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PostgreSQL is great for data analytics and engineering work.
July 24, 2019

PostgreSQL is great for data analytics and engineering work.

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with PostgreSQL

PostgreSQL on Greenplum is being used as a data warehouse by the entire data and analytics team on my project. There are also other teams using the database as well, but it solves the business problems of running large analytics workflows with billions of rows of archived data to create reporting dashboards. It is able to run in a massively parallel processing format.
  • data processing
  • big data analytics
  • data aggregation
  • SQL syntax support
  • query error handling
  • programmatic access
  • provide better data visibility and analytics
  • creates new business opportunities in analytics
  • needs more technical support and administration
Postgresql is the best tool out there for relational data so I have to give it a high rating when it comes to analytics, data availability and consistency, so on and so forth. SQL is also a relatively consistent language so when it comes to building new tables and loading data in from the OLTP database, there are enough tools where we can perform ETL on a scalable basis.
PostgreSQL is the proper tool when data consistency matters and other BASE or document-based databases are simply improper. I think PostgreSQL has a fantastic system of slony replication, triggers, and other data maintenance functionality that other databases generally don't have. The themes of postgres are also varied, but postgres was selected because of it's massively parallel processing functionality and data consistency.
PostgreSQL is great as a data warehousing solution in large organizations but it is also problematic when it is improperly used as a transactional database. Postgres is a OLAP, not an OLTP database where you would use something like MySQL instead for storing live data. It has great read but poor write speeds.