13 docs tagged with "Enterprise Option"

This document explains how to create your custom values file for the ScalarDB Analytics server chart. For details on the parameters, see the README of the ScalarDB Analytics server chart.

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.

To run ScalarDB Enterprise Standard/Premium or ScalarDB Analytics, you must create a .properties file and add your commercial license key and a certificate to the file. In your .properties file, copy one of the following configurations, based on the product you're using, and paste the contents in the .properties file, replacing `` with your license key.

You can use the following trial license keys for ScalarDB Enterprise Standard/Premium and ScalarDB Analytics. If you have a commercial license key, please refer to How to Configure a Commercial License Key to configure your license key.

ScalarDB provides various libraries and tools to help you build and operate scalable and reliable applications. Below are some key libraries and tools available.

To run ScalarDB Enterprise Standard/Premium or ScalarDB Analytics, you need to configure a license key.

Monitoring is essential for maintaining the health and performance of your ScalarDB deployment. This section provides guidance on monitoring ScalarDB in Kubernetes cluster environments, including checking system availability, collecting time-series metrics, and viewing logs through monitoring dashboards.

In this 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 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.