How to integrate IBM Netezza in Heroku Postgres

Discover how to integrate IBM Netezza and Heroku Postgres and instantly get access to your data.

WITH DATA VIRTUALITY PIPES
Replicate IBM Netezza data into Heroku Postgres and analyze it with your BI Tool.
How to integrate IBM Netezza in Heroku Postgres with Pipes


About IBM Netezza

IBM Netezza designs and markets advanced data warehouse appliances for analytics applications. Use cases for IBM Netezza include business intelligence, data warehousing, business continuity planning and predictive analytics. The Netezza SQL commands support the standard grammar (SQL-92). The purpose-built, embedded, advanced analytics platform IBM Netezza Analytics helps client's analytics to meet their business demands.

About Heroku Postgres

Heroku Postgres provides a SQL database-as-a-service based on PostgreSQL. Besides the database Heroku Postgres delivers an additional toolset consisting of the possibility to fork the database to clone it with a single command for running test on the clone, read-only replicas of the database, continuous protection and mutli-ingress to access the database from anywhere on the internet.

MOVE YOUR DATA WITH PIPES

Pipes allows you to connect to IBM Netezza, Heroku Postgres and more than 200 other cloud services and databases. Automate your data workflows with data pipelines.

DATA VIRTUALITY OFFERS TWO PIPES SOLUTIONS

depending on your needs.

Pipes

Easy and reliable data replication in the cloud

Pipes enables you to move data from any data source to your target storage or data warehouse on schedule. Integrate your data with just a few clicks and without any coding.

Pipes Professional

Advanced data replication hosted in the cloud or on-premises

Pipes Professional enables you to transform and model your data with SQL before replication. Customize your data pipelines and build data models across sources in 80% less time.

Pipes Professional features include:

  • SQL Modelling Layer
  • Complex Replication Types
  • Advanced Jobs & Schedules
  • Metadata Repositories
  • Custom Extraction Definitions
  • Job Dependencies
  • Multiple Target Storages
  • Data Federation Features
  • Custom Job Triggers
Your future with automated data workflows is only a few clicks away.