Greenplum Database vs Informatica

Overview

Informatica

Stacks14

Followers2

Votes0

Greenplum Database

Stacks47

Followers111

Votes0

GitHub Stars6.2K

Forks1.7K

Greenplum Database vs Informatica: What are the differences?

1. **Architecture**: Greenplum Database is a massively parallel processing (MPP) database designed for analytics and business intelligence workloads, while Informatica is an integration platform that provides data integration, data quality, and data governance services. 2. **Use Cases**: Greenplum Database is optimized for analytical processing of large datasets, making it ideal for data warehousing and business intelligence applications. On the other hand, Informatica is used for data integration, data quality management, and master data management across various systems and applications. 3. **Scalability**: Greenplum Database is highly scalable and able to handle petabytes of data in a parallel processing manner, giving it an advantage in handling large-scale analytical workloads. In contrast, Informatica is designed to manage data integration tasks across diverse systems, providing a scalable solution for data integration needs. 4. **Data Transformation**: Greenplum Database focuses on high-performance analytics and complex queries with SQL, while Informatica specializes in data transformation, cleansing, and enrichment through its ETL (Extract, Transform, Load) capabilities. 5. **Vendor Focus**: Greenplum Database is developed and supported by Pivotal Software, focused on providing analytical processing solutions, while Informatica Corporation primarily offers data integration and management software to help organizations optimize their data infrastructure. 6. **Cost**: Greenplum Database may require an investment in hardware and licensing costs due to its specialized MPP architecture, while Informatica offers a range of pricing options based on the specific services and features needed for data integration and management tasks.

In Summary, Greenplum Database is geared towards analytical processing and data warehousing, focusing on large-scale analytics, while Informatica caters to data integration, quality management, and governance needs across multiple systems and applications.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

Informatica	Greenplum Database
It delivers enterprise data integration and management software powering analytics for big data and cloud. Unlock data's potential.	It is a massively parallel processing (MPP) database server with an architecture specially designed to manage large-scale analytic data warehouses and business intelligence workloads. It is based on PostgreSQL open-source technology.
Business Users on Data Analyst and Metadata management; Improved Administrator experience; Build in Intelligence to improve performance.	Core SQL Conformance; MPP Architecture; Innovative Query Optimization; Polymorphic Data Storage; Integrated In-Database Analytics
Statistics
GitHub Stars -	GitHub Stars 6.2K
GitHub Forks -	GitHub Forks 1.7K
Stacks 14	Stacks 47
Followers 2	Followers 111
Votes 0	Votes 0
Integrations
Amazon CloudFront Amazon Redshift Amazon RDS AWS CloudTrail	PostgreSQL Kong Slick Heroku Apache Hive Clever Cloud Couchbase Sequelize Sails.js Metabase

What are some alternatives to Informatica, Greenplum Database?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.