remove unnecessary question titles

aiven · Jan 18, 2024 · d73c3c0 · d73c3c0
1 parent eb22fec
commit d73c3c0
Show file tree

Hide file tree

Showing 12 changed files with 29 additions and 88 deletions.
diff --git a/docs/products/dragonfly/concepts/overview.rst b/docs/products/dragonfly/concepts/overview.rst
@@ -1,14 +1,11 @@
 Aiven for Dragonfly® overview
 ================================
 
-Aiven for Dragonfly is an advanced, **high-scale, and Redis®* compatible in-memory database service** that can be easily deployed in your preferred cloud environment. It provides lightning-fast data storage and retrieval capabilities, making it ideal for businesses that handle large-scale data operations.
+Aiven for Dragonfly is an advanced, **high-scale, and Redis®* compatible in-memory database service** that can be deployed in your preferred cloud environment. It provides lightning-fast data storage and retrieval capabilities, making it ideal for businesses that handle large-scale data operations.
 
 .. important:: 
     Aiven for Dragonfly® is currently a :doc:`limited availability </docs/platform/concepts/beta_services>` service. If you are interested in exploring this offering, reach out to our sales team at [email protected] for more information and access.
 
-Why Dragonfly?
---------------
-
 Dragonfly is designed to overcome the limitations of Redis Open Source Software (Redis OSS), especially under high-load conditions. While renowned for its speed and adaptability as an in-memory data repository, Redis encounters limitations when handling large-scale data management and achieving high throughput. Dragonfly addresses these challenges by enabling vertical scaling, optimizing hardware resources, and supporting larger memory footprints.
 
 With Aiven for Dragonfly, businesses can handle workloads exceeding 1TB with more than 10 times the throughput performance of Redis OSS, along with reduced latency. This makes it an ideal solution for enterprises with growing data needs.

diff --git a/docs/products/influxdb.rst b/docs/products/influxdb.rst
@@ -1,16 +1,9 @@
 Aiven for InfluxDB®
 ===================
 
-What is Aiven for InfluxDB®?
-----------------------------
-
 Aiven for InfluxDB® is a fully managed high-performance time series database designed for highly variable data, deployable in the cloud of your choice. It can ingest and query hundreds of thousands of data points a second.
 
-
-Why InfluxDB®?
---------------
-
-InfluxDB is a great time series data solution. It is known for its ability to handle variable data sets and query past data. And with Aiven, you can deploy InfluxDB in minutes and enhance your existing architecture.
+With Aiven, you can deploy InfluxDB in minutes and enhance your existing architecture.
 
 Get started with Aiven for InfluxDB®
 -------------------------------------

diff --git a/docs/products/kafka.rst b/docs/products/kafka.rst
@@ -1,19 +1,10 @@
 Aiven for Apache Kafka®
 =======================
 
-What is Aiven for Apache Kafka®?
---------------------------------
-
 Aiven for Apache Kafka® is a fully managed **distributed data streaming platform**, deployable in the cloud of your choice. Apache Kafka is an open source data streaming platform, ideal for event-driven applications, near-real-time data transfer and pipelines, stream analytics, and many more applications where a lot of data needs to move between applications in a speedy manner.
 
 Kafka stores a potentially large number of records, each contains a small amount of data, usually for a limited period of time. The storage is organised into "topics" and "partitions" so that many data streams can be handled at once, regardless of how much data is flowing into or out of your Aiven for Apache Kafka service.
 
-
-Why Apache Kafka?
------------------
-
-Apache Kafka itself is technically a distributed log storage mechanism; in reality it is a best-in-class, highly-available data streaming solution. Oh, and it just happens to have an incredibly rich ecosystem of open source tooling that connects to and extends the existing platform.
-
 Aiven for Apache Kafka® MirrorMaker 2
 '''''''''''''''''''''''''''''''''''''
 

diff --git a/docs/products/kafka/concepts/consumer-lag-predictor.rst b/docs/products/kafka/concepts/consumer-lag-predictor.rst
@@ -4,9 +4,10 @@ Consumer lag predictor for Aiven for Apache Kafka®
 The **consumer lag predictor** for Aiven for Apache Kafka estimates the delay between the time a message is produced and when it's eventually consumed by a consumer group. This information can be used to improve the performance, scalability, and cost-effectiveness of your Kafka cluster.
 
 .. important::
-    Consumer Lag Predictor for Aiven for Apache Kafka® is a limited availability feature. If you're interested in trying out this feature, contact the sales team at [email protected].
 
-To use the **consumer lag predictor** effectively, setting up :doc:`Prometheus integration </docs/platform/howto/integrations/prometheus-metrics>` with your Aiven for Apache Kafka® service is essential. Prometheus integration enables the extraction of key metrics necessary for lag prediction and monitoring. 
+   Consumer Lag Predictor for Aiven for Apache Kafka® is a limited availability feature. If you're interested in trying out this feature, contact the sales team at [email protected].
+
+To use the **consumer lag predictor** effectively, set up :doc:`Prometheus integration </docs/platform/howto/integrations/prometheus-metrics>` with your Aiven for Apache Kafka® service. The Prometheus integration enables the extraction of key metrics necessary for lag prediction and monitoring. 
 
 Why use consumer lag predictor?
 ---------------------------------

diff --git a/docs/products/kafka/kafka-connect.rst b/docs/products/kafka/kafka-connect.rst
@@ -1,19 +1,10 @@
 Aiven for Apache Kafka® Connect
 ===============================
 
-What is Aiven for Apache Kafka® Connect?
-----------------------------------------
-
 Aiven for Apache Kafka® Connect is a fully managed **distributed Apache Kafka® integration component**, deployable in the cloud of your choice. Apache Kafka Connect lets you integrate your existing data sources and sinks with Apache Kafka.
 
 With an Apache Kafka Connect connector, you can source data from an existing technology into a topic or sink data from a topic to a target technology by defining the endpoints.
 
-
-Why Apache Kafka® Connect?
---------------------------
-
-Apache Kafka represents the best in class data streaming solution. Apache Kafka Connect allows integrating Apache Kafka with the rest of your data architecture with a configuration file which defines the source and the target of your data.
-
 Source connectors
 -----------------
 

diff --git a/docs/products/kafka/karapace.rst b/docs/products/kafka/karapace.rst
@@ -1,11 +1,7 @@
 Karapace
 ========
-What is Karapace?
------------------
-`Karapace <https://karapace.io/>`_ is an Aiven built open-source Schema Registry for Apache Kafka®, and provides a central repository to store and retrieve schemas. It consists of a **Schema Registry** and a **REST API**. All Kafka services on Aiven support both these features (Schema registry and REST API), and as a user, based on your requirements, you can enable or disable them. 
 
-Why Karapace?
--------------
+`Karapace <https://karapace.io/>`_ is an Aiven built open-source Schema Registry for Apache Kafka®, and provides a central repository to store and retrieve schemas. It consists of a **Schema Registry** and a **REST API**. All Kafka services on Aiven support both these features (Schema registry and REST API), and as a user, based on your requirements, you can enable or disable them. 
 
 Karapace supports storing schemas in a central repository, which clients can access to serialize and deserialize messages. The schemas also maintain their own version histories and can be checked for compatibility between their different respective versions. It also includes support for JSON Schema, Avro and Protobuf data formats. 
 

diff --git a/docs/products/m3db.rst b/docs/products/m3db.rst
@@ -1,18 +1,11 @@
 Aiven for M3
 ============
 
-What is Aiven for M3?
----------------------
-
 Aiven for M3 is a fully managed **distributed time series database**, deployable in the cloud of your choice which can bring unlimited scalability and high-availability to your monitoring environment and other time series applications.
 
 Aiven for M3 consists of  ``n`` number of **M3DB** and **M3 Coordinator** pairs (where  ``n`` is the number of nodes as chosen for your Aiven plan). 
 
-
-Why M3?
--------
-
-M3 is a specialized time series store which is a great choice if your organization has a very large volume of metrics to handle, and it can be used as part of your observability solution. It is optimized for storing and serving time series through associated pairs of times and values. It also provides a reverse index of time series. 
+M3 is a great choice if your organization has a very large volume of metrics to handle, and it can be used as part of your observability solution. It is optimized for storing and serving time series through associated pairs of times and values. It also provides a reverse index of time series. 
 
 .. note::
    Aiven offers M3 because we ourselves needed a solution that would work with the size of our own metrics - and we love it!

diff --git a/docs/products/mysql/overview.rst b/docs/products/mysql/overview.rst
@@ -1,15 +1,9 @@
 Aiven for MySQL® overview
 =========================
 
-What is Aiven for MySQL?
-------------------------
-
 Aiven for MySQL® is a full managed relational database service, deployable in the cloud of your choice. It is available at a size to suit your needs, from single-node starter plans to highly-available production platforms. MySQL has been a key part of the open source database landscape for a long time and it's a popular and reliable database platform. Aiven takes care of the management side and provides a MySQL that you can use.
 
-Why MySQL?
-----------
-
-MySQL is a traditional open source relational database that plays nicely with many well-established applications. Whether you are building something new or looking for a modern platform for your existing applications, Aiven for MySQL is a friendly and scalable offering of one of the best open source databases around.
+MySQL is a traditional open source relational database that interacts with many well-established applications. Whether you are building something new or looking for a modern platform for your existing applications, Aiven for MySQL is a friendly and scalable offering of one of the best open source databases around.
 
 Get started with Aiven for MySQL
 --------------------------------

diff --git a/docs/products/opensearch/concepts/service-overview.rst b/docs/products/opensearch/concepts/service-overview.rst
@@ -3,17 +3,13 @@ Aiven for OpenSearch® overview
 
 Aiven for OpenSearch® is a fully managed distributed search and analytics suite, deployable in the cloud of your choice. Ideal for logs management, application and website search, analytical aggregations and more. OpenSearch is an open source fork derived from Elasticsearch.
 
-
 `OpenSearch® <https://opensearch.org>`_ is an open-source search and analytics suite including a search engine, NoSQL document database, and visualization interface. OpenSearch offers a distributed, full-text search engine based on `Apache Lucene® <https://lucene.apache.org/>`_ with a RESTful API interface and support for JSON documents. Aiven for OpenSearch and Aiven for OpenSearch Dashboards are available on a cloud of your choice.
 
 .. note::
     OpenSearch and OpenSearch Dashboards projects were forked in 2021 from the formerly open source projects Elasticsearch and Kibana.
 
 Aiven for OpenSearch includes OpenSearch Dashboards, giving a fully-featured user interface and visualization platform for your data.
 
-Why OpenSearch?
----------------
-
 OpenSearch is designed to be robust and scalable, capable of handling various data types and structures. It provides high-performance search functionality for data of any size or type, and with schemaless storage, it can index various sources with different data structures.
 
 OpenSearch is widely used for log ingestion and analysis, mainly because it can handle large data volumes, and OpenSearch Dashboards provide a powerful interface to the data, including search, aggregation, and analysis functionality.

diff --git a/docs/products/opensearch/howto/opensearch-and-nodejs.rst b/docs/products/opensearch/howto/opensearch-and-nodejs.rst
@@ -1,7 +1,7 @@
 Write search queries with OpenSearch® and NodeJS
 ================================================
 
-Learn how the OpenSearch® JavaScript client gives a clear and useful interface to communicate with an OpenSearch cluster and run search queries. To make it more delicious we'll be using a recipe dataset from Kaggle 🍕.
+Learn how the OpenSearch® JavaScript client gives a clear and useful interface to communicate with an OpenSearch cluster and run search queries. To make it more delicious we'll be using a recipe dataset from Kaggle.
 
 Prepare the playground
 **********************
@@ -395,21 +395,19 @@ In the next method we combine what we learned so far, using both term-level and
 
 Now it's your turn to experiment! Create your own boolean query, using what we learned to find recipes with particular nutritional values and ingredients. Experiment using different clauses to see how they affects the results.
 
-What's next?
-************
-
-Now that you learned how to work with search queries, have a look at :doc:`our tutorial for aggregations <opensearch-aggregations-and-nodejs>`. Or, if you're done for a day, see :doc:`how you can pause the service </docs/platform/howto/pause-from-cli>`.
-
-Resources
-*********
-
-We created an OpenSearch cluster, connected to it and tried out different types of search queries. But this is just a tip of the iceberg. Here are some resources to help you learn other features of OpenSearch and its JavaScript client
-
-* `Demo repository <https://github.com/aiven/demo-open-search-node-js>`_ - All the examples we run in this tutorial can be found in
-* `OpenSearch JavaScript client  <https://github.com/opensearch-project/opensearch-js>`_
-* :doc:`How to use OpenSearch with curl <opensearch-with-curl>`
-* `Official OpenSearch documentation <https://opensearch.org>`_
-    *  `Term-level queries <https://opensearch.org/docs/latest/opensearch/query-dsl/term/>`_
-    *  `Full-text queries <https://opensearch.org/docs/latest/opensearch/query-dsl/full-text/>`_
-    *  `Boolean queries <https://opensearch.org/docs/latest/opensearch/query-dsl/bool/>`_
+Related pages
+*************
+
+- :doc:`Aggregation tutorial <opensearch-aggregations-and-nodejs>`. 
+- :doc:`Pausing the service </docs/platform/howto/pause-from-cli>`.
+- `Demo repository <https://github.com/aiven/demo-open-search-node-js>`_.
+  All the examples we run in this tutorial can be found in:
+
+  - `OpenSearch JavaScript client  <https://github.com/opensearch-project/opensearch-js>`_
+  - :doc:`How to use OpenSearch with curl <opensearch-with-curl>`
+  - `Official OpenSearch documentation <https://opensearch.org>`_
+
+    -  `Term-level queries <https://opensearch.org/docs/latest/opensearch/query-dsl/term/>`_
+    -  `Full-text queries <https://opensearch.org/docs/latest/opensearch/query-dsl/full-text/>`_
+    -  `Boolean queries <https://opensearch.org/docs/latest/opensearch/query-dsl/bool/>`_
 
diff --git a/docs/products/postgresql/concepts/pgvector.rst b/docs/products/postgresql/concepts/pgvector.rst
@@ -53,12 +53,8 @@ There are multiple industry applications for similarity searches over vector emb
    * AI-powered tools can find similarities between products or transactions, which can be used to produce product recommendations or detect potential scams or frauds.
    * Sentiment analysis: words represented with similar vector embeddings have similar sentiment scores.
 
-What's next
------------
-
-:doc:`Enable and use pgvector on Aiven for PostgreSQL® </docs/products/postgresql/howto/use-pgvector>`
-
 Related pages
----------------
+-------------
 
-`pgvector README on GitHub <https://github.com/pgvector/pgvector/blob/master/README.md>`_
+- :doc:`Enable and use pgvector on Aiven for PostgreSQL® </docs/products/postgresql/howto/use-pgvector>`
+- `pgvector README on GitHub <https://github.com/pgvector/pgvector/blob/master/README.md>`_
diff --git a/docs/products/redis/concepts/overview.rst b/docs/products/redis/concepts/overview.rst
@@ -3,9 +3,6 @@ Aiven for Redis®* overview
 
 Aiven for Redis®* is a fully managed **in-memory NoSQL database**, deployable in the cloud of your choice which can help you store and access data quickly and efficiently.
 
-Why Redis®?
------------
-
 Redis® is an open source, in-memory NoSQL database that serves as a fast data store, cache, or lightweight message broker. 
 
 With Aiven, you can leverage the power of Redis to improve the performance of your applications by easily setting up high-performance data caching. Additionally, Redis can be integrated seamlessly into your observability stack for purposes such as logging and monitoring.
@@ -31,13 +28,11 @@ Ways to use Aiven for Redis
 -----------------------------
 
 - Redis can be used as a supplementary data store, complementing a primary database like PostgreSQL®.
-
 - Redis is suitable for transient data, caching values for quick access, and data that can be reestablished, such as session data. While Redis is not a persistent storage solution by default, it can be configured to be persistent.
 
 
-Redis resources
-----------------
+Related pages
+-------------
 
 * `Redis documentation <https://redis.io/documentation>`_
-
 * `Redis refcard on DZone <https://dzone.com/refcardz/getting-started-with-redis>`_