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.
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.
Since Spark and Scala may be incompatible among different minor versions, ScalarDB Analytics with Spark offers different artifacts for various Spark and Scala versions, named in the format scalardb-analytics-spark-. Make sure that you select the artifact matching the Spark and Scala versions you're using. For example, if you're using Spark 3.5 with Scala 2.13, you must specify scalardb-analytics-spark-3.52.13.