33 Popular Database Technologies for Big Data Management 

my article on starburst data
My featured article on Starburst Data

I am a Customer Success Manager at Starburst Data who has authored a featured blog article, and I work with big tech companies that query an entire database of more than Terabytes of enterprise data every day. Using my expertise, here I created a list of 33 popular database technologies for companies that need to build big data management systems. This list aims to help data engineers, data scientists, data analysts, business intelligence and business analytics, SQL developer, data management teams, and database technology stakeholders who need inspiration for big data management and analyzing data. This list does not include visualization tools.

My List of My Database Technologies for Big Data

This is a list of database technologies for big data that I have experienced with my customers’ use for big data management. The description of each technology is an excerpt from its website or Wikipedia.

  1. Apache Hive – Apache Hive is a data warehouse software and big data management project built on top of Apache Hadoop for providing big data queries and analysis.

  2. MySQL – MySQL is an open-source relational database and big data management system.

  3. PostgreSQL – PostgreSQL, also known as Postgres, is a free and open-source relational database and big data management system emphasizing extensibility and SQL compliance.

  4. MongoDB – MongoDB is a source-available cross-platform document-oriented database management system and big data management program.

  5. Amazon Redshift – Amazon Redshift is a data warehouse product that forms part of the larger cloud-computing platform Amazon Web Services for companies in need of big data management.

  6. Azure Synapse Analytics – Azure Synapse Analytics is a limitless analytics service and big data management that brings together big data integration, enterprise big data warehousing, and big data analytics. 

  7. Google BigQuery – BigQuery is a fully-managed, serverless data warehouse and big data management system that enables scalable analysis over petabytes of data.

  8. IBM DB2 Warehouse – IBM® Db2® Warehouse provides a client-managed, preconfigured big data warehouse and big data storage that runs in private clouds, virtual private clouds, and other container-supported infrastructures. 

  9. Teradata – Teradata is the connected multi-cloud big data platform for enterprise analytics companies for data management.

  10. Databricks Delta Lake – Delta Lake is an open format storage layer managing data that delivers reliability, security, and performance on your data lake — for streaming, batch operations, and big data management. 

  11. Oracle Database – Oracle Database is a multi-model databases management system managing data produced and marketed by Oracle Corporation. 

  12. SAP HANA – SAP HANA is an in-memory, column-oriented, relational database management system developed and marketed by SAP SE.

  13. Apache Iceberg – Iceberg is a high-performance format for huge analytic tables and big data management. 

  14. Snowflake – Snowflake is a cloud computing–based data cloud management and database system.

Internet’s List of Database Technologies for Big Data

This is a list of database technologies for big data that I have heard many companies use for big data management. The description of each technology is an excerpt from its website or Wikipedia.

  • Dynamo DB
  • Elasticache
  • Google Sheets
  • Maria DB
  • Redis
  • Cassandra
  • Splunk
  • Kafka
  • Greenplum
  • MinIO
  • Netezza
  • Clickhouse
  • Druid
  • JMX
  • Kinesis
  • Kudu
  • Phoenix
  • Pinot 
  • Prometheus

Good Luck in Finding the Best Database Management System

Here I made a list of 33 big data management to help data engineers to manage data. It includes a wide variety of database technology like data storage (database objects), data warehouse, database server, data processing (or data processing tools), data warehouses, relational database, NoSQL databases, data integration tools, and many more. Data engineers and data scientists, wish you luck on your journey toward good data quality.

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