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IBM Streaming Analytics VS Apache Flink

Compare IBM Streaming Analytics VS Apache Flink and see what are their differences

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IBM Streaming Analytics logo IBM Streaming Analytics

IBM Streaming Analytics enables you to analyze a broad range of streaming text, video, audio, geospatial and sensor data.

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
  • IBM Streaming Analytics Landing page
    Landing page //
    2023-02-11
  • Apache Flink Landing page
    Landing page //
    2023-10-03

IBM Streaming Analytics videos

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IBM Streaming Analytics and Python

Apache Flink videos

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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

Category Popularity

0-100% (relative to IBM Streaming Analytics and Apache Flink)

IBM Streaming Analytics

Apache Flink

Data Management
100 100%
Data Management
0% 0
Big Data
0 0%
Big Data
100% 100
Stream Processing
12 12%
Stream Processing
88% 88
Elastic Search
100 100%
Elastic Search
0% 0

User comments

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Social recommendations and mentions

Based on our record, Apache Flink seems to be more popular. It has been mentiond 27 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.

IBM Streaming Analytics mentions (0)

We have not tracked any mentions of IBM Streaming Analytics yet. Tracking of IBM Streaming Analytics recommendations started around Mar 2021.

Apache Flink mentions (27)

  • 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 / 13 days 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 / 2 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 / 4 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 / 4 months ago
  • Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
    Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 4 months ago
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What are some alternatives?

When comparing IBM Streaming Analytics and Apache Flink, you can also consider the following products

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

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

TIBCO Spotfire - TIBCO Spotfire is a Business Intelligence (BI) solution that provides users with executive dashboards, data visualization, data analytics and KPIs push to mobile devices.

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

Amazon Elasticsearch Service - Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch in the AWS Cloud.

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

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