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Posted to commits@druid.apache.org by fj...@apache.org on 2020/10/05 20:59:11 UTC
[druid-website-src] branch c created (now d26d5d5)
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at d26d5d5 wording changes
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[druid-website-src] 01/01: wording changes
Posted by fj...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
fjy pushed a commit to branch c
in repository https://gitbox.apache.org/repos/asf/druid-website-src.git
commit d26d5d5fa8abe20933d528c6eaef8f802b84b375
Author: fjy <fa...@gmail.com>
AuthorDate: Mon Oct 5 13:58:58 2020 -0700
wording changes
---
index.html | 18 +++++++++---------
technology.md | 2 +-
use-cases.md | 2 +-
3 files changed, 11 insertions(+), 11 deletions(-)
diff --git a/index.html b/index.html
index 03f16c9..d3934ba 100644
--- a/index.html
+++ b/index.html
@@ -12,7 +12,7 @@ canonical: 'https://druid.apache.org/'
<div class="container">
<div class="row">
<div class="text-center">
- <p class="lead">Apache Druid is a high performance real-time analytics database.</p>
+ <p class="lead">Apache Druid is a high performance analytics database.</p>
<p><a class="button" href="/downloads.html"><span class="fa fa-download"></span> Download</a>
<a class="button" href="https://github.com/apache/druid/"><span class="fab fa-github"></span> GitHub</a></p>
</div>
@@ -30,35 +30,35 @@ canonical: 'https://druid.apache.org/'
<div class="features">
<div class="feature">
<span class="fa fa-chart-line fa"></span>
- <h5>A modern cloud-native, stream-native, analytics database</h5>
+ <h5>Power data applications and products</h5>
<p>
- Druid is designed for workflows where fast queries and ingest really matter. Druid excels at instant data visibility, ad-hoc queries, operational analytics, and handling high concurrency. Consider Druid as an open source alternative to data warehouses for a variety of <a href='/use-cases'>use cases</a>.
+ Druid is designed for <a href='/use-cases'>workflows</a> where instant data visibility, fast ad-hoc queries, powerful analytics, and/or handling high query concurrency really matter. As such, Druid is often used to power UIs where interactive, consistent responses are desired for end users.
</p>
</div>
<div class="feature">
<span class="fa fa-forward fa"></span>
<h5>Easy integration with your existing data pipelines</h5>
<p>
- Druid can natively stream data from message buses such as <a href='http://kafka.apache.org/'>Kafka</a>, <a href='https://aws.amazon.com/kinesis/'>Amazon Kinesis</a>, and more, and batch load files from data lakes such as <a href='https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html'>HDFS</a>, <a href='https://aws.amazon.com/s3/'>Amazon S3</a>, and more.
+ Druid streams data from message buses such as <a href='http://kafka.apache.org/'>Kafka</a>, <a href='https://aws.amazon.com/kinesis/'>Amazon Kinesis</a>, and batch load files from data lakes such as <a href='https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html'>HDFS</a>, <a href='https://aws.amazon.com/s3/'>Amazon S3</a>. Druid supports most popular file formats for structured and semi-structured data.
</p>
</div>
<div class="feature">
<span class="fa fa-lightbulb fa"></span>
- <h5>Up to 100x faster than traditional solutions</h5>
+ <h5>Fast, consistent queries at high concurrency</h5>
<p>
- Druid has been <a href='https://imply.io/post/performance-benchmark-druid-presto-hive'>benchmarked</a> to greatly outperform legacy solutions for data ingestion and data querying. Druid's novel architecture combines the best of <a href='https://en.wikipedia.org/wiki/Data_warehouse'>data warehouses</a>, <a href='https://en.wikipedia.org/wiki/Time_series_database'>timeseries databases</a>, and <a href='https://en.wikipedia.org/wiki/Search_engine_(computing)'>search systems</a>.
+ Druid has been <a href='https://imply.io/post/performance-benchmark-druid-presto-hive'>benchmarked</a> to greatly outperform legacy solutions for data ingestion and data querying. Druid combines novel storage ideas, indexing structures, and both exact and approximate queries so that most results can return in under a second.
</p>
</div>
<div class="feature">
<span class="fa fa-unlock fa"></span>
- <h5>Unlock new workflows</h5>
+ <h5>Broad applicability</h5>
<p>
- Druid <a href='/use-cases'>unlocks new types of queries and workflows</a> for clickstream, APM, supply chain, network telemetry, digital marketing, and many other forms of event-driven data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data.
+ Druid <a href='/use-cases'>unlocks new types of queries and workflows</a> for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data.
</p>
</div>
<div class="feature">
<span class="fa fa-cloud fa"></span>
- <h5>Deploy in AWS/GCP/Azure, hybrid clouds, Kubernetes, and bare metal</h5>
+ <h5>Deploy in public, private, and hybrid clouds</h5>
<p>
Druid can be deployed in any *NIX environment on commodity hardware, both in the cloud and on premise. Deploying Druid is easy: scaling up and down is as simple as adding and removing Druid services.
</p>
diff --git a/technology.md b/technology.md
index e516692..30f8d20 100644
--- a/technology.md
+++ b/technology.md
@@ -6,7 +6,7 @@ canonical: 'https://druid.apache.org/technology'
---
Apache Druid is an open source distributed data store.
-Druid’s core design combines ideas from [data warehouses](https://en.wikipedia.org/wiki/Data_warehouse), [timeseries databases](https://en.wikipedia.org/wiki/Time_series_database), and [search systems](https://en.wikipedia.org/wiki/Full-text_search) to create a unified system for real-time analytics for a broad range of [use cases](/use-cases). Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture.
+Druid’s core design combines ideas from [data warehouses](https://en.wikipedia.org/wiki/Data_warehouse), [timeseries databases](https://en.wikipedia.org/wiki/Time_series_database), and [search systems](https://en.wikipedia.org/wiki/Full-text_search) to create a powerful backend for a broad range of [use cases](/use-cases). Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture.
<div class="image-large">
<img src="img/diagram-2.png" style="max-width: 360px">
diff --git a/use-cases.md b/use-cases.md
index edf5a89..025972e 100644
--- a/use-cases.md
+++ b/use-cases.md
@@ -5,7 +5,7 @@ sectionid: use-cases
canonical: 'https://druid.apache.org/use-cases'
---
-## Real-time analytics and intelligence
+## Power data applications
Apache Druid is a database that is most often used for powering use cases where real-time ingest, fast query performance, and high uptime are important. As such, Druid is commonly used for powering GUIs of analytical applications, or as a backend for highly-concurrent APIs that need fast aggregations. Druid works best with event-oriented data.
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