(flink-web) branch asf-site updated: Add missing word (#705)

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dannycranmer pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new eff4311b2 Add missing word (#705)
eff4311b2 is described below

commit eff4311b2702f64694c68fdfd7af605b058ca7b9
Author: Tom Bentley <to...@users.noreply.github.com>
AuthorDate: Mon Apr 15 14:02:11 2024 +0100

    Add missing word (#705)
    
    Various improvements and typo fixes
---
 docs/content/what-is-flink/flink-architecture.md | 2 +-
 docs/content/what-is-flink/use-cases.md          | 8 ++++----
 docs/i18n/en.yaml                                | 2 +-
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/content/what-is-flink/flink-architecture.md b/docs/content/what-is-flink/flink-architecture.md
index 6f5f42cf4..f2a4676ce 100644
--- a/docs/content/what-is-flink/flink-architecture.md
+++ b/docs/content/what-is-flink/flink-architecture.md
@@ -30,7 +30,7 @@ Convince yourself by exploring the [use cases]({{< relref "use-cases" >}}) that
 
 Apache Flink is a distributed system and requires compute resources in order to execute applications. Flink integrates with all common cluster resource managers such as [Hadoop YARN](https://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YARN.html) and [Kubernetes](https://kubernetes.io/) but can also be setup to run as a stand-alone cluster.
 
-Flink is designed to work well each of the previously listed resource managers. This is achieved by resource-manager-specific deployment modes that allow Flink to interact with each resource manager in its idiomatic way.
+Flink is designed to work well with each of the previously listed resource managers. This is achieved by resource-manager-specific deployment modes that allow Flink to interact with each resource manager in its idiomatic way.
 
 When deploying a Flink application, Flink automatically identifies the required resources based on the application's configured parallelism and requests them from the resource manager. In case of a failure, Flink replaces the failed container by requesting new resources. All communication to submit or control an application happens via REST calls. This eases the integration of Flink in many environments.
 
diff --git a/docs/content/what-is-flink/use-cases.md b/docs/content/what-is-flink/use-cases.md
index 42d09da97..5bc7233f3 100644
--- a/docs/content/what-is-flink/use-cases.md
+++ b/docs/content/what-is-flink/use-cases.md
@@ -53,13 +53,13 @@ In contrast, event-driven applications are based on stateful stream processing a
 
 ### What are the advantages of event-driven applications?
 
-Instead of querying a remote database, event-driven applications access their data locally which yields better performance, both in terms of throughput and latency. The periodic checkpoints to a remote persistent storage can be asynchronously and incrementally done. Hence, the impact of checkpointing on the regular event processing is very small. However, the event-driven application design provides more benefits than just local data access. In the tiered architecture, it is common that  [...]
+Instead of querying a remote database, event-driven applications access their data locally which yields better performance, both in terms of throughput and latency. The periodic checkpoints to a remote persistent storage can be asynchronous and incremental. Hence, the impact of checkpointing on the regular event processing is very small. However, the event-driven application design provides more benefits than just local data access. In the tiered architecture, it is common that multiple  [...]
 
 ### How does Flink support event-driven applications?
 
 The limits of event-driven applications are defined by how well a stream processor can handle time and state. Many of Flink's outstanding features are centered around these concepts. Flink provides a rich set of state primitives that can manage very large data volumes (up to several terabytes) with exactly-once consistency guarantees. Moreover, Flink's support for event-time, highly customizable window logic, and fine-grained control of time as provided by the `ProcessFunction` enable th [...]
 
-However, Flink's outstanding feature for event-driven applications is savepoint. A savepoint is a consistent state image that can be used as a starting point for compatible applications. Given a savepoint, an application can be updated or adapt its scale, or multiple versions of an application can be started for A/B testing.
+However, Flink's outstanding feature for event-driven applications is its support for savepoints. A savepoint is a consistent state image that can be used as a starting point for compatible applications. Given a savepoint, an application can be updated or adapt its scale, or multiple versions of an application can be started for A/B testing.
 
 ### What are typical event-driven applications?
 
@@ -75,7 +75,7 @@ However, Flink's outstanding feature for event-driven applications is savepoint.
 
 Analytical jobs extract information and insight from raw data. Traditionally, analytics are performed as batch queries or applications on bounded data sets of recorded events. In order to incorporate the latest data into the result of the analysis, it has to be added to the analyzed data set and the query or application is rerun. The results are written to a storage system or emitted as reports.
 
-With a sophisticated stream processing engine, analytics can also be performed in a real-time fashion. Instead of reading finite data sets, streaming queries or applications ingest real-time event streams and continuously produce and update results as events are consumed. The results are either written to an external database or maintained as internal state. Dashboard application can read the latest results from the external database or directly query the internal state of the application.
+With a sophisticated stream processing engine, analytics can also be performed in a real-time fashion. Instead of reading finite data sets, streaming queries or applications ingest real-time event streams and continuously produce and update results as events are consumed. The results are either written to an external database or maintained as internal state. A dashboard application can read the latest results from the external database or directly query the internal state of the application.
 
 Apache Flink supports streaming as well as batch analytical applications as shown in the figure below.
 
@@ -87,7 +87,7 @@ Apache Flink supports streaming as well as batch analytical applications as show
 
 The advantages of continuous streaming analytics compared to batch analytics are not limited to a much lower latency from events to insight due to elimination of periodic import and query execution. In contrast to batch queries, streaming queries do not have to deal with artificial boundaries in the input data which are caused by periodic imports and the bounded nature of the input.
 
-Another aspect is a simpler application architecture. A batch analytics pipeline consist of several independent components to periodically schedule data ingestion and query execution. Reliably operating such a pipeline is non-trivial because failures of one component affect the following steps of the pipeline. In contrast, a streaming analytics application which runs on a sophisticated stream processor like Flink incorporates all steps from data ingestions to continuous result computatio [...]
+Another aspect is a simpler application architecture. A batch analytics pipeline consists of several independent components to periodically schedule data ingestion and query execution. Reliably operating such a pipeline is non-trivial because failures of one component affect the following steps of the pipeline. In contrast, a streaming analytics application which runs on a sophisticated stream processor like Flink incorporates all steps from data ingestions to continuous result computati [...]
 
 ### How does Flink support data analytics applications?
 
diff --git a/docs/i18n/en.yaml b/docs/i18n/en.yaml
index 876a53b21..2e0a2a2fb 100644
--- a/docs/i18n/en.yaml
+++ b/docs/i18n/en.yaml
@@ -97,7 +97,7 @@
   translation: Event Driven Applications
 
 - id: use-cases.event-driven.description
-  translation: An event-driven application is a stateful application that ingest events from one or more event streams and reacts to incoming events by triggering computations, state updates, or external actions.
+  translation: An event-driven application is a stateful application that ingests events from one or more event streams and reacts to incoming events by triggering computations, state updates, or external actions.
 
 - id: use-cases.stream-batch
   translation: Stream & Batch Analytics