These data warehouses each work a little differently, but when you want to run a query, they’ll utilize that parallelization to split up your data and aggregate the results before returning your query. Products like BigQuery, AWS Redshift, Snowflake, and Vertica are able to process big data because they’re powered by huge numbers of parallel engines, making analytical queries across massive data sources much more manageable. Includes: BigQuery, Redshift, Snowflake, Verticaĭata warehouses are well-equipped to execute analytical queries on large amounts of data. At that point, it may be time to look at another technology specifically built for big data and analytical queries. As you scale up and your analytical queries become more complex, they may run slowly no matter how much hardware you throw at the problem. While you can accomplish analytical queries via any of these four engines, it can require a lot of extra hardware (additional RAM, CPUs, and faster disks) to keep up with the growth of the volume of your data, and that gets expensive very quickly. These four systems are transactional databases, and aren’t specifically built for analytical queries. A lot of this depends on how your data warehouse is configured and whether you’re using ETL processes to aggregate data so your database has to read fewer rows.
Traditional relational databases can handle analytical queries of hundreds of thousands of records with little problem, but if your organization needs to significantly scale up - think millions of records or more - you may hit a ceiling with your RDBMS. Metabase, the software, can use either Postgres or MySQL for its production database. These databases are well-suited for use as the application database for your software. Lots of organizations that use Metabase do so simply by connecting their relational database so they can start getting insights right away. If your organization is short on time or resources, or lacks any data experts to build ETLs, a relational database system like MySQL, Postgres, SQL Server, or Oracle Database is a great option. Of these four, MySQL and PostgreSQL are open source. Includes: Microsoft SQL Server, MySQL, Oracle Database, PostgreSQLĪll four of these relational database management systems (RDBMS) have been around for decades, and handle all-around database needs - inserting records, reading, updating, deleting records - quite well. All-around workhorses: traditional relational database management systems Here’s a rundown of the major players that Metabase supports, whether you’re new to the data space or just need a refresher. There are a lot of database and data source options out there to consider when building your data stack. For ad hoc analysis: file-based databases.All-around workhorses: traditional relational database management systems.
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