What are the best alternatives to Postgres for analytics?

When seeking alternatives to PostgreSQL for analytical workloads, especially for use with Metabase, several databases are designed to outperform PostgreSQL in large-scale data analysis. These alternatives often feature columnar storage, distributed architectures, and optimizations for complex queries, contrasting with PostgreSQL's strength in transactional processing. Some of the best alternatives to PostgreSQL for analytics, which are also officially supported by Metabase, include: * **ClickHouse** is an open-source, columnar analytical database management system specifically optimized for fast read performance and complex analytical queries over large datasets. It excels in real-time analytics, event tracking, and time-series data, offering significant speed and efficiency advantages over PostgreSQL for massive data processing. Metabase has an official connector for ClickHouse. * **Snowflake** is a cloud-native data platform built for Online Analytical Processing (OLAP), capable of managing petabytes of historical data and supporting thousands of concurrent analytical users. Its architecture separates storage and compute, allowing independent scaling and improving query performance drastically for large datasets. Metabase officially supports Snowflake. * **Amazon Redshift** is a fully managed, petabyte-scale data warehouse service in the cloud. While based on PostgreSQL, it has been optimized with columnar storage and a massively parallel processing (MPP) architecture specifically for data warehousing and business intelligence tasks. Redshift is designed for fast analytics on large datasets and integrates well with AWS services. Metabase offers an official connector for Amazon Redshift. * **Google BigQuery** is a fully managed, serverless data warehouse designed for high-performance analytics. It leverages Google's infrastructure for processing massive datasets, using a columnar storage format for efficient data compression and fast query execution, even with SQL-like queries. BigQuery is ideal for business analytics, large-scale data processing, and real-time insights. Metabase officially supports Google BigQuery. These alternatives are generally better suited for analytical workloads due to their architectural design, which prioritizes fast query performance, scalability, and efficiency when dealing with large volumes of data for reporting and business intelligence.

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These alternatives often feature columnar storage, distributed architectures, and optimizations for complex queries, contrasting with PostgreSQL\u0027s strength in transactional processing.\n\nSome of the best alternatives to PostgreSQL for analytics, which are also officially supported by Metabase, include:\n\n* **ClickHouse** is an open-source, columnar analytical database management system specifically optimized for fast read performance and complex analytical queries over large datasets. It excels in real-time analytics, event tracking, and time-series data, offering significant speed and efficiency advantages over PostgreSQL for massive data processing. Metabase has an official connector for ClickHouse.\n* **Snowflake** is a cloud-native data platform built for Online Analytical Processing (OLAP), capable of managing petabytes of historical data and supporting thousands of concurrent analytical users. Its architecture separates storage and compute, allowing independent scaling and improving query performance drastically for large datasets. Metabase officially supports Snowflake.\n* **Amazon Redshift** is a fully managed, petabyte-scale data warehouse service in the cloud. While based on PostgreSQL, it has been optimized with columnar storage and a massively parallel processing (MPP) architecture specifically for data warehousing and business intelligence tasks. Redshift is designed for fast analytics on large datasets and integrates well with AWS services. Metabase offers an official connector for Amazon Redshift.\n* **Google BigQuery** is a fully managed, serverless data warehouse designed for high-performance analytics. It leverages Google\u0027s infrastructure for processing massive datasets, using a columnar storage format for efficient data compression and fast query execution, even with SQL-like queries. BigQuery is ideal for business analytics, large-scale data processing, and real-time insights. 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href=\"https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG4E-3YMPeecLUE7ZWtX_uP08lom8UCTfgkvfp-842hxHMDuU2krzxNuUsgRe28gJE851oMTwkIIrGt6HGVppqIiVs7vQIIMMqUU_BJbFfLGOVLamTHLcU6YqmTncopuVutXARNZ1C_yHtK7X8IPeT9DYsrk_uXCTwJ8BaQRbaiCUCAaQDsppqiIV_Odvn0BEKeWstXfuWEV1SjQfgauBk8q6KIRzX-H_wd77PboDjrnw==\"\u003ebest alternatives to PostgreSQL for analytics\u003c/a\u003e\n \u003ca class=\"chip\" href=\"https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFIjdTYYwPuSEQwfADY3asec7c4arT915jhBN6kMoY12LpelDoJ3UxtNl-c5Jdbyjpdh0gUiPz8jkyd8rjKFm1vLISOXWZb0T7mPM4A_lcLd-PKNqQfXtr0NWATkECWtkt-pocDo2tmyQ9Jyp39SgRSR9Yam0BDbsmlGeV7DzupkMWgEAFhW8ndY_iAAD0ps8bcuJhEzx1jpeHGKgUXJ9TmuuQtTdOU-A==\"\u003ePostgreSQL vs Redshift for analytics\u003c/a\u003e\n \u003ca class=\"chip\" 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