This guide explains how to deploy ScalarDB Analytics in a public cloud environment. ScalarDB Analytics currently uses Apache Spark as an execution engine and supports managed Spark services provided by public cloud providers, such as Amazon EMR and Databricks.
This tutorial describes how to run analytical queries on sample data by using ScalarDB Analytics. The source code is available at https://github.com/scalar-labs/scalardb-samples/tree/main/scalardb-analytics-spark-sample.
In this sub-category, you can learn how to set up and configure ScalarDB Analytics, an analytics component of ScalarDB. After setting it up, you can run analytical queries over ScalarDB-managed databases, which are updated through ScalarDB transactions, and non-ScalarDB-managed databases.
This guide explains how to develop ScalarDB Analytics applications. For details on the architecture and design, see ScalarDB Analytics Design
ScalarDB Analytics is the analytical component of ScalarDB. Similar to ScalarDB, it unifies diverse data sources - ranging from RDBMSs like PostgreSQL and MySQL to NoSQL databases such as Cassandra and DynamoDB - into a single logical database. While ScalarDB focuses on operational workloads with strong transactional consistency across multiple databases, ScalarDB Analytics is optimized for analytical workloads. It supports a wide range of queries, including complex joins, aggregations, and window functions. ScalarDB Analytics operates seamlessly on both ScalarDB-managed data sources and non-ScalarDB-managed ones, enabling advanced analytical queries across various datasets.
ScalarDB Analytics is the analytical component of ScalarDB. Similar to ScalarDB, it unifies diverse data sources—ranging from RDBMSs like PostgreSQL and MySQL to NoSQL databases like Cassandra and DynamoDB—into a single logical database. This enables you to perform analytical queries across multiple databases seamlessly.
In this sub-category, you can see tutorials on how to run analytical queries over the databases that you write through ScalarDB by using a component called ScalarDB Analytics.