October 2025
Intermediate to advanced
284 pages
7h
English
Content preview from Building Data Integration Solutions
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Chapter 6. Data Stores and Management Systems
Data storage does not necessarily need to impose structure on data. For example, a filesystem may store data as raw files with no inherent organization other than folder structure. However, throughout the majority of this book, data storage is equated with databases. And as discussed in Part I, there are two primary types of contemporary databases: relational databases and non-relational databases. For structured data that can be organized into rows and columns, relational databases are the obvious choice because of their ubiquity and efficiency. The relational databases described in this chapter include IBM Db2, Microsoft SQL Server, MySQL and MariaDB, Oracle Database, PostgreSQL, SQLite, and Sybase and SAP.
For unstructured data, such as images, videos, audio files, and text files, non-relational databases are more appropriate than relational databases. We’ll go over multiple kinds of non-relational databases in this chapter, including document stores and key-value storage, graph databases, vector databases, and wide-column stores. The specific non-relational databases covered in this chapter include Amazon DynamoDB and DocumentDB, Apache Ignite, MongoDB, Redis, Amazon Neptune, Neo4j, TigerGraph, Pinecone, Apache Cassandra and HBase, AWS Keyspaces, and Google Bigtable.
Data warehouses, data lakes, and data lakehouses are also discussed in this chapter and include product offerings such as Amazon Redshift, Apache Doris, Apache Druid, ...
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