DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Enterprise AI Trend Report: Gain insights on ethical AI, MLOps, generative AI, large language models, and much more.

2024 Cloud survey: Share your insights on microservices, containers, K8s, CI/CD, and DevOps (+ enter a $750 raffle!) for our Trend Reports.

PostgreSQL: Learn about the open-source RDBMS' advanced capabilities, core components, common commands and functions, and general DBA tasks.

AI Automation Essentials. Check out the latest Refcard on all things AI automation, including model training, data security, and more.

Related

  • Real-Time Analytics: All Data, Any Data, Any Scale, at Any Time
  • Cognitive and Perspective Analytics
  • The State of Observability 2024: Navigating Complexity With AI-Driven Insights
  • Unlocking the Power Duo: Kafka and ClickHouse for Lightning-Fast Data Processing

Trending

  • Harnessing the Power of Observability in Kubernetes With OpenTelemetry
  • Top Secrets Management Tools for 2024
  • The Power of Generative AI: How It Is Revolutionizing Business Process Automation
  • Deploying Heroku Apps To Staging and Production Environments With GitLab CI/CD
  1. DZone
  2. Data Engineering
  3. Data
  4. Why Apache Kafka and Apache Flink Work Well Together to Boost Real-Time Data Analytics

Why Apache Kafka and Apache Flink Work Well Together to Boost Real-Time Data Analytics

Use Flink and Kafka to create reliable, scalable, low-latency real-time data processing pipelines with fault tolerance and exactly-once processing guarantees.

By 
Gautam Goswami user avatar
Gautam Goswami
DZone Core CORE ·
Feb. 13, 24 · Analysis
Like (1)
Save
Tweet
Share
3.8K Views

Join the DZone community and get the full member experience.

Join For Free

When data is analyzed and processed in real time, it can yield insights and actionable information either instantly or with very little delay from the time the data is collected. The capacity to collect, handle, and retain user-generated data in real time is crucial for many applications in today’s data-driven environment. 

There are various ways to emphasize the significance of real-time data analytics like timely decision-making, IoT and sensor data processing, enhanced customer experience, proactive problem resolution, fraud detection and security, etc. Rising to the demands of diverse real-time data processing scenarios, Apache Kafka has established itself as a dependable and scalable event streaming platform. 

In short, the process of collecting data in real-time as streams of events from event sources such as databases, sensors, and software applications is known as event streaming. With real-time data processing and analytics in mind, Apache Flink is a potent open-source program. For situations where quick insights and minimal processing latency are critical, it offers a consistent and effective platform for managing continuous streams of data. 

Apache Flink and Kafka working together

Causes for the Improved Collaboration Between Apache Flink and Kafka

  • Apache Flink joined the Apache Incubator in 2014, and since its inception, Apache Kafka has consistently stood out as one of the most frequently utilized connectors for Apache Flink. It is just a data processing engine that can be clubbed with the processing logic but does not provide any storage mechanism. Since Kafka provides the foundational layer for storing streaming data, Flink can serve as a computational layer for Kafka, powering real-time applications and pipelines.
  • Apache Flink has produced first-rate support for creating Kafka-based apps throughout the years. By utilizing the numerous services and resources offered by the Kafka ecosystem, Flink applications are able to leverage Kafka as both a source and a sink. Avro, JSON, and Protobuf are just a few widely used formats that Flink natively supports.
  • Apache Kafka proved to be an especially suitable match for Apache Flink. Unlike alternative systems such as ActiveMQ, RabbitMQ, etc., Kafka offers the capability to durably store data streams indefinitely, enabling consumers to read streams in parallel and replay them as necessary. This aligns with Flink’s distributed processing model and fulfills a crucial requirement for Flink’s fault tolerance mechanism.
  • Kafka can be used by Flink applications as a source as well as a sink by utilizing the many tools and services available in the Kafka ecosystem. Flink offers native support for commonly used formats like Avro, JSON, and Protobuf, similar to Kafka’s support for these formats.
  • Other external systems can be linked to Flink’s Table API and SQL programs to read and write batch and streaming tables. Access to data kept in external systems such as a file system, database, message queue, or key-value store is made possible by a table source. For Kafka, it’s nothing but a key-value pair.  Events are added to the Flink table in a similar manner as they are appended to the Kafka topic. A topic in a Kafka cluster is mapped to a table in Flink. In Flink, each table is equal to a stream of events that describe the modifications being made to that particular table. The table is automatically updated when a query refers to it, and its results are either materialized or emitted.

Conclusion

In conclusion, we can create reliable, scalable, low-latency real-time data processing pipelines with fault tolerance and exactly-once processing guarantees by combining Apache Flink and Apache Kafka. For businesses wishing to instantly evaluate and gain insights from streaming data, this combination provides a potent option.

Thank you for reading this write-up. If you found this content valuable, please consider liking and sharing.

Analytics Apache Flink Data (computing) kafka

Published at DZone with permission of Gautam Goswami, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

Related

  • Real-Time Analytics: All Data, Any Data, Any Scale, at Any Time
  • Cognitive and Perspective Analytics
  • The State of Observability 2024: Navigating Complexity With AI-Driven Insights
  • Unlocking the Power Duo: Kafka and ClickHouse for Lightning-Fast Data Processing

Partner Resources


Comments

ABOUT US

  • About DZone
  • Send feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends: