What are the best database technologies for BI?

1 Relational databases

Relational databases are the traditional and widely used database technology for BI. They organize data into tables with predefined columns and rows, and use structured query language (SQL) to manipulate and query data. Relational databases are good for BI because they offer high performance, consistency, security, and scalability. They can handle complex queries, join data from multiple tables, and enforce data integrity and quality rules. Some of the most popular relational databases for BI are MySQL, Oracle, SQL Server, and PostgreSQL.

2 NoSQL databases

NoSQL databases are a newer and more flexible database technology for BI. They store data in non-tabular formats, such as documents, graphs, key-value pairs, or columns. They use different query languages or APIs to access and manipulate data. NoSQL databases are good for BI because they can handle large volumes, variety, and velocity of data. They can also support unstructured or semi-structured data, such as text, images, or JSON. They can scale horizontally and provide high availability and fault tolerance. Some of the most popular NoSQL databases for BI are MongoDB, Cassandra, Neo4j, and Redis.

3 Data warehouses

Data warehouses are a specialized database technology for BI. They store data that has been extracted, transformed, and loaded (ETL) from various sources and applications, and optimized for analysis and reporting. They use a dimensional model that consists of facts and dimensions, and supports multidimensional queries and aggregations. Data warehouses are good for BI because they provide a single source of truth, historical perspective, and fast query response. They can also integrate data from different domains and systems, and enable complex and ad hoc analysis. Some of the most popular data warehouses for BI are Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure SQL Data Warehouse.

4 Data lakes

Data lakes are a relatively new and emerging database technology for BI. They store data in its raw and original format, without any predefined schema or structure. They use a distributed file system or object storage to store data as files or objects, and use various tools and frameworks to process and analyze data. Data lakes are good for BI because they can store any type of data, from structured to unstructured, and from batch to streaming. They can also support exploratory and advanced analysis, such as machine learning and artificial intelligence. Some of the most popular data lakes for BI are Amazon S3, Google Cloud Storage, Hadoop, and Apache Spark.

5 Hybrid databases

Hybrid databases are a combination of two or more database technologies for BI. They aim to leverage the best features and capabilities of each technology, and overcome their limitations and challenges. They can store data in different formats and models, and use different query languages and APIs to access and manipulate data. Hybrid databases are good for BI because they can support diverse and dynamic data requirements, and provide flexibility and versatility. They can also bridge the gap between operational and analytical systems, and enable real-time and near-real-time analysis. Some of the most popular hybrid databases for BI are MongoDB Atlas, Azure Cosmos DB, Couchbase, and FaunaDB.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?