In today's data-driven world, the need for real-time data streaming and processing has become paramount. Apache Kafka, an open-source distributed event streaming platform, has emerged as a fundamental technology in meeting this demand.
Streaming Data Ingest and Processing with Apache KafkaAttunity
Apache™ Kafka is a fast, scalable, durable, and fault-tolerant
publish-subscribe messaging system. It offers higher throughput, reliability and replication. To manage growing data volumes, many companies are leveraging Kafka for streaming data ingest and processing.
Join experts from Confluent, the creators of Apache™ Kafka, and the experts at Attunity, a leader in data integration software, for a live webinar where you will learn how to:
-Realize the value of streaming data ingest with Kafka
-Turn databases into live feeds for streaming ingest and processing
-Accelerate data delivery to enable real-time analytics
-Reduce skill and training requirements for data ingest
The recorded webinar on slide 32 includes a demo using automation software (Attunity Replicate) to stream live changes from a database into Kafka and also includes a Q&A with our experts.
For more information, please go to www.attunity.com/kafka.
Apache kafka an ideal data streaming solution for your banksandipanmukherjee13
The world is being devoured by software, especially in the banking sector. Many use cases need continuous real-time data integration and processing. Apache Kafka is used in a variety of sectors for mission-critical transactional workloads as well as big data analytics. Whether you need to interface with old systems, process mission-critical payment data, or develop batch reports and analytic models, Kafka is a popular architectural choice. It is used throughout the banking services industry for mission-critical transactional workloads and big data analytics. The success of Kafka can be attributed to its great scalability, dependability, and elastic open infrastructure.
Data Analytics is often described as one of the biggest challenges associated with big data, but even before that step can happen, data must be ingested and made available to enterprise users. That’s where Apache Kafka comes in.
In this Kafka Tutorial, we will discuss Kafka Architecture. In this Kafka Architecture article, we will see API’s in Kafka. Moreover, we will learn about Kafka Broker, Kafka Consumer, Zookeeper, and Kafka Producer. Also, we will see some fundamental concepts of Kafka.
Streaming Data Ingest and Processing with Apache KafkaAttunity
Apache™ Kafka is a fast, scalable, durable, and fault-tolerant
publish-subscribe messaging system. It offers higher throughput, reliability and replication. To manage growing data volumes, many companies are leveraging Kafka for streaming data ingest and processing.
Join experts from Confluent, the creators of Apache™ Kafka, and the experts at Attunity, a leader in data integration software, for a live webinar where you will learn how to:
-Realize the value of streaming data ingest with Kafka
-Turn databases into live feeds for streaming ingest and processing
-Accelerate data delivery to enable real-time analytics
-Reduce skill and training requirements for data ingest
The recorded webinar on slide 32 includes a demo using automation software (Attunity Replicate) to stream live changes from a database into Kafka and also includes a Q&A with our experts.
For more information, please go to www.attunity.com/kafka.
Apache kafka an ideal data streaming solution for your banksandipanmukherjee13
The world is being devoured by software, especially in the banking sector. Many use cases need continuous real-time data integration and processing. Apache Kafka is used in a variety of sectors for mission-critical transactional workloads as well as big data analytics. Whether you need to interface with old systems, process mission-critical payment data, or develop batch reports and analytic models, Kafka is a popular architectural choice. It is used throughout the banking services industry for mission-critical transactional workloads and big data analytics. The success of Kafka can be attributed to its great scalability, dependability, and elastic open infrastructure.
Data Analytics is often described as one of the biggest challenges associated with big data, but even before that step can happen, data must be ingested and made available to enterprise users. That’s where Apache Kafka comes in.
In this Kafka Tutorial, we will discuss Kafka Architecture. In this Kafka Architecture article, we will see API’s in Kafka. Moreover, we will learn about Kafka Broker, Kafka Consumer, Zookeeper, and Kafka Producer. Also, we will see some fundamental concepts of Kafka.
Un'introduzione ad Apache Kafka e Kafka Connect APIs (part of Apache Kafka), in particolare come Kafka possa essere usato assieme ad Elasticsearch.
Grazie a Seacom per averci invitato all'evento a Roma.
Solution Brief: Real-Time Pipeline AcceleratorBlueData, Inc.
Get started with Spark Streaming, Kafka, and Cassandra for real-time data analytics.
BlueData makes it easy to deploy Spark infrastructure and applications on- premises. The BlueData EPIC software platform is purpose-built to simplify and accelerate the deployment of Spark, Hadoop, and other tools for Big Data analytics—leveraging Docker containers and virtualized infrastructure.
Our new Real-Time Pipeline Accelerator solution provides the software and professional services you need for building data pipelines in a multi-tenant environment for Spark Streaming, Kafka, and Cassandra. With help from the BlueData team, you’ll also have two end-to-end real-time data pipelines as a starting point.
Learn more about BlueData at www.bluedata.com
https://www.learntek.org/blog/apache-kafka/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/
https://www.learntek.org/blog/apache-kafka/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Lesfurest.com invited me to talk about the KAPPA Architecture style during a BBL.
Kappa architecture is a style for real-time processing of large volumes of data, combining stream processing, storage, and serving layers into a single pipeline. It's different from the Lambda architecture, uses separate batch and stream processing pipelines.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
When NOT to Use Apache Kafka? With Kai Waehner | Current 2022HostedbyConfluent
When NOT to Use Apache Kafka? With Kai Waehner | Current 2022
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
Event Driven Architectures with Apache Kafka on HerokuHeroku
Apache Kafka is the backbone for building architectures that deal with billions of events a day. Chris Castle, Developer Advocate, will show you where it might fit in your roadmap.
- What Apache Kafka is and how to use it on Heroku
- How Kafka enables you to model your data as immutable streams of events, introducing greater parallelism into your applications
- How you can use it to solve scale problems across your stack such as managing high throughput inbound events and building data pipelines
Learn more at https://www.heroku.com/kafka
Reveal.js version of slides: http://slides.com/christophercastle/deck#/
Leveraging Mainframe Data for Modern Analyticsconfluent
“The mainframe is going away” is as true now as it was 10, 20 and 30 years ago. Mainframes are still crucial in handling critical business transactions, they were however built for an era where batch data movement was the norm and can be difficult to integrate into today’s data-driven, real-time, analytics-focused business processes as well as the environments that support them. Until now.
Join experts from Confluent, Attunity, and Capgemini for a one-hour online talk session where you’ll learn how to:
Unlock your mainframe data with unique change data capture (CDC) functionality without incurring the complexity and expense that come with sending ongoing queries into the mainframe database
How using CDC benefits advanced analytics approaches such as deep machine learning and predictive analytics
Deliver ongoing streams of data in real-time to the most demanding analytics environments
Ensure that your analytics environment includes the broadest possible range of data sources and destinations while ensuring true enterprise-grade functionality
Identify use cases that can help you get started delivering value to the business moving from POC to Pilot to Production
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. Learn the differences between a event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
Big data + cloud computing glossary for communityKumar Chinnakali
150 Big data and Cloud Computing related words
with their definitions you must know !
Say Hi to Henry the owl!
Henry, thesmartest and wisest of all, is the Big Data and Cloud expert who has gained his knowledge by surfing the clouds his entire life .
Being a curious Owl, he always used to explore the Big Data tools and terms which he came across during his expedition in the clouds.
Neha Narkhede talks about the experience at LinkedIn moving from batch-oriented ETL to real-time streams using Apache Kafka and how the design and implementation of Kafka was driven by this goal of acting as a real-time platform for event data. She covers some of the challenges of scaling Kafka to hundreds of billions of events per day at Linkedin, supporting thousands of engineers, etc.
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and K...Timothy Spann
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and Kafka
Apache NiFi, Apache Flink, Apache Kafka
Timothy Spann
Principal Developer Advocate
Cloudera
Data in Motion
https://budapestdata.hu/2023/en/speakers/timothy-spann/
Timothy Spann
Principal Developer Advocate
Cloudera (US)
LinkedIn · GitHub · datainmotion.dev
June 8 · Online · English talk
Building Modern Data Streaming Apps with NiFi, Flink and Kafka
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink SQL. We will stream data into Apache Iceberg.
We use the best streaming tools for the current applications with FLaNK. flankstack.dev
BIO
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
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Un'introduzione ad Apache Kafka e Kafka Connect APIs (part of Apache Kafka), in particolare come Kafka possa essere usato assieme ad Elasticsearch.
Grazie a Seacom per averci invitato all'evento a Roma.
Solution Brief: Real-Time Pipeline AcceleratorBlueData, Inc.
Get started with Spark Streaming, Kafka, and Cassandra for real-time data analytics.
BlueData makes it easy to deploy Spark infrastructure and applications on- premises. The BlueData EPIC software platform is purpose-built to simplify and accelerate the deployment of Spark, Hadoop, and other tools for Big Data analytics—leveraging Docker containers and virtualized infrastructure.
Our new Real-Time Pipeline Accelerator solution provides the software and professional services you need for building data pipelines in a multi-tenant environment for Spark Streaming, Kafka, and Cassandra. With help from the BlueData team, you’ll also have two end-to-end real-time data pipelines as a starting point.
Learn more about BlueData at www.bluedata.com
https://www.learntek.org/blog/apache-kafka/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/
https://www.learntek.org/blog/apache-kafka/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Lesfurest.com invited me to talk about the KAPPA Architecture style during a BBL.
Kappa architecture is a style for real-time processing of large volumes of data, combining stream processing, storage, and serving layers into a single pipeline. It's different from the Lambda architecture, uses separate batch and stream processing pipelines.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
When NOT to Use Apache Kafka? With Kai Waehner | Current 2022HostedbyConfluent
When NOT to Use Apache Kafka? With Kai Waehner | Current 2022
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
Event Driven Architectures with Apache Kafka on HerokuHeroku
Apache Kafka is the backbone for building architectures that deal with billions of events a day. Chris Castle, Developer Advocate, will show you where it might fit in your roadmap.
- What Apache Kafka is and how to use it on Heroku
- How Kafka enables you to model your data as immutable streams of events, introducing greater parallelism into your applications
- How you can use it to solve scale problems across your stack such as managing high throughput inbound events and building data pipelines
Learn more at https://www.heroku.com/kafka
Reveal.js version of slides: http://slides.com/christophercastle/deck#/
Leveraging Mainframe Data for Modern Analyticsconfluent
“The mainframe is going away” is as true now as it was 10, 20 and 30 years ago. Mainframes are still crucial in handling critical business transactions, they were however built for an era where batch data movement was the norm and can be difficult to integrate into today’s data-driven, real-time, analytics-focused business processes as well as the environments that support them. Until now.
Join experts from Confluent, Attunity, and Capgemini for a one-hour online talk session where you’ll learn how to:
Unlock your mainframe data with unique change data capture (CDC) functionality without incurring the complexity and expense that come with sending ongoing queries into the mainframe database
How using CDC benefits advanced analytics approaches such as deep machine learning and predictive analytics
Deliver ongoing streams of data in real-time to the most demanding analytics environments
Ensure that your analytics environment includes the broadest possible range of data sources and destinations while ensuring true enterprise-grade functionality
Identify use cases that can help you get started delivering value to the business moving from POC to Pilot to Production
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. Learn the differences between a event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
Big data + cloud computing glossary for communityKumar Chinnakali
150 Big data and Cloud Computing related words
with their definitions you must know !
Say Hi to Henry the owl!
Henry, thesmartest and wisest of all, is the Big Data and Cloud expert who has gained his knowledge by surfing the clouds his entire life .
Being a curious Owl, he always used to explore the Big Data tools and terms which he came across during his expedition in the clouds.
Neha Narkhede talks about the experience at LinkedIn moving from batch-oriented ETL to real-time streams using Apache Kafka and how the design and implementation of Kafka was driven by this goal of acting as a real-time platform for event data. She covers some of the challenges of scaling Kafka to hundreds of billions of events per day at Linkedin, supporting thousands of engineers, etc.
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https://budapestdata.hu/2023/en/speakers/timothy-spann/
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Principal Developer Advocate
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LinkedIn · GitHub · datainmotion.dev
June 8 · Online · English talk
Building Modern Data Streaming Apps with NiFi, Flink and Kafka
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink SQL. We will stream data into Apache Iceberg.
We use the best streaming tools for the current applications with FLaNK. flankstack.dev
BIO
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
When stars align: studies in data quality, knowledge graphs, and machine lear...
Apache Kafka Use Cases_ When To Use It_ When Not To Use_.pdf
1. Apache Kafka Use Cases: When To Use It?
When Not To Use?
In today's data-driven world, the need for real-time data streaming and processing has
become paramount. Apache Kafka, an open-source distributed event streaming
platform, has emerged as a fundamental technology in meeting this demand.
While Kafka offers numerous advantages, it's essential to understand when it's the
optimal choice and when other solutions might be more suitable. In this comprehensive
guide, we'll explore various use cases for Apache Kafka, shedding light on when it
excels and when alternative options might be preferred.
2. Key components and concepts of Apache Kafka include
Apache Kafka is an open-source distributed event streaming platform developed by the
Apache Software Foundation. It is designed to handle large volumes of real-time data
and facilitate the seamless, high-throughput, and fault-tolerant transmission of data
streams across different applications and systems.
Kafka was originally created by LinkedIn and later open-sourced as part of the Apache
project, becoming a fundamental tool for building real-time data pipelines and
event-driven architectures.
3. Topics & Partitions
Data streams in Kafka are organized into topics, which serve as logical channels for
publishing and subscribing to data. Each topic can have multiple producers and
consumers. Each topic is divided into partitions to enable parallel processing and
distribution of data. Partitions are replicated across multiple brokers for fault tolerance.
4. Producers & Consumers
Producers are responsible for sending data records to Kafka topics. They can be various
data sources, applications, or systems that generate data. Consumers read and process
data from Kafka topics. They can be applications, services, or systems that subscribe to
one or more topics to receive real-time data updates.
Brokers
Kafka brokers form the core of the Kafka cluster. They store and manage data records,
serving as the central communication point for producers and consumers. Kafka
clusters can consist of multiple brokers for scalability and fault tolerance.
ZooKeeper
While Kafka has been moving towards removing its dependency on Apache ZooKeeper
for metadata management, older versions still rely on ZooKeeper for cluster
coordination and management.
Retention
Kafka can retain data for a configurable period, allowing consumers to replay historical
data or enabling batch processing of data.
Streams and Connect
Kafka offers Kafka Streams for stream processing applications and Kafka Connect for
building connectors to integrate with external data sources and sinks.
5. Apache Kafka is widely used for various use cases, including real-time data streaming,
log aggregation, event sourcing, data integration, complex event processing (CEP),
change data capture (CDC), and more.
It provides strong durability guarantees and is known for its high throughput, low
latency, and scalability, making it a popular choice for organizations dealing with large
volumes of data and requiring real-time data processing and analysis.
Use Cases for Apache Kafka
We will uncover how Apache Kafka serves as the backbone for various use cases,
providing a reliable and scalable solution for handling data streams. Whether you are
looking to build a real-time data analytics platform, implement event-driven
architectures, or enable IoT communication, Kafka offers a robust foundation to
transform your data management strategies.
6. Real-time Data Streaming
Apache Kafka is the go-to solution when you require real-time data streaming at scale. It
excels in scenarios where large volumes of data must be ingested, processed, and
disseminated with minimal latency. Industries such as finance, e-commerce, and
telecommunications rely on Kafka to power applications that demand up-to-the-minute
information.
Log Aggregation
7. Kafka serves as a centralized repository for logs generated by diverse services and
applications. This aggregation simplifies log analysis, debugging, and troubleshooting,
making it a favorite choice in DevOps and system monitoring.
Event Sourcing
In event-driven architectures, Kafka shines by maintaining a complete and ordered
history of events. This historical context is invaluable in domains like finance,
healthcare, and e-commerce, where auditing, traceability, and compliance requirements
are stringent.
Data Integration
Kafka's versatility makes it an excellent choice for data integration across
heterogeneous systems, databases, and applications. It enables the seamless flow of
data in complex microservices architectures, enhancing interoperability and reducing
data silos.
Messaging
Kafka can be employed as a robust messaging system for real-time communication
between applications. This use case finds applications in chat applications,
notifications, and managing the deluge of data generated by IoT ecosystems.
Batch Data Processing
Kafka's durability and data retention capabilities make it well-suited for batch data
processing. This proves beneficial when you need to reprocess data, backfill historical
records, or maintain a complete data history.
8. Complex Event Processing (CEP)
Organizations dealing with high-volume, high-velocity data streams, such as financial
institutions and network monitoring, leverage Kafka for complex event processing. It
enables the detection of intricate patterns and anomalies in real time, aiding fraud
detection and situational awareness.
Change Data Capture (CDC)
Kafka's ability to capture and replicate changes made to databases in real-time
positions it as a vital component for building data warehouses, data lakes, and analytics
platforms. It simplifies the process of data synchronization and keeps analytical
systems up-to-date.
When Not to Use Apache Kafka
While Apache Kafka is a powerful and versatile distributed event streaming platform, it's
important to recognize that it may not always be the best fit for every data processing
scenario. Understanding the limitations and scenarios where Apache Kafka might not
be the optimal choice is crucial for making informed decisions when architecting your
data infrastructure.
In this section, we'll explore situations and use cases where Apache Kafka may not be
the most suitable solution, helping you determine when to consider alternative
technologies or approaches.
Simple Request-Response Communication
9. If your application predominantly relies on simple request-response communication and
doesn't involve real-time streaming or event-driven patterns, traditional RESTful APIs or
RPC mechanisms might be more straightforward and suitable.
Small-Scale Projects
For small-scale projects with limited data volume and velocity, setting up and managing
Kafka clusters could be overly complex and resource-intensive. Simpler data integration
tools or message queues may offer a more cost-effective solution.
High Latency Tolerance
If your application can tolerate higher latencies, other solutions may be easier to
implement and maintain. Kafka's primary strength lies in low-latency, real-time data
streaming, and may be over-engineered for use cases with more relaxed latency
requirements.
Limited Resources
Organizations lacking the necessary resources, whether human, hardware, or financial,
to manage and maintain Kafka clusters might consider managed Kafka services or
alternative solutions that require less overhead.
Monolithic Applications
If your application architecture remains predominantly monolithic and does not
embrace microservices or event-driven components, the benefits of Kafka's event
streaming may be limited, and simpler communication mechanisms may suffice.
10. Lack of Expertise
Implementing and maintaining Kafka effectively requires expertise. If your team lacks
experience with Kafka or event-driven architectures, consider investing in training or
consulting services to ensure successful adoption.
Companies using Apache Kafka
Thousands of companies including Fortune 100 use Kafka. It serves as a dependable
solution, empowering organizations to revamp their data strategies through event
streaming architecture.
● LinkedIn
● Twitter
● Netflix
● Adidas
● Cisco
● Paypal
Final Words
Apache Kafka is a versatile and powerful tool for managing real-time data streaming,
event-driven architectures, and complex data integration scenarios. However, it's crucial
to evaluate your specific use case, project scale, and available resources when
considering Kafka.
While it excels in many scenarios, alternative options might better suit your needs when
simplicity, resource constraints, or different communication patterns come into play.
Careful consideration of your project's requirements will help you determine whether
Apache Kafka is the right tool to propel your data-driven endeavors forward.