What are the best alternatives to Postgres for analytics?

When seeking alternatives to PostgreSQL for analytics, several robust database solutions offer enhanced performance and features for analytical workloads and data warehousing. The "best" alternative often depends on specific requirements such as data volume, real-time needs, scalability, and budget. Here are some top alternatives to PostgreSQL for analytics: **Cloud Data Warehouses (Managed Services):** These are often preferred for their scalability, performance, and reduced administrative overhead, and typically integrate well with BI tools like Metabase and Power BI. * **Amazon Redshift:** A fully-managed, petabyte-scale cloud data warehouse service optimized for analytical queries. Metabase officially supports Amazon Redshift. * **Google BigQuery:** A fully-managed, serverless data warehouse that allows for rapid analysis of large datasets. Metabase officially supports BigQuery. * **Snowflake:** A cloud data platform known for its unique architecture that separates storage and compute, offering excellent scalability and concurrency. Metabase officially supports Snowflake. * **Azure SQL Database:** A fully managed, intelligent relational database service built on the Microsoft SQL Server engine, offering scalability, high availability, and security features. It's designed to support modern cloud applications and integrates well within the Microsoft ecosystem, making it a strong contender for Power BI users. **Analytical Databases and Distributed SQL Databases:** These databases are built for high-performance analytical queries or offer horizontal scalability beyond what a single PostgreSQL instance can provide. * **ClickHouse:** An open-source, column-oriented database management system designed for online analytical processing (OLAP). It's known for its high performance on analytical queries and is officially supported by Metabase. * **TiDB:** Provides horizontal scalability and hybrid transactional-analytical processing (HTAP) while being MySQL protocol compatible. It separates compute from storage and is designed to address PostgreSQL's scaling limitations. * **SingleStore (formerly MemSQL):** Offers a hybrid rowstore and columnstore architecture for real-time analytics without ETL pipelines. It supports both SQL and NoSQL workloads and is built for high-throughput data ingestion and distributed architecture for horizontal scaling. * **Apache Cassandra:** A column-oriented NoSQL database designed for handling large and heterogeneous datasets, known for its high availability across multiple nodes and impressive query speeds. While primarily NoSQL, it can be used for analytical workloads, especially with tools that can query NoSQL sources. * **Druid:** A column-oriented, open-source, distributed data store suitable for real-time analytics. Metabase officially supports Druid. * **Vertica:** A distributed, shared-nothing, database management system specifically designed for analytics. Metabase officially supports Vertica. **Traditional Relational Databases with Strong Analytical Capabilities:** These databases, while not exclusively analytical, offer robust features and performance suitable for many analytical use cases, especially with proper indexing and query optimization. * **Microsoft SQL Server:** A popular relational database with advanced analytics capabilities, including in-database machine learning. It's a natural fit for Power BI due to its Microsoft origin and extensive integration. Metabase also supports Microsoft SQL Server. * **Oracle Database:** A powerful relational multi-model database management system known for its advanced analytics, high availability, and scalability features. Metabase officially supports Oracle Database. * **MySQL:** The world's most popular open-source database, recognized for reliability and performance in straightforward transactional and some analytical workloads. Metabase officially supports MySQL. * **MariaDB:** An open-source relational database management system, often seen as a modern alternative to MySQL, offering good performance and various scaling options. Metabase officially supports MariaDB. **NoSQL Databases (for specific analytical patterns):** While not traditional SQL alternatives, some NoSQL databases can be valuable for specific analytical needs, especially with unstructured or semi-structured data. * **MongoDB:** A source-available, cross-platform, document-oriented database that can handle large amounts of data. Metabase officially supports MongoDB. * **Redis:** An in-memory NoSQL database that delivers sub-millisecond response times, making it ideal for real-time analyses and low-latency scenarios. Metabase generally connects to a wide range of JDBC-compatible databases, including many of the alternatives listed above, either through native drivers or JDBC connections. Power BI also offers extensive connectivity to various data sources, including most major SQL databases and cloud data warehouses.

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The \"best\" alternative often depends on specific requirements such as data volume, real-time needs, scalability, and budget.\n\nHere are some top alternatives to PostgreSQL for analytics:\n\n**Cloud Data Warehouses (Managed Services):**\nThese are often preferred for their scalability, performance, and reduced administrative overhead, and typically integrate well with BI tools like Metabase and Power BI.\n\n* **Amazon Redshift:** A fully-managed, petabyte-scale cloud data warehouse service optimized for analytical queries. Metabase officially supports Amazon Redshift.\n* **Google BigQuery:** A fully-managed, serverless data warehouse that allows for rapid analysis of large datasets. Metabase officially supports BigQuery.\n* **Snowflake:** A cloud data platform known for its unique architecture that separates storage and compute, offering excellent scalability and concurrency. Metabase officially supports Snowflake.\n* **Azure SQL Database:** A fully managed, intelligent relational database service built on the Microsoft SQL Server engine, offering scalability, high availability, and security features. It\u0027s designed to support modern cloud applications and integrates well within the Microsoft ecosystem, making it a strong contender for Power BI users.\n\n**Analytical Databases and Distributed SQL Databases:**\nThese databases are built for high-performance analytical queries or offer horizontal scalability beyond what a single PostgreSQL instance can provide.\n\n* **ClickHouse:** An open-source, column-oriented database management system designed for online analytical processing (OLAP). It\u0027s known for its high performance on analytical queries and is officially supported by Metabase.\n* **TiDB:** Provides horizontal scalability and hybrid transactional-analytical processing (HTAP) while being MySQL protocol compatible. It separates compute from storage and is designed to address PostgreSQL\u0027s scaling limitations.\n* **SingleStore (formerly MemSQL):** Offers a hybrid rowstore and columnstore architecture for real-time analytics without ETL pipelines. It supports both SQL and NoSQL workloads and is built for high-throughput data ingestion and distributed architecture for horizontal scaling.\n* **Apache Cassandra:** A column-oriented NoSQL database designed for handling large and heterogeneous datasets, known for its high availability across multiple nodes and impressive query speeds. While primarily NoSQL, it can be used for analytical workloads, especially with tools that can query NoSQL sources.\n* **Druid:** A column-oriented, open-source, distributed data store suitable for real-time analytics. Metabase officially supports Druid.\n* **Vertica:** A distributed, shared-nothing, database management system specifically designed for analytics. Metabase officially supports Vertica.\n\n**Traditional Relational Databases with Strong Analytical Capabilities:**\nThese databases, while not exclusively analytical, offer robust features and performance suitable for many analytical use cases, especially with proper indexing and query optimization.\n\n* **Microsoft SQL Server:** A popular relational database with advanced analytics capabilities, including in-database machine learning. It\u0027s a natural fit for Power BI due to its Microsoft origin and extensive integration. Metabase also supports Microsoft SQL Server.\n* **Oracle Database:** A powerful relational multi-model database management system known for its advanced analytics, high availability, and scalability features. Metabase officially supports Oracle Database.\n* **MySQL:** The world\u0027s most popular open-source database, recognized for reliability and performance in straightforward transactional and some analytical workloads. Metabase officially supports MySQL.\n* **MariaDB:** An open-source relational database management system, often seen as a modern alternative to MySQL, offering good performance and various scaling options. Metabase officially supports MariaDB.\n\n**NoSQL Databases (for specific analytical patterns):**\nWhile not traditional SQL alternatives, some NoSQL databases can be valuable for specific analytical needs, especially with unstructured or semi-structured data.\n\n* **MongoDB:** A source-available, cross-platform, document-oriented database that can handle large amounts of data. Metabase officially supports MongoDB.\n* **Redis:** An in-memory NoSQL database that delivers sub-millisecond response times, making it ideal for real-time analyses and low-latency scenarios.\n\nMetabase generally connects to a wide range of JDBC-compatible databases, including many of the alternatives listed above, either through native drivers or JDBC connections. 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