What are the best alternatives to Postgres for analytics?

When seeking alternatives to PostgreSQL for analytics within Metabase, several database options offer enhanced performance, scalability, or specialized features for analytical workloads. Metabase supports a wide range of databases, making it adaptable to various analytical needs. Here are some of the best alternatives to PostgreSQL for analytics, categorized by type: **Cloud Data Warehouses (for large-scale analytics):** These are often fully managed, scalable, and optimized for analytical queries on massive datasets. * **Amazon Redshift:** A fully-managed, petabyte-scale data warehouse service. * **Google BigQuery:** A fully-managed, serverless data warehouse that's highly scalable for large datasets. * **Snowflake:** A cloud-native data warehouse known for its flexibility and ability to handle diverse data workloads. * **Databricks:** A unified analytics platform built on Apache Spark, suitable for large-scale data processing and analytics. * **Vertica:** An analytical database designed for high performance on large datasets. **Specialized Analytical Databases:** These databases are built from the ground up to excel in analytical processing. * **ClickHouse:** An open-source, column-oriented database management system optimized for online analytical processing (OLAP) queries. * **Druid:** A column-oriented, open-source, distributed data store ideal for real-time analytics on large datasets. **Other Relational Databases (can also handle analytical queries, especially with proper indexing and optimization):** * **MySQL:** A popular open-source relational database, often simpler to set up than PostgreSQL and performs well for straightforward workloads. Metabase can use MySQL as its production database and connect to it for analytics. * **Microsoft SQL Server:** A robust relational database offering advanced analytics capabilities and high availability. * **Oracle Database:** Known for its comprehensive features, scalability, and security, suitable for mission-critical applications and advanced analytics. * **MariaDB:** An open-source relational database that is a community-developed fork of MySQL, offering similar benefits. **Query Engines:** These are not databases themselves but can query data from various sources, including real-time data, and are suitable for massive datasets. * **SparkSQL:** An open-source query engine that can ingest and query data from a wide variety of sources. * **Presto (and Starburst):** Open-source distributed SQL query engines designed for fast analytical queries against various data sources, including large data lakes. When choosing an alternative, consider factors such as data volume, query complexity, real-time analytics requirements, existing infrastructure, and budget. Metabase offers official connectors for all the mentioned databases, ensuring smooth integration for your analytics needs.

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Metabase supports a wide range of databases, making it adaptable to various analytical needs.\n\nHere are some of the best alternatives to PostgreSQL for analytics, categorized by type:\n\n**Cloud Data Warehouses (for large-scale analytics):**\nThese are often fully managed, scalable, and optimized for analytical queries on massive datasets.\n* **Amazon Redshift:** A fully-managed, petabyte-scale data warehouse service.\n* **Google BigQuery:** A fully-managed, serverless data warehouse that\u0027s highly scalable for large datasets.\n* **Snowflake:** A cloud-native data warehouse known for its flexibility and ability to handle diverse data workloads.\n* **Databricks:** A unified analytics platform built on Apache Spark, suitable for large-scale data processing and analytics.\n* **Vertica:** An analytical database designed for high performance on large datasets.\n\n**Specialized Analytical Databases:**\nThese databases are built from the ground up to excel in analytical processing.\n* **ClickHouse:** An open-source, column-oriented database management system optimized for online analytical processing (OLAP) queries.\n* **Druid:** A column-oriented, open-source, distributed data store ideal for real-time analytics on large datasets.\n\n**Other Relational Databases (can also handle analytical queries, especially with proper indexing and optimization):**\n* **MySQL:** A popular open-source relational database, often simpler to set up than PostgreSQL and performs well for straightforward workloads. Metabase can use MySQL as its production database and connect to it for analytics.\n* **Microsoft SQL Server:** A robust relational database offering advanced analytics capabilities and high availability.\n* **Oracle Database:** Known for its comprehensive features, scalability, and security, suitable for mission-critical applications and advanced analytics.\n* **MariaDB:** An open-source relational database that is a community-developed fork of MySQL, offering similar benefits.\n\n**Query Engines:**\nThese are not databases themselves but can query data from various sources, including real-time data, and are suitable for massive datasets.\n* **SparkSQL:** An open-source query engine that can ingest and query data from a wide variety of sources.\n* **Presto (and Starburst):** Open-source distributed SQL query engines designed for fast analytical queries against various data sources, including large data lakes.\n\nWhen choosing an alternative, consider factors such as data volume, query complexity, real-time analytics requirements, existing infrastructure, and budget. 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