Software Alternatives & Reviews

Kudu VS Apache Flink

Compare Kudu VS Apache Flink and see what are their differences

Kudu logo Kudu

Your personal AdWords expert

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
  • Kudu Landing page
    Landing page //
    2021-09-28
  • Apache Flink Landing page
    Landing page //
    2023-10-03

Kudu

Categories
  • Data Dashboard
  • Big Data
  • Databases
  • Data Warehousing
Website kudu.apache.org
Details $-

Apache Flink

Categories
  • Stream Processing
  • Big Data
  • Developer Tools
  • Web Framework
Website flink.apache.org
Details $

Kudu videos

Knife Review : Cold Steel Kudu

More videos:

  • Review - Grill Spotlight: Kudu Open Fire Grill (Unboxing, Setup, & Review)
  • Review - Product Review: Cold Steel Kudu

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

Category Popularity

0-100% (relative to Kudu and Apache Flink)
Databases
15 15%
85% 85
Big Data
7 7%
93% 93
Stream Processing
0 0%
100% 100
Data Dashboard
100 100%
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 26 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.

Kudu mentions (0)

We have not tracked any mentions of Kudu yet. Tracking of Kudu recommendations started around Mar 2021.

Apache Flink mentions (26)

  • 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 / about 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 / 3 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 / 3 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 / 3 months ago
  • Snowflake - what are the streaming capabilities it provides?
    When low latency matters you should always consider an ETL approach rather than ELT, e.g. Collect data in Kafka and process using Kafka Streams/Flink in Java or Quix Streams/Bytewax in Python, then sink it to Snowflake where you can handle non-critical workloads (as is the case for 99% of BI/analytics). This way you can choose the right path for your data depending on how quickly it needs to be served. Source: 11 months ago
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What are some alternatives?

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

Vertica - Vertica is a grid-based, column-oriented database designed to manage large, fast-growing volumes of...

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

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

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

Apache Druid - Fast column-oriented distributed data store

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