchetypeArtifactId=quickstart-java            \
       -DarchetypeVersion={{site.FLINK_VERSION_STABLE}}
     {% endhighlight %}
@@ -35,15 +35,15 @@ Use one of the following commands to __create a project__:
 # Inspect Project
 There will be a new directory in your working directory. If you've used the _curl_ approach, the directory is called `quickstart`. Otherwise, it has the name of your artifactId.
 
-The sample project is a __Maven project__, which contains two classes. _Job_ is a basic skeleton program and _WordCountJob_ a working example. Please note that the _main_ method of both classes allow you to start Stratosphere in a development/testing mode.
+The sample project is a __Maven project__, which contains two classes. _Job_ is a basic skeleton program and _WordCountJob_ a working example. Please note that the _main_ method of both classes allow you to start Flink in a development/testing mode.
 
 We recommend to __import this project into your IDE__ to develop and test it. If you use Eclipse, the [m2e plugin](http://www.eclipse.org/m2e/) allows to [import Maven projects](http://books.sonatype.com/m2eclipse-book/reference/creating-sect-importing-projects.html#fig-creating-import). Some Eclipse bundles include that plugin by default, other require you to install it manually. The IntelliJ IDE also supports Maven projects out of the box.
 
 
-A note to Mac OS X users: The default JVM heapsize for Java is too small for Stratosphere. You have to manually increase it. Choose "Run Configurations" -> Arguments and write into the "VM Arguments" box: "-Xmx800m" in Eclipse.
+A note to Mac OS X users: The default JVM heapsize for Java is too small for Flink. You have to manually increase it. Choose "Run Configurations" -> Arguments and write into the "VM Arguments" box: "-Xmx800m" in Eclipse.
 
 # Build Project
-If you want to __build your project__, go to your project directory and issue the `mvn clean package` command. You will __find a jar__ that runs on every Stratosphere cluster in `target/stratosphere-project-0.1-SNAPSHOT.jar`.
+If you want to __build your project__, go to your project directory and issue the `mvn clean package` command. You will __find a jar__ that runs on every Flink cluster in `target/flink-project-0.1-SNAPSHOT.jar`.
 
 # Next Steps
 Write your application!
@@ -114,6 +114,6 @@ public class LineSplitter extends FlatMapFunction<String, Tuple2<String, Integer
   }
 }
 ```
-{% gh_link /stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/wordcount/WordCount.java "Check GitHub" %} for the full example code.
+{% gh_link /flink-examples/flink-java-examples/src/main/java/org/apache/flink/example/java/wordcount/WordCount.java "Check GitHub" %} for the full example code.
 
 For a complete overview over our Java API, have a look at the [API Documentation](java_api_guide.html) and [further example programs](java_api_examples.html). If you have any trouble, ask on our [Mailing List](http://mail-archives.apache.org/mod_mbox/incubator-flink-dev/). We are happy to provide help.

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/local_execution.md
----------------------------------------------------------------------
diff --git a/docs/local_execution.md b/docs/local_execution.md
index 11047b5..4a126bb 100644
--- a/docs/local_execution.md
+++ b/docs/local_execution.md
@@ -4,13 +4,13 @@ title:  "Local Execution"
 
 # Local Execution/Debugging
 
-Stratosphere can run on a single machine, even in a single Java Virtual Machine. This allows users to test and debug Stratosphere programs locally. This section gives an overview of the local execution mechanisms.
+Flink can run on a single machine, even in a single Java Virtual Machine. This allows users to test and debug Flink programs locally. This section gives an overview of the local execution mechanisms.
 
 **NOTE:** Please also refer to the [debugging section](java_api_guide.html#debugging) in the Java API documentation for a guide to testing and local debugging utilities in the Java API.
 
-The local environments and executors allow you to run Stratosphere programs in local Java Virtual Machine, or with within any JVM as part of existing programs. Most examples can be launched locally by simply hitting the "Run" button of your IDE.
+The local environments and executors allow you to run Flink programs in local Java Virtual Machine, or with within any JVM as part of existing programs. Most examples can be launched locally by simply hitting the "Run" button of your IDE.
 
-If you are running Stratosphere programs locally, you can also debug your program like any other Java program. You can either use `System.out.println()` to write out some internal variables or you can use the debugger. It is possible to set breakpoints within `map()`, `reduce()` and all the other methods.
+If you are running Flink programs locally, you can also debug your program like any other Java program. You can either use `System.out.println()` to write out some internal variables or you can use the debugger. It is possible to set breakpoints within `map()`, `reduce()` and all the other methods.
 
 The `JobExecutionResult` object, which is returned after the execution finished, contains the program runtime and the accumulator results.
 
@@ -19,7 +19,7 @@ The `JobExecutionResult` object, which is returned after the execution finished,
 
 # Maven Dependency
 
-If you are developing your program in a Maven project, you have to add the `stratosphere-clients` module using this dependency:
+If you are developing your program in a Maven project, you have to add the `flink-clients` module using this dependency:
 
 ```xml
 <dependency>
@@ -31,7 +31,7 @@ If you are developing your program in a Maven project, you have to add the `stra
 
 # Local Environment
 
-The `LocalEnvironment` is a handle to local execution for Stratosphere programs. Use it to run a program within a local JVM - standalone or embedded in other programs.
+The `LocalEnvironment` is a handle to local execution for Flink programs. Use it to run a program within a local JVM - standalone or embedded in other programs.
 
 The local environment is instantiated via the method `ExecutionEnvironment.createLocalEnvironment()`. By default, it will use as many local threads for execution as your machine has CPU cores (hardware contexts). You can alternatively specify the desired parallelism. The local environment can be configured to log to the console using `enableLogging()`/`disableLogging()`.
 
@@ -79,7 +79,7 @@ public static void main(String[] args) throws Exception {
 
 # LocalDistributedExecutor
 
-Stratosphere also offers a `LocalDistributedExecutor` which starts multiple TaskManagers within one JVM. The standard `LocalExecutor` starts one JobManager and one TaskManager in one JVM.
+Flink also offers a `LocalDistributedExecutor` which starts multiple TaskManagers within one JVM. The standard `LocalExecutor` starts one JobManager and one TaskManager in one JVM.
 With the `LocalDistributedExecutor` you can define the number of TaskManagers to start. This is useful for debugging network related code and more of a developer tool than a user tool.
 
 ```java

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/local_setup.md
----------------------------------------------------------------------
diff --git a/docs/local_setup.md b/docs/local_setup.md
index 8467a5b..dd8ffe5 100644
--- a/docs/local_setup.md
+++ b/docs/local_setup.md
@@ -2,15 +2,15 @@
 title:  "Local Setup"
 ---
 
-This documentation is intended to provide instructions on how to run Stratosphere locally on a single machine.
+This documentation is intended to provide instructions on how to run Flink locally on a single machine.
 
 # Download
 
-Go to the [downloads page]({{site.baseurl}}/downloads/) and get the ready to run package. If you want to interact with Hadoop (e.g. HDFS or HBase), make sure to pick the Stratosphere package **matching your Hadoop version**. When in doubt or you plan to just work with the local file system pick the package for Hadoop 1.2.x.
+Go to the [downloads page]({{site.baseurl}}/downloads/) and get the ready to run package. If you want to interact with Hadoop (e.g. HDFS or HBase), make sure to pick the Flink package **matching your Hadoop version**. When in doubt or you plan to just work with the local file system pick the package for Hadoop 1.2.x.
 
 # Requirements
 
-Stratosphere runs on **Linux**, **Mac OS X** and **Windows**. The only requirement for a local setup is **Java 1.6.x** or higher. The following manual assumes a *UNIX-like environment*, for Windows see [Stratosphere on Windows](#windows).
+Flink runs on **Linux**, **Mac OS X** and **Windows**. The only requirement for a local setup is **Java 1.6.x** or higher. The following manual assumes a *UNIX-like environment*, for Windows see [Flink on Windows](#windows).
 
 You can check the correct installation of Java by issuing the following command:
 
@@ -28,17 +28,17 @@ Java HotSpot(TM) 64-Bit Server VM (build 17.1-b03, mixed mode)
 
 # Configuration
 
-**For local mode Stratosphere is ready to go out of the box and you don't need to change the default configuration.**
+**For local mode Flink is ready to go out of the box and you don't need to change the default configuration.**
 
-The out of the box configuration will use your default Java installation. You can manually set the environment variable `JAVA_HOME` or the configuration key `env.java.home` in `conf/stratosphere-conf.yaml` if you want to manually override the Java runtime to use. Consult the [configuration page](config.html) for further details about configuring Stratosphere.
+The out of the box configuration will use your default Java installation. You can manually set the environment variable `JAVA_HOME` or the configuration key `env.java.home` in `conf/flink-conf.yaml` if you want to manually override the Java runtime to use. Consult the [configuration page](config.html) for further details about configuring Flink.
 
-# Starting Stratosphere
+# Starting Flink
 
-**You are now ready to start Stratosphere.** Unpack the downloaded archive and change to the newly created `stratosphere` directory. There you can start Stratosphere in local mode:
+**You are now ready to start Flink.** Unpack the downloaded archive and change to the newly created `flink` directory. There you can start Flink in local mode:
 
 ```bash
-$ tar xzf stratosphere-*.tgz
-$ cd stratosphere
+$ tar xzf flink-*.tgz
+$ cd flink
 $ bin/start-local.sh
 Starting job manager
 ```
@@ -46,9 +46,9 @@ Starting job manager
 You can check that the system is running by checking the log files in the `logs` directory:
 
 ```bash
-$ tail log/stratosphere-*-jobmanager-*.log
+$ tail log/flink-*-jobmanager-*.log
 INFO ... - Initializing memory manager with 409 megabytes of memory
-INFO ... - Trying to load eu.stratosphere.nephele.jobmanager.scheduler.local.LocalScheduler as scheduler
+INFO ... - Trying to load org.apache.flinknephele.jobmanager.scheduler.local.LocalScheduler as scheduler
 INFO ... - Setting up web info server, using web-root directory ...
 INFO ... - Web info server will display information about nephele job-manager on localhost, port 8081.
 INFO ... - Starting web info server for JobManager on port 8081
@@ -56,34 +56,34 @@ INFO ... - Starting web info server for JobManager on port 8081
 
 The JobManager will also start a web frontend on port 8081, which you can check with your browser at `http://localhost:8081`.
 
-# Stratosphere on Windows
+# Flink on Windows
 
-If you want to run Stratosphere on Windows you need to download, unpack and configure the Stratosphere archive as mentioned above. After that you can either use the **Windows Batch** file (`.bat`) or use **Cygwin**  to run the Stratosphere Jobmanager.
+If you want to run Flink on Windows you need to download, unpack and configure the Flink archive as mentioned above. After that you can either use the **Windows Batch** file (`.bat`) or use **Cygwin**  to run the Flink Jobmanager.
 
-To start Stratosphere in local mode from the *Windows Batch*, open the command window, navigate to the `bin/` directory of Stratosphere and run `start-local.bat`.
+To start Flink in local mode from the *Windows Batch*, open the command window, navigate to the `bin/` directory of Flink and run `start-local.bat`.
 
 ```bash
-$ cd stratosphere
+$ cd flink
 $ cd bin
 $ start-local.bat
-Starting Stratosphere job manager. Webinterface by default on http://localhost:8081/.
+Starting Flink job manager. Webinterface by default on http://localhost:8081/.
 Do not close this batch window. Stop job manager by pressing Ctrl+C.
 ```
 
-After that, you need to open a second terminal to run jobs using `stratosphere.bat`.
+After that, you need to open a second terminal to run jobs using `flink.bat`.
 
-With *Cygwin* you need to start the Cygwin Terminal, navigate to your Stratosphere directory and run the `start-local.sh` script:
+With *Cygwin* you need to start the Cygwin Terminal, navigate to your Flink directory and run the `start-local.sh` script:
 
 ```bash
-$ cd stratosphere
+$ cd flink
 $ bin/start-local.sh
 Starting Nephele job manager
 ```
 
-If you are installing Stratosphere from the git repository and you are using the Windows git shell, Cygwin can produce a failure similiar to this one:
+If you are installing Flink from the git repository and you are using the Windows git shell, Cygwin can produce a failure similiar to this one:
 
 ```bash
-c:/stratosphere/bin/start-local.sh: line 30: $'\r': command not found
+c:/flink/bin/start-local.sh: line 30: $'\r': command not found
 ```
 
 This error occurs, because git is automatically transforming UNIX line endings to Windows style line endings when running in Windows. The problem is, that Cygwin can only deal with UNIX style line endings. The solution is to adjust the Cygwin settings to deal with the correct line endings by following these three steps:

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/run_example_quickstart.md
----------------------------------------------------------------------
diff --git a/docs/run_example_quickstart.md b/docs/run_example_quickstart.md
index 13b5b4b..bc122c0 100644
--- a/docs/run_example_quickstart.md
+++ b/docs/run_example_quickstart.md
@@ -2,21 +2,21 @@
 title: "Quick Start: Run K-Means Example"
 ---
 
-This guide will Peter demonstrate Stratosphere's features by example. You will see how you can leverage Stratosphere's Iteration-feature to find clusters in a dataset using [K-Means clustering](http://en.wikipedia.org/wiki/K-means_clustering). 
+This guide will Peter demonstrate Flink's features by example. You will see how you can leverage Flink's Iteration-feature to find clusters in a dataset using [K-Means clustering](http://en.wikipedia.org/wiki/K-means_clustering). 
 On the way, you will see the compiler, the status interface and the result of the algorithm.
 
 
 #  Generate Input Data
-Stratosphere contains a data generator for K-Means.
+Flink contains a data generator for K-Means.
 
-	# Download Stratosphere
+	# Download Flink
 	wget {{ site.FLINK_VERSION_STABLE_dl }}
-	tar xzf stratosphere-*.tgz 
-	cd stratosphere-*
+	tar xzf flink-*.tgz 
+	cd flink-*
 	mkdir kmeans
 	cd kmeans
 	# Run data generator
-	java -cp  ../examples/stratosphere-java-examples-{{ site.FLINK_VERSION_STABLE }}-KMeans.jar eu.stratosphere.example.java.clustering.util.KMeansDataGenerator 500 10 0.08
+	java -cp  ../examples/flink-java-examples-{{ site.FLINK_VERSION_STABLE }}-KMeans.jar org.apache.flinkexample.java.clustering.util.KMeansDataGenerator 500 10 0.08
 	cp /tmp/points .
 	cp /tmp/centers .
 
@@ -47,17 +47,17 @@ The following overview presents the impact of the different standard deviations
 
 
 # Run Clustering
-We are using the generated input data to run the clustering using a Stratosphere job.
+We are using the generated input data to run the clustering using a Flink job.
 
-	# go to the Stratosphere-root directory
-	cd stratosphere
-	# start Stratosphere (use ./bin/start-cluster.sh if you're on a cluster)
+	# go to the Flink-root directory
+	cd flink
+	# start Flink (use ./bin/start-cluster.sh if you're on a cluster)
 	./bin/start-local.sh
-	# Start Stratosphere web client
+	# Start Flink web client
 	./bin/start-webclient.sh
 
-# Review Stratosphere Compiler
-The Stratosphere webclient allows to submit Stratosphere programs using a graphical user interface.
+# Review Flink Compiler
+The Flink webclient allows to submit Flink programs using a graphical user interface.
 
 <div class="row" style="padding-top:15px">
 	<div class="col-md-6">
@@ -67,7 +67,7 @@ The Stratosphere webclient allows to submit Stratosphere programs using a graphi
 		1. <a href="http://localhost:8080/launch.html">Open webclient on localhost:8080</a> <br>
 		2. Upload the file. 
 			{% highlight bash %}
-			examples/stratosphere-java-examples-0.5-SNAPSHOT-KMeansIterative.jar
+			examples/flink-java-examples-0.5-SNAPSHOT-KMeansIterative.jar
 			{% endhighlight %} </br>
 		3. Select it in the left box to see how the operators in the plan are connected to each other. <br>
 		4. Enter the arguments in the lower left box:
@@ -76,7 +76,7 @@ The Stratosphere webclient allows to submit Stratosphere programs using a graphi
 			{% endhighlight %}
 			For example:
 			{% highlight bash %}
-			file:///tmp/stratosphere/kmeans/points file:///tmp/stratosphere/kmeans/centers file:///tmp/stratosphere/kmeans/result 20
+			file:///tmp/flink/kmeans/points file:///tmp/flink/kmeans/centers file:///tmp/flink/kmeans/result 20
 			{% endhighlight %}
 	</div>
 </div>
@@ -98,7 +98,7 @@ The Stratosphere webclient allows to submit Stratosphere programs using a graphi
 	</div>
 	<div class="col-md-6">
 		1. Press the <b>Continue</b> button to start executing the job. <br>
-		2. <a href="http://localhost:8080/launch.html">Open Stratosphere's monitoring interface</a> to see the job's progress.<br>
+		2. <a href="http://localhost:8080/launch.html">Open Flink's monitoring interface</a> to see the job's progress.<br>
 		3. Once the job has finished, you can analyize the runtime of the individual operators.
 	</div>
 </div>

http://git-wip-us.apache.org/repos/asf/incubator-flink/blob/fbc93386/docs/scala_api_examples.md
----------------------------------------------------------------------
diff --git a/docs/scala_api_examples.md b/docs/scala_api_examples.md
index 2b6e795..fad1919 100644
--- a/docs/scala_api_examples.md
+++ b/docs/scala_api_examples.md
@@ -2,10 +2,10 @@
 title:  "Scala API Examples"
 ---
 
-The following example programs showcase different applications of Stratosphere from simple word counting to graph algorithms.
-The code samples illustrate the use of [Stratosphere's Scala API](scala_api_guide.html). 
+The following example programs showcase different applications of Flink from simple word counting to graph algorithms.
+The code samples illustrate the use of [Flink's Scala API](scala_api_guide.html). 
 
-The full source code of the following and more examples can be found in the [stratosphere-scala-examples](https://github.com/apache/incubator-flink/tree/ca2b287a7a78328ebf43766b9fdf39b56fb5fd4f/stratosphere-examples/stratosphere-scala-examples) module.
+The full source code of the following and more examples can be found in the [flink-scala-examples](https://github.com/apache/incubator-flink/tree/ca2b287a7a78328ebf43766b9fdf39b56fb5fd4f/flink-examples/flink-scala-examples) module.
 
 # Word Count
 
@@ -25,7 +25,7 @@ val counts = words.groupBy { case (word, _) => word }
 val output = counts.write(wordsOutput, CsvOutputFormat()))
 ```
 
-The {% gh_link /stratosphere-examples/stratosphere-scala-examples/src/main/scala/eu/stratosphere/examples/scala/wordcount/WordCount.scala "WordCount example" %} implements the above described algorithm with input parameters: `<degree of parallelism>, <text input path>, <output path>`. As test data, any text file will do.
+The {% gh_link /flink-examples/flink-scala-examples/src/main/scala/org/apache/flink/examples/scala/wordcount/WordCount.scala "WordCount example" %} implements the above described algorithm with input parameters: `<degree of parallelism>, <text input path>, <output path>`. As test data, any text file will do.
 
 # Page Rank
 
@@ -71,7 +71,7 @@ val output = finalRanks.write(outputPath, CsvOutputFormat())
 
 
 
-The {% gh_link /stratosphere-examples/stratosphere-scala-examples/src/main/scala/eu/stratosphere/examples/scala/graph/PageRank.scala "PageRank program" %} implements the above example.
+The {% gh_link /flink-examples/flink-scala-examples/src/main/scala/org/apache/flink/examples/scala/graph/PageRank.scala "PageRank program" %} implements the above example.
 It requires the following parameters to run: `<pages input path>, <link input path>, <output path>, <num pages>, <num iterations>`.
 
 Input files are plain text files and must be formatted as follows:
@@ -123,7 +123,7 @@ val components = initialComponents.iterateWithDelta(initialComponents, { _.verte
 val output = components.write(componentsOutput, CsvOutputFormat())
 ```
 
-The {% gh_link /stratosphere-examples/stratosphere-scala-examples/src/main/scala/eu/stratosphere/examples/scala/graph/ConnectedComponents.scala "ConnectedComponents program" %} implements the above example. It requires the following parameters to run: `<vertex input path>, <edge input path>, <output path> <max num iterations>`.
+The {% gh_link /flink-examples/flink-scala-examples/src/main/scala/org/apache/flink/examples/scala/graph/ConnectedComponents.scala "ConnectedComponents program" %} implements the above example. It requires the following parameters to run: `<vertex input path>, <edge input path>, <output path> <max num iterations>`.
 
 Input files are plain text files and must be formatted as follows:
 - Vertices represented as IDs and separated by new-line characters.
@@ -147,7 +147,7 @@ WHERE l_orderkey = o_orderkey
 GROUP BY l_orderkey, o_shippriority;
 ```
 
-The Stratosphere Scala program, which implements the above query looks as follows.
+The Flink Scala program, which implements the above query looks as follows.
 
 ```scala
 // --- define some custom classes to address fields by name ---
@@ -171,10 +171,10 @@ val prioritizedOrders = prioritizedItems
 val output = prioritizedOrders.write(ordersOutput, CsvOutputFormat(formatOutput))
 ```
 
-The {% gh_link /stratosphere-examples/stratosphere-scala-examples/src/main/scala/eu/stratosphere/examples/scala/relational/RelationalQuery.scala "Relational Query program" %} implements the above query. It requires the following parameters to run: `<orders input path>, <lineitem input path>, <output path>, <degree of parallelism>`.
+The {% gh_link /flink-examples/flink-scala-examples/src/main/scala/org/apache/flink/examples/scala/relational/RelationalQuery.scala "Relational Query program" %} implements the above query. It requires the following parameters to run: `<orders input path>, <lineitem input path>, <output path>, <degree of parallelism>`.
 
 The orders and lineitem files can be generated using the [TPC-H benchmark](http://www.tpc.org/tpch/) suite's data generator tool (DBGEN). 
-Take the following steps to generate arbitrary large input files for the provided Stratosphere programs:
+Take the following steps to generate arbitrary large input files for the provided Flink programs:
 
 1.  Download and unpack DBGEN
 2.  Make a copy of *makefile.suite* called *Makefile* and perform the following changes: