Software Alternatives, Accelerators & Startups

Apache Flink VS Greenplum Database

Compare Apache Flink VS Greenplum Database and see what are their differences

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Greenplum Database logo Greenplum Database

Greenplum Database is an open source parallel data warehousing platform.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • Greenplum Database Landing page
    Landing page //
    2023-07-29

Apache Flink videos

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Greenplum Database videos

No Greenplum Database videos yet. You could help us improve this page by suggesting one.

+ Add video

Category Popularity

0-100% (relative to Apache Flink and Greenplum Database)
Big Data
79 79%
21% 21
Databases
61 61%
39% 39
Stream Processing
100 100%
0% 0
Data Warehousing
0 0%
100% 100

User comments

Share your experience with using Apache Flink and Greenplum Database. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Apache Flink should be more popular than Greenplum Database. It has been mentiond 28 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Flink mentions (28)

  • Show HN: An SQS Alternative on Postgres
    You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 13 days ago
  • Top 10 Common Data Engineers and Scientists Pain Points in 2024
    Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 1 month ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
  • Go concurrency simplified. Part 4: Post office as a data pipeline
    Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago
  • Five Apache projects you probably didn't know about
    Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago
View more

Greenplum Database mentions (4)

  • Ask HN: It's 2023, how do you choose between MySQL and Postgres?
    Friends don't let their friends choose Mysql :) A super long time ago (decades) when I was using Oracle regularly I had to make a decision on which way to go. Although Mysql then had the mindshare I thought that Postgres was more similar to Oracle, more standards compliant, and more of a real enterprise type of DB. The rumor was also that Postgres was heavier than MySQL. Too many horror stories of lost data... - Source: Hacker News / about 1 year ago
  • Amazon Aurora's Read/Write Capability Enhancement with Apache ShardingSphere-Proxy
    A database solution architect at AWS, with over 10 years of experience in the database industry. Lili has been involved in the R&D of the Hadoop/Hive NoSQL database, enterprise-level database DB2, distributed data warehouse Greenplum/Apache HAWQ and Amazon’s cloud native database. - Source: dev.to / almost 2 years ago
  • What’s the Database Plus concept and what challenges can it solve?
    Today, it is normal for enterprises to leverage diversified databases. In my market of expertise, China, in the Internet industry, MySQL together with data sharding middleware is the go to architecture, with GreenPlum, HBase, Elasticsearch, Clickhouse and other big data ecosystems being auxiliary computing engine for analytical data. At the same time, some legacy systems (such as SQLServer legacy from .NET... - Source: dev.to / about 2 years ago
  • Inspecting joins in PostgreSQL
    PostgreSQL is a free and advanced database system with the capacity to handle a lot of data. It’s available for very large data in several forms like Greenplum and Redshift on Amazon. It is open source and is managed by an organized and very principled community. - Source: dev.to / over 2 years ago

What are some alternatives?

When comparing Apache Flink and Greenplum Database, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Amazon Redshift - Learn about Amazon Redshift cloud data warehouse.

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Microsoft Azure Data Lake - Azure Data Lake is a real-time data processing and analytics solution that works across platforms and languages.