Apache Kafka is a distributed streaming platform. It provides a high-throughput distributed messaging system that can handle trillions of events daily. Many large companies use Kafka for application logging, metrics collection, and powering real-time analytics. The current version is 0.8.2 and upcoming versions will include a new consumer, security features, and support for transactions.
I Heart Log: Real-time Data and Apache KafkaJay Kreps
This presentation discusses how logs and stream-processing can form a backbone for data flow, ETL, and real-time data processing. It will describe the challenges and lessons learned as LinkedIn built out its real-time data subscription and processing infrastructure. It will also discuss the role of real-time processing and its relationship to offline processing frameworks such as MapReduce.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
I Heart Log: Real-time Data and Apache KafkaJay Kreps
This presentation discusses how logs and stream-processing can form a backbone for data flow, ETL, and real-time data processing. It will describe the challenges and lessons learned as LinkedIn built out its real-time data subscription and processing infrastructure. It will also discuss the role of real-time processing and its relationship to offline processing frameworks such as MapReduce.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
This is the first part of the presentation.
Here is the 2nd part of this presentation:-
http://www.slideshare.net/knoldus/introduction-to-apache-kafka-part-2
Real time Messages at Scale with Apache Kafka and CouchbaseWill Gardella
Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together.
This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
The session discusses on how companies are using Apache Kafka & also covers under the hood details like partitions, brokers, replication.
About apache kafka: Apache Kafka is a distributed a streaming platform, Apache Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and is able to process streams of events. Kafka provides reliable, millisecond responses to support both customer-facing applications and connecting downstream systems with real-time data.
Stream your Operational Data with Apache Spark & Kafka into Hadoop using Couc...Data Con LA
Abstract:-
Tracking user events as they happen can challenge anyone providing real time user interaction. It can demand both huge scale and a lot of processing to support dynamic adjustment to targeting products and services. As the operational data store Couchbase data services are capable of processing tens of millions of updates a day. Streaming through systems such as Apache Spark and Kafka into Hadoop, information about these key events can be turned into deeper knowledge. We will review Lambda architectures deployed at sites like PayPal, Live Person and LinkedIn that leverage a Couchbase Data Pipeline.
Bio:-
Justin Michaels. With over 20 years experience in deploying mission critical systems, Justin Michaels industry experience covers capacity planning, architecture and industry vertical experience. Justin brings his passion for architecting, implementing and improving Couchbase to the community as a Solution Architect. His expertise involves both conventional application platforms as well as distributed data management systems. He regularly engages with existing and new Couchbase customers in performance reviews, architecture planning and best practice guidance.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
This is the first part of the presentation.
Here is the 2nd part of this presentation:-
http://www.slideshare.net/knoldus/introduction-to-apache-kafka-part-2
Real time Messages at Scale with Apache Kafka and CouchbaseWill Gardella
Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together.
This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
The session discusses on how companies are using Apache Kafka & also covers under the hood details like partitions, brokers, replication.
About apache kafka: Apache Kafka is a distributed a streaming platform, Apache Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and is able to process streams of events. Kafka provides reliable, millisecond responses to support both customer-facing applications and connecting downstream systems with real-time data.
Stream your Operational Data with Apache Spark & Kafka into Hadoop using Couc...Data Con LA
Abstract:-
Tracking user events as they happen can challenge anyone providing real time user interaction. It can demand both huge scale and a lot of processing to support dynamic adjustment to targeting products and services. As the operational data store Couchbase data services are capable of processing tens of millions of updates a day. Streaming through systems such as Apache Spark and Kafka into Hadoop, information about these key events can be turned into deeper knowledge. We will review Lambda architectures deployed at sites like PayPal, Live Person and LinkedIn that leverage a Couchbase Data Pipeline.
Bio:-
Justin Michaels. With over 20 years experience in deploying mission critical systems, Justin Michaels industry experience covers capacity planning, architecture and industry vertical experience. Justin brings his passion for architecting, implementing and improving Couchbase to the community as a Solution Architect. His expertise involves both conventional application platforms as well as distributed data management systems. He regularly engages with existing and new Couchbase customers in performance reviews, architecture planning and best practice guidance.
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.
Developing Realtime Data Pipelines With Apache KafkaJoe Stein
Developing Realtime Data Pipelines With Apache Kafka. Apache Kafka is publish-subscribe messaging rethought as a distributed commit log. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers. Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact. Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
Logging. Everyone does it. Many don't know why they do it. It is often considered a boring chore. A chore that is done by habit rather than for a purpose. But it doesn't have to be! Learn how to build a powerful, scalable open source logging environment with LogStash.
Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.
Being Ready for Apache Kafka - Apache: Big Data Europe 2015Michael Noll
These are the slides of my Kafka talk at Apache: Big Data Europe in Budapest, Hungary. Enjoy! --Michael
Apache Kafka is a high-throughput distributed messaging system that has become a mission-critical infrastructure component for modern data platforms. Kafka is used across a wide range of industries by thousands of companies such as Twitter, Netflix, Cisco, PayPal, and many others.
After a brief introduction to Kafka this talk will provide an update on the growth and status of the Kafka project community. Rest of the talk will focus on walking the audience through what's required to put Kafka in production. We’ll give an overview of the current ecosystem of Kafka, including: client libraries for creating your own apps; operational tools; peripheral components required for running Kafka in production and for integration with other systems like Hadoop. We will cover the upcoming project roadmap, which adds key features to make Kafka even more convenient to use and more robust in production.
Real-time streaming and data pipelines with Apache KafkaJoe Stein
Get up and running quickly with Apache Kafka http://kafka.apache.org/
* Fast * A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients.
* Scalable * Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers
* Durable * Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact.
* Distributed by Design * Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
Get started with Developing Frameworks in Go on Apache MesosJoe Stein
Apache Mesos provides a platform for building distributed systems. Mesos is built using the same principles as the Linux kernel, only at a different level of abstraction. The Mesos kernel runs on every machine and provides applications (e.g., Hadoop, Spark, Kafka, Elastic Search) with API’s for resource management and scheduling across entire datacenter and cloud environments. How to use that platform and what to make of it becomes a complex task requiring not only understanding of where the system has been but also where it is going. Using schedulers like Marathon and Aurora help to get your applications scheduled and executing on Mesos. In many cases it makes sense to build a framework and integrate directly. This talk will breakdown what is involved in building a framework in Go, how to-do this with examples and why you would want to-do this. Frameworks are not only for generally available software applications (like Kafka, HDFS, Spark ,etc) but should also be used for custom internal R&D built software applications too.
Introducing Kafka Streams, the new stream processing library of Apache Kafka,...Michael Noll
Video recording: https://www.youtube.com/watch?v=o7zSLNiTZbA
Slides of my talk at Berlin Buzzwords in June 2016.
Abstract:
"In the past few years Apache Kafka has established itself as the world's most popular real-time, large-scale messaging system. It is used across a wide range of industries by thousands of companies such as Netflix, Cisco, PayPal, Twitter, and many others.
In this session I am introducing the audience to Kafka Streams, which is the latest addition to the Apache Kafka project. Kafka Streams is a stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a high-level DSL for writing stream processing applications. As such it is the most convenient yet scalable option to process and analyze data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Apache Storm and Spark Streaming, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka."
Apache Kafka 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
Data Pipeline with Kafka, This slide include
Kafka Introduction, Topic / Partitions, Produce / Consumer, Quick Start, Offset Monitoring, Example Code, Camus
OSSNA Building Modern Data Streaming AppsTimothy Spann
OSSNA
Building Modern Data Streaming Apps
https://ossna2023.sched.com/event/1Jt05/virtual-building-modern-data-streaming-apps-with-open-source-timothy-spann-streamnative
Timothy Spann
Cloudera
Principal Developer Advocate
Data in Motion
In my session, I will show you some best practices I have discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In my modern approach, we utilize several open-source frameworks to maximize all the best features. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there, we build streaming ETL with Apache Spark and enhance events with Pulsar Functions for ML and enrichment. We make continuous queries against our topics with Flink SQL. We will stream data into various open-source data stores, including Apache Iceberg, Apache Pinot, and others. We use the best streaming tools for the current applications with the open source stack - FLiPN. https://www.flipn.app/ Updates: This will be in-person with live coding based on feedback from the crowd. This will also include new data stores, new sources, and data relevant to and from the Vancouver area. This will also include updates to the platforms and inclusion of Apache Iceberg, Apache Pinot and some other new tech.
https://github.com/tspannhw/SpeakerProfile Tim Spann is a Principal Developer Advocate for Cloudera. He works with Apache Kafka, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a 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 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.
Timothy J Spann
Cloudera
Principal Developer Advocate
Hightstown, NJ
Websitehttps://datainmotion.dev/
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.
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.
Building Streaming Data Applications Using Apache KafkaSlim Baltagi
Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing.
In this talk you will learn more about:
1. A quick introduction to Kafka Core, Kafka Connect and Kafka Streams: What is and why?
2. Code and step-by-step instructions to build an end-to-end streaming data application using Apache Kafka
Building streaming data applications using Kafka*[Connect + Core + Streams] b...Data Con LA
Abstract:- Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing. In this talk you will learn more about: A quick introduction to Kafka Core, Kafka Connect and Kafka Streams through code examples, key concepts and key features. A reference architecture for building such Kafka-based streaming data applications. A demo of an end-to-end Kafka-based streaming data application.
Devfest uk & ireland using apache nifi with apache pulsar for fast data on-r...Timothy Spann
Devfest uk & ireland using apache nifi with apache pulsar for fast data on-ramp 2022
As the Pulsar communities grows, more and more connectors will be added. To enhance the availability of sources and sinks and to make use of the greater Apache Streaming community, joining forces between Apache NiFi and Apache Pulsar is a perfect fit. Apache NiFi also adds the benefits of ELT, ETL, data crunching, transformation, validation and batch data processing. Once data is ready to be an event, NiFi can launch it into Pulsar at light speed.
I will walk through how to get started, some use cases and demos and answer questions.
https://www.devfest-uki.com/schedule
https://linktr.ee/tspannhw
A brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will introduce some of the newer components of Kafka that will help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.
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.
Today, many companies are faced with a huge quantity of data and a wide variety of tools with which to process it. This potentially allows for great opportunities to satisfy customers’ needs and bring user experience to the next level. However, in order to achieve this and provide a competitive solution, sophisticated and complex data processing is needed. Such processing can rarely be done with one tool or framework — a number of tools are often involved, each having prowess in a particular field of the processing pipeline.
In this session, we will see the latest endeavors of Apache Ignite to integrate with other big data platforms and provide its in-memory computing strengths for data processing pipelines. In particular we will have a closer look at how it can be integrated and used with Apache Kafka and/or Flume, and outline several use scenarios.
IoT Architectures for Apache Kafka and Event Streaming - Industry 4.0, Digita...Kai Wähner
The Internet of Things (IoT) is getting more and more traction as valuable use cases come to light. Whether you are in Healthcare, Telecommunications, Manufacturing, Banking or Retail to name a few industries, there is one key challenge and that's the integration of backend IoT data logs and applications, business services and cloud services to process the data in real time and at scale.
In this talk, we will be sharing how Kafka has become the leading technology used throughout the business to provide Real Time Event Streaming. Explore real life use cases of Kafka Connect, Kafka Streams and KSQL independent of the data deployment be it on a private or public Cloud, On Premise or at the Edge.
Audi - Connected car infrastructure
Robert Bosch Power Tools - Track and Trace of devices and people at construction areas
Deutsche Bahn - Customer 360 for train timetable updates
E.ON - IoT Streaming Platform to integrate and build smart home, smart building and smart grid infrastructures
Spark (Structured) Streaming vs. Kafka Streams - two stream processing platfo...Guido Schmutz
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams.
Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala.
Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application.
In this presentation Guido Schmutz talks about Apache Kafka, Kafka Core, Kafka Connect, Kafka Streams, Kafka and "Big Data"/"Fast Data Ecosystems, Confluent Data Platform and Kafka in Architecture.
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.
Similar to Current and Future of Apache Kafka (20)
SMACK Stack 1.0 has been Spark, Mesos, Akka, Cassandra and Kafka working into different cohesive systems delivering different solutions for different use cases. Haven't heard about it before? Oh man! Where have you been? https://www.google.com/search?q=smack+stack+1.0
SMACK Stack 1.1 we go a step further Streaming, Mesos, Analytics, Cassandra and Kafka and Joe Stein will walk through in detail some of the different viable options for Streaming and Analytics with Mesos, Kafka and Cassandra.
Real-Time Log Analysis with Apache Mesos, Kafka and CassandraJoe Stein
Slides for our solution we developed for using Mesos, Docker, Kafka, Spark, Cassandra and Solr (DataStax Enterprise Edition) all developed in Go for doing realtime log analysis at scale. Many organizations either need or want log analysis in real time where you can see within a second what is happening within your entire infrastructure. Today, with the hardware available and software systems we have in place, you can develop, build and use as a service these solutions.
Developing Real-Time Data Pipelines with Apache KafkaJoe Stein
Developing Real-Time Data Pipelines with Apache Kafka http://kafka.apache.org/ is an introduction for developers about why and how to use Apache Kafka. Apache Kafka is a publish-subscribe messaging system rethought of as a distributed commit log. Kafka is designed to allow a single cluster to serve as the central data backbone. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of coordinated consumers. Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages. For the Spring user, Spring Integration Kafka and Spring XD provide integration with Apache Kafka.
Using schedulers like Marathon and Aurora help to get your applications scheduled and executing on Mesos. In many cases it makes sense to build a framework and integrate directly. This talk will breakdown what is involved in building a framework, how to-do this with examples and why you would want to-do this. Frameworks are not only for generally available software applications (like Kafka, HDFS, Spark ,etc) but can also be used for custom internal R&D built software applications too.
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache AccumuloJoe Stein
In this talk we will walk through how Apache Kafka and Apache Accumulo can be used together to orchestrate a de-coupled, real-time distributed and reactive request/response system at massive scale. Multiple data pipelines can perform complex operations for each message in parallel at high volumes with low latencies. The final result will be inline with the initiating call. The architecture gains are immense. They allow for the requesting system to receive a response without the need for direct integration with the data pipeline(s) that messages must go through. By utilizing Apache Kafka and Apache Accumulo, these gains sustain at scale and allow for complex operations of different messages to be applied to each response in real-time.
Detail behind the Apache Cassandra 2.0 release and what is new in it including Lightweight Transactions (compare and swap) Eager retries, Improved compaction, Triggers (experimental) and more!
• CQL cursors
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
2. Joe Stein
• Developer, Architect & Technologist
• Founder & Principal Consultant => Big Data Open Source Security LLC - http://stealth.ly
Big Data Open Source Security LLC provides professional services and product solutions for the collection,
storage, transfer, real-time analytics, batch processing and reporting for complex data streams, data sets and
distributed systems. BDOSS is all about the "glue" and helping companies to not only figure out what Big Data
Infrastructure Components to use but also how to change their existing (or build new) systems to work with
them.
• Apache Kafka Committer & PMC member
• Blog & Podcast - http://allthingshadoop.com
• Twitter @allthingshadoop
3. Apache Kafka
• Apache Kafka
o http://kafka.apache.org
• Apache Kafka Source Code
o https://github.com/apache/kafka
• Documentation
o http://kafka.apache.org/documentation.html
• FAQ
o https://cwiki.apache.org/confluence/display/KAFKA/FAQ
• Wiki
o https://cwiki.apache.org/confluence/display/KAFKA/Index
20. Recap
• Producers - ** push **
o Batching
o Compression
o Sync (Ack), Async (auto batch)
o Replication for durability and fault tolerance
o Sequential writes, guaranteed ordering within each partition
• Consumers - ** pull **
o No state held by broker
o Consumers control reading from the stream
• Zero Copy for producers and consumers to and from the broker
http://kafka.apache.org/documentation.html#maximizingefficiency
• Message stay on disk when consumed, deletes on TTL or compaction
https://kafka.apache.org/documentation.html#compaction
21. Client Libraries
● JVM Client supported by the Apache Project https://kafka.apache.org/documentation.html#api
● Community Clients https://cwiki.apache.org/confluence/display/KAFKA/Clients
• Python - Pure Python implementation with full protocol support. Consumer and Producer
implementations included, GZIP and Snappy compression supported.
• C - High performance C library with full protocol support
• C++ - Native C++ library with protocol support for Metadata, Produce, Fetch, and Offset.
• Go (aka golang) Pure Go implementation with full protocol support. Consumer and Producer
implementations included, GZIP and Snappy compression supported.
• Ruby - Pure Ruby, Consumer and Producer implementations included, GZIP and Snappy
compression supported. Ruby 1.9.3 and up (CI runs MRI 2.
• Clojure - Clojure DSL for the Kafka API
• JavaScript (NodeJS) - NodeJS client in a pure JavaScript implementation
• stdin & stdout
Wire Protocol Developers Guide
https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol
22. ● LinkedIn - Apache Kafka is used at LinkedIn for activity
stream data and operational metrics. This powers various
products like LinkedIn Newsfeed, LinkedIn Today in addition
to our offline analytics systems like Hadoop.
● Twitter - As part of their Storm stream processing
infrastructure, e.g. this.
● Netflix - Real-time monitoring and event-processing pipeline.
● Square - We use Kafka as a bus to move all systems events
through our various datacenters. This includes metrics, logs,
custom events etc. On the consumer side, we output into
Splunk, Graphite, Esper-like real-time alerting.
● Spotify - Kafka is used at Spotify as part of their log delivery
system.
● Pinterest - Kafka is used with Secor as part of their log
collection pipeline.
● Uber
● Tumblr - See this
● Box - At Box, Kafka is used for the production analytics
pipeline & real time monitoring infrastructure. We are
planning to use Kafka for some of the new products &
● Mozilla - Kafka will soon be replacing part of our current
production system to collect performance and usage data
from the end-users browser for projects like Telemetry, Test
Pilot, etc. Downstream consumers usually persist to either
HDFS or HBase.
● Tagged - Apache Kafka drives our new pub sub system
which delivers real-time events for users in our latest game -
Deckadence. It will soon be used in a host of new use cases
including group chat and back end stats and log collection.
● Foursquare - Kafka powers online to online messaging, and
online to offline messaging at Foursquare. We integrate with
monitoring, production systems, and our offline infrastructure,
including hadoop.
● StumbleUpon - Data collection platform for analytics.
● Coursera - At Coursera, Kafka powers education at scale,
serving as the data pipeline for realtime learning
analytics/dashboards.
● Shopify - Access logs, A/B testing events, domain events ("a
checkout happened", etc.), metrics, delivery to HDFS, and
customer reporting. We are now focusing on consumers:
analytics, support tools, and fraud analysis.
23. ● Mate1.com Inc. - Apache kafka is used at Mate1 as our main
event bus that powers our news and activity feeds,
automated review systems, and will soon power real time
notifications and log distribution.
● Boundary - Apache Kafka aggregates high-flow message
streams into a unified distributed pubsub service, brokering
the data for other internal systems as part of Boundary's real-time
network analytics infrastructure.
● Ancestry.com - Kafka is used as the event log processing
pipeline for delivering better personalized product and
service to our customers.
● DataSift - Apache Kafka is used at DataSift as a collector of
monitoring events and to track user's consumption of data
streams in real
time.http://highscalability.com/blog/2011/11/29/datasift-architecture-
realtime-datamining-at-120000-tweets-p.html
● Spongecell - We use Kafka to run our entire analytics and
monitoring pipeline driving both real-time and ETL
applications for our customers.
● Wooga - We use Kafka to aggregate and process tracking
data from all our facebook games (which are hosted at
● AddThis - Apache Kafka is used at AddThis to collect events
generated by our data network and broker that data to our
analytics clusters and real-time web analytics platform.
● Urban Airship - At Urban Airship we use Kafka to buffer
incoming data points from mobile devices for processing by
our analytics infrastructure.
● Metamarkets - We use Kafka to ingest real-time event data,
stream it to Storm and Hadoop, and then serve it from our
Druid cluster to feed our interactive analytics dashboards.
We've also built connectors for directly ingesting events from
Kafka into Druid.
● Simple - We use Kafka at Simple for log aggregation and to
power our analytics infrastructure.
● Gnip - Kafka is used in their twitter ingestion and processing
pipeline.
● Loggly - Loggly is the world's most popular cloud-based log
management. Our cloud-based log management service
helps DevOps and technical teams make sense of the the
massive quantity of logs. Kafka is used as part of our log
collection and processing infrastructure.
24. ● RichRelevance - Real-time tracking event pipeline.
● SocialTwist - We use Kafka internally as part of our reliable
email queueing system.
● Countandra - We use a hierarchical distributed counting
engine, uses Kafka as a primary speedy interface as well as
routing events for cascading counting
● FlyHajj.com - We use Kafka to collect all metrics and events
generated by the users of the website.
● uSwitch - See this blog.
● InfoChimps - Kafka is part of the InfoChimps real-time data
platform.
● Visual Revenue - We use Kafka as a distributed queue in
front of our web traffic stream processing infrastructure
(Storm).
● Oolya - Kafka is used as the primary high speed message
queue to power Storm and our real-time analytics/event
ingestion pipelines.
● Datadog - Kafka brokers data to most systems in our metrics
and events ingestion pipeline. Different modules contribute
and consume data from it, for streaming CEP (homegrown),
● VisualDNA We use Kafka 1. as an infrastructure that helps us
bring continuously the tracking events from various
datacenters into our central hadoop cluster for offline
processing, 2. as a propagation path for data integration, 3.
as a real-time platform for future inference and
recommendation engines
● Sematext - in SPM (performance monitoring + alerting),
Kafka is used for metrics collection and feeds SPM's in-memory
data aggregation (OLAP cube creation) as well as
our CEP/Alerts servers (see also: SPM for Kafka
performance monitoring). In SA (search analytics) Kafka is
used in search and click stream collection before being
aggregated and persisted. In Logsene (log analytics) Kafka is
used to pass logs and other events from front-end receivers
to the persistent backend.
● Wize Commerce - At Wize Commerce (previously, NexTag),
Kafka is used as a distributed queue in front of Storm based
processing for search index generation. We plan to also use
it for collecting user generated data on our web tier, landing
the data into various data sinks like Hadoop, HBase, etc.
● Quixey - At Quixey, The Search Engine for Apps, Kafka is an
integral part of our eventing, logging and messaging
25. ● LinkSmart - Kafka is used at LinkSmart as an event stream
feeding Hadoop and custom real time systems.
● LucidWorks Big Data - We use Kafka for syncing LucidWorks
Search (Solr) with incoming data from Hadoop and also for
sending LucidWorks Search logs back to Hadoop for analysis.
● Cloud Physics - Kafka is powering our high-flow event
pipeline that aggregates over 1.2 billion metric series from
1000+ data centers for near-to-real time data center
operational analytics and modeling
● Graylog2 - Graylog2 is a free and open source log
management and data analysis system. It's using Kafka as
default transport for Graylog2 Radio. The use case is
described here.
● Yieldbot - Yieldbot uses kafka for real-time events, camus for
batch loading, and mirrormakers for x-region replication.
● LivePerson - Using Kafka as the main data bus for all real
time events.
● Retention Science - Click stream ingestion and processing.
● Strava - Powers our analytics pipeline, activity feeds denorm
and several other production services.
● Outbrain - We use Kafka in production for real time log collection and
processing, and for cross-DC cache propagation.
● SwiftKey - We use Apache Kafka for analytics event processing.
● Yeller - Yeller uses Kafka to process large streams of incoming exception
data for it's customers. Rate limiting, throttling and batching are all built on
top of Kafka.
● Emerging Threats - Emerging threats uses Kafka in our event pipeline to
process billions of malware events for search indices, alerting systems, etc.
● Hotels.com - Hotels.com uses Kafka as pipeline to collect real time events
from multiple sources and for sending data to HDFS.
● Helprace - Kafka is used as a distributed high speed message queue in our
help desk software as well as our real-time event data aggregation and
analytics.
● Exponential is using Kafka in production to power the events ingestion
pipeline for real time analytics and log feed consumption.
● Livefyre - uses Kafka for the real time notifications, analytics pipeline and as
the primary mechanism for general pub/sub.
● Exoscale - uses Kafka in production.
● Cityzen Data - uses Kafka as well, we provide a platform for collecting,
storing and analyzing machine data.
● Criteo - use Kafka in production for over a year for stream processing and
log transfer (over 2M messages/s and growing)
● The Wikimedia Foundation - uses Kafka as a log transport for analytics data
26. Where are we going? (0.8.2)
● 0.8.2-beta is released https://kafka.apache.org/downloads.html
○ Better consistency vs. availability (min.isr per topic)
○ LZ4 Compression
○ Offset Storage away from Zookeeper
○ Better Topic Management
○ New Java Producer
○ Over 100 bug fixes
○ More!
https://archive.apache.org/dist/kafka/0.8.2-beta/RELEASE_NOTES.html
30. Where are we going? (0.9.0)
● New Consumer
https://cwiki.apache.org/confluence/display/KAFKA/Kafka+0.9+Consumer+Rewrite+Design
● Continued operational improvements
o Command Line Interface / Admin API
o https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Command+Line+and+Relate
d+Improvements
● Security https://cwiki.apache.org/confluence/display/KAFKA/Security
o Authentication
TLS/SSL
Kerberos
o Authorization
Plugable
● Idempotence, Transactions
https://cwiki.apache.org/confluence/display/KAFKA/Transactional+Messaging+in+Kafka
31. Really Quick Start (Scala)
1) Install Vagrant http://www.vagrantup.com/
2) Install Virtual Box https://www.virtualbox.org/
3) git clone https://github.com/stealthly/scala-kafka
4) cd scala-kafka
5) vagrant up
Zookeeper will be running on 192.168.86.5
BrokerOne will be running on 192.168.86.10
All the tests in ./src/test/scala/* should pass (which is also /vagrant/src/test/scala/* in the vm)
6) ./gradlew test