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DSE 6.7 Developer Guide (EOSL)

Getting started
Information about developing applications for DataStax Enterprise.
Release notes
Release notes for DataStax Enterprise.
Installing
DataStax Enterprise 6.7 can be installed in a number of ways, depending on the purpose of the installation, the type of operating system, and the available permissions.
Configuration
Information about configuring DataStax Enterprise, such as recommended production setting, configuration files, snitch configuration, start-up parameters, heap dump settings, using virtual nodes, and more.
DSE advanced functionality
Information on using DSE Analytics, DSE Search, DSE Graph, DSEFS (DataStax Enterprise file system), and DSE Advanced Replication.
- DSE Analytics
  DataStax Enterprise 6.7 Analytics includes integration with Apache Spark.
  - About DSE Analytics
    Use DSE Analytics to analyze huge databases. DSE Analytics includes integration with Apache Spark.
  - Setting the replication factor for analytics keyspaces
    Guidelines and steps to set the replication factor for keyspaces on DSE Analytics nodes.
  - DSE Analytics and Search integration
    DSE SearchAnalytics clusters can use DSE Search queries within DSE Analytics jobs.
  - About DSE Analytics Solo
    DSE Analytics Solo datacenters provide analytics processing with Spark and distributed storage using DSEFS without storing transactional database data.
  - Analyzing data using Spark
    Spark is the default mode when you start an analytics node in a packaged installation. Spark runs locally on each node.
    - About Spark
      Information about Spark architecture and capabilities.
    - Using Spark with DataStax Enterprise
      DataStax Enterprise integrates with Apache Spark to allow distributed analytic applications to run using database data.
    - Configuring Spark
      Configuring Spark includes setting Spark properties for DataStax Enterprise and the database, enabling Spark apps, and setting permissions.
    - Using Spark modules with DataStax Enterprise
      Spark Streaming, Spark SQL, and MLlib are modules that extend the capabilities of Spark.
      - Getting started with Spark Streaming
        Spark Streaming allows you to consume live data streams from sources, including Akka, Kafka, and Twitter. This data can then be analyzed by Spark applications, and the data can be stored in the database. This example uses Scala.
      - Using Spark SQL to query data
        Spark SQL allows you to execute Spark queries using a variation of the SQL language.
      - Using SparkR with DataStax Enterprise
        Apache SparkR is a front-end for the R programming language for creating analytics applications. DataStax Enterprise integrates SparkR to support creating data frames from DSE data.
    - Using AlwaysOn SQL service
      AlwaysOn SQL is a high availability service that responds to SQL queries from JDBC and ODBC applications.
    - Accessing DataStax Enterprise data from external Spark clusters
      Information on accessing data in DataStax Enterprise clusters from external Spark clusters, or Bring Your Own Spark (BYOS).
    - Using the Spark Jobserver
      DSE includes Spark Jobserver, a REST interface for submitting and managing Spark jobs.
    - Spark examples
      DataStax Enterprise includes Spark example applications that demonstrate different Spark features.
    - DSE Spark Connector API documentation
  - DSEFS (DataStax Enterprise file system)
    DSEFS (DataStax Enterprise file system) is the default distributed file system on DSE Analytics nodes.
- DSE Search
  DSE Search allows you to quickly find data and provide a modern search experience for your users, helping you create features like product catalogs, document repositories, ad-hoc reporting engines, and more.
- DSE Graph
  Documentation for developers and administrators on installing, configuring, and using the features and capabilities of DSE Graph.
- NodeSync service
  Continuous background repair that virtually eliminates manual efforts to run repair operations in a DataStax cluster.
- DSE Advanced Replication
  Documentation for configuring and using configurable distributed data replication.
Developing applications with DataStax drivers
The DataStax drivers are the primary resource for application developers creating solutions using DataStax Enterprise (DSE).
Tools
Tools include nodetool, dse, and dsefs shell commands, dsetool, fs-stress tool, pre-flight check, and yaml_diff tools, and the sstableloader.
Operations
DataStax Enterprise operation topics, such as node and datacenter operations, changing replication strategies, configuring compaction and compression, caching, and tuning Bloom filters.
CQL
CQL (Cassandra Query Language) is a query language for the DataStax Enterprise database.

Using Spark modules with DataStax Enterprise

Spark Streaming, Spark SQL, and MLlib are modules that extend the capabilities of Spark.

Getting started with Spark Streaming
Spark Streaming allows you to consume live data streams from sources, including Akka, Kafka, and Twitter. This data can then be analyzed by Spark applications, and the data can be stored in the database. This example uses Scala.
Using Spark SQL to query data
Spark SQL allows you to execute Spark queries using a variation of the SQL language.
Using SparkR with DataStax Enterprise
Apache SparkR is a front-end for the R programming language for creating analytics applications. DataStax Enterprise integrates SparkR to support creating data frames from DSE data.

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Build time: 2023-03-29 08:44:25.407

DataStax, Titan, and TitanDB are registered trademarks of DataStax, Inc. and its subsidiaries in the United States and/or other countries.

Apache, Apache Cassandra, Cassandra, Apache Tomcat, Tomcat, Apache Lucene, Apache Solr, Apache Hadoop, Hadoop, Apache Pulsar, Pulsar, Apache Spark, Spark, Apache TinkerPop, TinkerPop, Apache Kafka and Kafka are either registered trademarks or trademarks of the Apache Software Foundation or its subsidiaries in Canada, the United States and/or other countries.

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