StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Flink vs Kapacitor

Apache Flink vs Kapacitor

OverviewDecisionsComparisonAlternatives

Overview

Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
Kapacitor
Kapacitor
Stacks40
Followers54
Votes0

Apache Flink vs Kapacitor: What are the differences?

## Apache Flink vs. Kapacitor

Apache Flink and Kapacitor are two popular tools used for stream processing, but they have key differences that distinguish them from each other.

1. **Design Philosophy**: Apache Flink is designed as a general-purpose stream processing framework that provides powerful data processing capabilities such as batch processing, stream processing, event-time processing, and iterative processing. On the other hand, Kapacitor is a lightweight data processing engine focused on high-performance stream processing and real-time alerting.

2. **Scalability**: Apache Flink is highly scalable and can easily handle large volumes of data with its distributed processing capabilities. It allows users to dynamically scale their processing resources based on workload and data requirements. In contrast, Kapacitor is not as scalable as Apache Flink and is primarily designed for smaller-scale data processing tasks.

3. **Ease of Use**: Apache Flink provides a rich set of APIs and libraries, making it suitable for a wide range of use cases. It offers APIs in Java, Scala, and Python, simplifying the development and deployment of streaming applications. Kapacitor, on the other hand, has a simpler architecture and is focused on providing stream processing and alerting capabilities with a more streamlined user experience.

4. **Data Processing Capabilities**: Apache Flink supports a wide range of operations for data processing, including windowing, joining, aggregating, and more. It also includes state management features for handling complex processing scenarios. Kapacitor, on the other hand, is more focused on real-time data processing and alerting, with support for stream processing tasks such as filtering, transformation, and anomaly detection.

5. **Integration**: Apache Flink integrates well with several storage systems, message queues, and data sources, allowing seamless data ingestion and processing. It also has connectors for popular frameworks like Apache Kafka and Apache Hadoop. Kapacitor, on the other hand, is designed to work closely with the TICK Stack, specifically with InfluxDB for data storage and alerting.

In Summary, Apache Flink and Kapacitor differ in their design philosophy, scalability, ease of use, data processing capabilities, and integration options, making them suitable for different types of stream processing tasks.

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)
or
Manual

Advice on Apache Flink, Kapacitor

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Flink
Apache Flink
Kapacitor
Kapacitor

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.

Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
can process both stream and batch data ; acting on data in real-time
Statistics
GitHub Stars
25.4K
GitHub Stars
-
GitHub Forks
13.7K
GitHub Forks
-
Stacks
534
Stacks
40
Followers
879
Followers
54
Votes
38
Votes
0
Pros & Cons
Pros
  • 16
    Unified batch and stream processing
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 8
    Easy to use streaming apis
  • 4
    Open Source
  • 2
    Low latency
No community feedback yet
Integrations
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka
InfluxDB
InfluxDB
Kafka
Kafka

What are some alternatives to Apache Flink, Kapacitor?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Storm

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase