This document provides an introduction and overview of key concepts for Apache Kafka. It discusses Kafka's architecture as a distributed streaming platform consisting of producers, brokers, consumers and topics partitioned into logs. It covers Kafka's high throughput and low latency capabilities through batching and zero-copy I/O. The document also outlines Kafka's guarantees around message ordering, delivery semantics, and how consumer groups work to partition data streams across consumer instances.
Apache Kafka is a distributed messaging system that provides fast, highly scalable messaging through a publish-subscribe model. It was built at LinkedIn as a central hub for messaging between systems and focuses on scalability and fault tolerance. Kafka uses a distributed commit log architecture with topics that are partitioned for scalability and parallelism. It provides high throughput and fault tolerance through replication and an in-sync replica set.
The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Multi-Cluster and Failover for Apache Kafka - Kafka Summit SF 17Gwen (Chen) Shapira
This document discusses disaster recovery strategies for Apache Kafka clusters running across multiple data centers. It outlines several failure scenarios like an entire data center being demolished and recommends solutions like running a single Kafka cluster across multiple near-by data centers. It then describes a "stretch cluster" approach using 3 data centers with replication between them to provide high availability. The document also discusses active-active replication between two data center clusters and challenges around consumer offsets not being identical across data centers during a failover. It recommends approaches like tracking timestamps and failing over consumers based on time.
Building Stream Infrastructure across Multiple Data Centers with Apache KafkaGuozhang Wang
To manage the ever-increasing volume and velocity of data within your company, you have successfully made the transition from single machines and one-off solutions to large distributed stream infrastructures in your data center, powered by Apache Kafka. But what if one data center is not enough? I will describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence, and provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication, and mirroring as well as disaster scenarios and failure handling.
This document provides guidance on scaling Apache Kafka clusters and tuning performance. It discusses expanding Kafka clusters horizontally across inexpensive servers for increased throughput and CPU utilization. Key aspects that impact performance like disk layout, OS tuning, Java settings, broker and topic monitoring, client tuning, and anticipating problems are covered. Application performance can be improved through configuration of batch size, compression, and request handling, while consumer performance relies on partitioning, fetch settings, and avoiding perpetual rebalances.
Building Event-Driven Systems with Apache KafkaBrian Ritchie
Event-driven systems provide simplified integration, easy notifications, inherent scalability and improved fault tolerance. In this session we'll cover the basics of building event driven systems and then dive into utilizing Apache Kafka for the infrastructure. Kafka is a fast, scalable, fault-taulerant publish/subscribe messaging system developed by LinkedIn. We will cover the architecture of Kafka and demonstrate code that utilizes this infrastructure including C#, Spark, ELK and more.
Sample code: https://github.com/dotnetpowered/StreamProcessingSample
Apache Kafka is a distributed messaging system that provides fast, highly scalable messaging through a publish-subscribe model. It was built at LinkedIn as a central hub for messaging between systems and focuses on scalability and fault tolerance. Kafka uses a distributed commit log architecture with topics that are partitioned for scalability and parallelism. It provides high throughput and fault tolerance through replication and an in-sync replica set.
The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Multi-Cluster and Failover for Apache Kafka - Kafka Summit SF 17Gwen (Chen) Shapira
This document discusses disaster recovery strategies for Apache Kafka clusters running across multiple data centers. It outlines several failure scenarios like an entire data center being demolished and recommends solutions like running a single Kafka cluster across multiple near-by data centers. It then describes a "stretch cluster" approach using 3 data centers with replication between them to provide high availability. The document also discusses active-active replication between two data center clusters and challenges around consumer offsets not being identical across data centers during a failover. It recommends approaches like tracking timestamps and failing over consumers based on time.
Building Stream Infrastructure across Multiple Data Centers with Apache KafkaGuozhang Wang
To manage the ever-increasing volume and velocity of data within your company, you have successfully made the transition from single machines and one-off solutions to large distributed stream infrastructures in your data center, powered by Apache Kafka. But what if one data center is not enough? I will describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence, and provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication, and mirroring as well as disaster scenarios and failure handling.
This document provides guidance on scaling Apache Kafka clusters and tuning performance. It discusses expanding Kafka clusters horizontally across inexpensive servers for increased throughput and CPU utilization. Key aspects that impact performance like disk layout, OS tuning, Java settings, broker and topic monitoring, client tuning, and anticipating problems are covered. Application performance can be improved through configuration of batch size, compression, and request handling, while consumer performance relies on partitioning, fetch settings, and avoiding perpetual rebalances.
Building Event-Driven Systems with Apache KafkaBrian Ritchie
Event-driven systems provide simplified integration, easy notifications, inherent scalability and improved fault tolerance. In this session we'll cover the basics of building event driven systems and then dive into utilizing Apache Kafka for the infrastructure. Kafka is a fast, scalable, fault-taulerant publish/subscribe messaging system developed by LinkedIn. We will cover the architecture of Kafka and demonstrate code that utilizes this infrastructure including C#, Spark, ELK and more.
Sample code: https://github.com/dotnetpowered/StreamProcessingSample
This document discusses strategies for building large-scale stream infrastructures across multiple data centers using Apache Kafka. It outlines common multi-data center patterns like stretched clusters, active/passive clusters, and active/active clusters. It also covers challenges like maintaining ordering and consumer offsets across data centers and potential solutions.
Protecting your data at rest with Apache Kafka by Confluent and Vormetricconfluent
This document discusses securing Apache Kafka deployments with Vormetric and Confluent Platform. It begins with an introduction to Apache Kafka and Confluent Platform. It then provides an overview of Vormetric's policy-driven security solution and how it can be used to encrypt Kafka data at rest. The document outlines the typical Confluent Platform deployment architecture and various security considerations, such as authentication, authorization, and data encryption. Finally, it provides steps for implementing secure deployments using SSL, Kerberos, and Vormetric encryption policies.
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.
This document discusses security features in Apache Kafka including SSL, SASL authentication using Kerberos or plaintext, and authorization controls. It provides an overview of how SSL and SASL authentication work in Kafka as well as how the Kafka authorizer controls access at a fine-grained level through ACLs defined on topics, operations, users and hosts. It also briefly mentions securing Zookeeper which stores Kafka metadata and ACLs.
How Apache Kafka is transforming Hadoop, Spark and StormEdureka!
This document provides an overview of Apache Kafka and how it is transforming Hadoop, Spark, and Storm. It begins with explaining why Kafka is needed, then defines what Kafka is and describes its architecture. Key components of Kafka like topics, producers, consumers and brokers are explained. The document also shows how Kafka can be used with Hadoop, Spark, and Storm for stream processing. It lists some companies that use Kafka and concludes by advertising an Edureka course on Apache Kafka.
Kafka security includes SSL for wire encryption, SASL (Kerberos) for authentication, and authorization controls. SSL uses certificates for encryption during network communication. SASL performs authentication using Kerberos credentials. Authorization is provided by pluggable authorizers that define access control lists controlling permissions for principals to perform operations on resources and hosts. Securing Zookeeper with ACLs and SASL is also important as Kafka stores metadata there.
IBM Message Hub service in Bluemix - Apache Kafka in a public cloudAndrew Schofield
This talk was presented at the Kafka Meetup London meeting on 20 January 2016. You can find more information about Message Hub here: http://ibm.biz/message-hub-bluemix-catalog
Rajasekar Elango works for the Monitoring and Management Team at Salesforce.com, which builds tools to monitor the health and performance of Salesforce infrastructure. They implemented Apache Kafka to securely collect and aggregate monitoring data from application servers across multiple datacenters. The secure Kafka implementation uses SSL/TLS mutual authentication between brokers and producers/consumers to encrypt traffic and authenticate clients across datacenters.
Event Hub and Kafka are messaging platforms for ingesting streaming events. Both are designed for events and support at least once messaging with partitioning and ordering within partitions. The key differences are that Event Hub is a fully managed PaaS on Azure while Kafka is an IaaS platform, Event Hub supports HTTP/REST and AMQP protocols while Kafka uses HTTP/REST, and Event Hub provides cross-region replication and throttling that Kafka does not.
Building Machine Learning inference pipelines at scale | AWS Summit Tel Aviv ...AWS Summits
Real-life Machine Learning (ML) workloads typically require more than training and predicting: data often needs to be pre-processed and post-processed, sometimes in multiple steps. Thus, developers and data scientists have to train and deploy not just a single algorithm, but a sequence of algorithms that will collaborate in delivering predictions from raw data. In this session, we’ll first show you how to use Apache Spark MLlib to build ML pipelines, and we’ll discuss scaling options when datasets grow huge. We’ll then show how to how implement inference pipelines on Amazon SageMaker, using Apache Spark, Scikit-learn, as well as ML algorithms implemented by Amazon.
Decisions behind hypervisor selection in CloudStack 4.3Tim Mackey
As presented at the 2014 CloudStack Collaboration Conference in Denver (CCCNA14), this deck covers the matrix of functions and features within each supported hypervisor in CloudStack 4.3. This deck forms an excellent reference document for those seeking to provide multi-hypervisor support within their Apache CloudStack based cloud, and for those seeking to determine which feature elements are supported by a given hypervisor.
(Stephane Maarek, DataCumulus) Kafka Summit SF 2018
Security in Kafka is a cornerstone of true enterprise production-ready deployment: It enables companies to control access to the cluster and limit risks in data corruption and unwanted operations. Understanding how to use security in Kafka and exploiting its capabilities can be complex, especially as the documentation that is available is aimed at people with substantial existing knowledge on the matter.
This talk will be delivered in a “hero journey” fashion, tracing the experience of an engineer with basic understanding of Kafka who is tasked with securing a Kafka cluster. Along the way, I will illustrate the benefits and implications of various mechanisms and provide some real-world tips on how users can simplify security management.
Attendees of this talk will learn about aspects of security in Kafka, including:
-Encryption: What is SSL, what problems it solves and how Kafka leverages it. We’ll discuss encryption in flight vs. encryption at rest.
-Authentication: Without authentication, anyone would be able to write to any topic in a Kafka cluster, do anything and remain anonymous. We’ll explore the available authentication mechanisms and their suitability for different types of deployment, including mutual SSL authentication, SASL/GSSAPI, SASL/SCRAM and SASL/PLAIN.
-Authorization: How ACLs work in Kafka, ZooKeeper security (risks and mitigations) and how to manage ACLs at scale
Deploying Apache CloudStack from API to UIJoe Brockmeier
For most organizations with a large computing footprint, it's not a matter of if you'll need a private cloud - it's when, and what kind. One of the most mature and widely deployed options is Apache CloudStack, a robust, turnkey cloud that includes everything you need to set up a private, public, or hybrid cloud. We'll cover Apache CloudStack from API to UI, and a little of everything in between.
This presentation is the introduction to the monthly CloudStack.org demonstration. The presentation details the latest features in the CloudStack open source project as well as project news. To attend a future presentation, with live demo and Q&A visit:
http://www.slideshare.net/cloudstack/introduction-to-cloudstack-12590733
CloudStack is an open source cloud computing platform that provides infrastructure as a service. It supports various hypervisors (KVM, Xen, VMware), has APIs for self-service provisioning, measures resource usage, and allows for rapid elasticity. CloudStack can be deployed as public, private or hybrid clouds and manages networks, storage, security and high availability of virtual machines.
Big Data means big hardware, and the less of it we can use to do the job properly, the better the bottom line. Apache Kafka makes up the core of our data pipelines at many organizations, including LinkedIn, and we are on a perpetual quest to squeeze as much as we can out of our systems, from Zookeeper, to the brokers, to the various client applications. This means we need to know how well the system is running, and only then can we start turning the knobs to optimize it. In this talk, we will explore how best to monitor Kafka and its clients to assure they are working well. Then we will dive into how to get the best performance from Kafka, including how to pick hardware and the effect of a variety of configurations in both the broker and clients. We’ll also talk about setting up Kafka for no data loss.
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.
Presented at SF Big Analytics Meetup
Online event processing applications often require the ability to ingest, store, dispatch and process events. Until now, supporting all of these needs has required different systems for each task -- stream processing engines, messaging queuing middleware, and pub/sub messaging systems. This has led to the unnecessary complexity for the development of such applications and operations leading to increased barrier to adoption in the enterprises. In this talk, Karthik will outline the need to unify these capabilities in a single system and make it easy to develop and operate at scale. Karthik will delve into how Apache Pulsar was designed to address this need with an elegant architecture. Apache Pulsar is a next generation distributed pub-sub system that was originally developed and deployed at Yahoo and running in production in more than 100+ companies. Karthik will explain how the architecture and design of Pulsar provides the flexibility to support developers and applications needing any combination of queuing, messaging, streaming and lightweight compute for events. Furthermore, he will provide real life use cases how Apache Pulsar is used for event processing ranging from data processing tasks to web processing applications.
Whether you are developing a greenfield data project or migrating a legacy system,
there are many critical design decisions to be made. Often, it is advantageous to not only
consider immediate requirements, but also the future requirements and technologies you may
want to support. Your project may start out supporting batch analytics with the vision of adding
realtime support. Or your data pipeline may feed data to one technology today, but tomorrow
an entirely new system needs to be integrated. Apache Kafka can help decouple these
decisions and provide a flexible core to your data architecture. This talk will show how building
Kafka into your pipeline can provide the flexibility to experiment, evolve and grow. It will also
cover a brief overview of Kafka, its architecture, and terminology.
This document discusses tuning Kafka for performance. It covers optimizing Zookeeper configurations like using SSDs; using RAID or JBOD for Kafka broker disks with testing showing XFS performs best; scaling Kafka clusters by considering disk capacity, network capacity, and partition counts; configuring topics for retention settings and partition balancing; and tuning Mirror Maker for network locality and producer/consumer settings.
This document discusses strategies for building large-scale stream infrastructures across multiple data centers using Apache Kafka. It outlines common multi-data center patterns like stretched clusters, active/passive clusters, and active/active clusters. It also covers challenges like maintaining ordering and consumer offsets across data centers and potential solutions.
Protecting your data at rest with Apache Kafka by Confluent and Vormetricconfluent
This document discusses securing Apache Kafka deployments with Vormetric and Confluent Platform. It begins with an introduction to Apache Kafka and Confluent Platform. It then provides an overview of Vormetric's policy-driven security solution and how it can be used to encrypt Kafka data at rest. The document outlines the typical Confluent Platform deployment architecture and various security considerations, such as authentication, authorization, and data encryption. Finally, it provides steps for implementing secure deployments using SSL, Kerberos, and Vormetric encryption policies.
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.
This document discusses security features in Apache Kafka including SSL, SASL authentication using Kerberos or plaintext, and authorization controls. It provides an overview of how SSL and SASL authentication work in Kafka as well as how the Kafka authorizer controls access at a fine-grained level through ACLs defined on topics, operations, users and hosts. It also briefly mentions securing Zookeeper which stores Kafka metadata and ACLs.
How Apache Kafka is transforming Hadoop, Spark and StormEdureka!
This document provides an overview of Apache Kafka and how it is transforming Hadoop, Spark, and Storm. It begins with explaining why Kafka is needed, then defines what Kafka is and describes its architecture. Key components of Kafka like topics, producers, consumers and brokers are explained. The document also shows how Kafka can be used with Hadoop, Spark, and Storm for stream processing. It lists some companies that use Kafka and concludes by advertising an Edureka course on Apache Kafka.
Kafka security includes SSL for wire encryption, SASL (Kerberos) for authentication, and authorization controls. SSL uses certificates for encryption during network communication. SASL performs authentication using Kerberos credentials. Authorization is provided by pluggable authorizers that define access control lists controlling permissions for principals to perform operations on resources and hosts. Securing Zookeeper with ACLs and SASL is also important as Kafka stores metadata there.
IBM Message Hub service in Bluemix - Apache Kafka in a public cloudAndrew Schofield
This talk was presented at the Kafka Meetup London meeting on 20 January 2016. You can find more information about Message Hub here: http://ibm.biz/message-hub-bluemix-catalog
Rajasekar Elango works for the Monitoring and Management Team at Salesforce.com, which builds tools to monitor the health and performance of Salesforce infrastructure. They implemented Apache Kafka to securely collect and aggregate monitoring data from application servers across multiple datacenters. The secure Kafka implementation uses SSL/TLS mutual authentication between brokers and producers/consumers to encrypt traffic and authenticate clients across datacenters.
Event Hub and Kafka are messaging platforms for ingesting streaming events. Both are designed for events and support at least once messaging with partitioning and ordering within partitions. The key differences are that Event Hub is a fully managed PaaS on Azure while Kafka is an IaaS platform, Event Hub supports HTTP/REST and AMQP protocols while Kafka uses HTTP/REST, and Event Hub provides cross-region replication and throttling that Kafka does not.
Building Machine Learning inference pipelines at scale | AWS Summit Tel Aviv ...AWS Summits
Real-life Machine Learning (ML) workloads typically require more than training and predicting: data often needs to be pre-processed and post-processed, sometimes in multiple steps. Thus, developers and data scientists have to train and deploy not just a single algorithm, but a sequence of algorithms that will collaborate in delivering predictions from raw data. In this session, we’ll first show you how to use Apache Spark MLlib to build ML pipelines, and we’ll discuss scaling options when datasets grow huge. We’ll then show how to how implement inference pipelines on Amazon SageMaker, using Apache Spark, Scikit-learn, as well as ML algorithms implemented by Amazon.
Decisions behind hypervisor selection in CloudStack 4.3Tim Mackey
As presented at the 2014 CloudStack Collaboration Conference in Denver (CCCNA14), this deck covers the matrix of functions and features within each supported hypervisor in CloudStack 4.3. This deck forms an excellent reference document for those seeking to provide multi-hypervisor support within their Apache CloudStack based cloud, and for those seeking to determine which feature elements are supported by a given hypervisor.
(Stephane Maarek, DataCumulus) Kafka Summit SF 2018
Security in Kafka is a cornerstone of true enterprise production-ready deployment: It enables companies to control access to the cluster and limit risks in data corruption and unwanted operations. Understanding how to use security in Kafka and exploiting its capabilities can be complex, especially as the documentation that is available is aimed at people with substantial existing knowledge on the matter.
This talk will be delivered in a “hero journey” fashion, tracing the experience of an engineer with basic understanding of Kafka who is tasked with securing a Kafka cluster. Along the way, I will illustrate the benefits and implications of various mechanisms and provide some real-world tips on how users can simplify security management.
Attendees of this talk will learn about aspects of security in Kafka, including:
-Encryption: What is SSL, what problems it solves and how Kafka leverages it. We’ll discuss encryption in flight vs. encryption at rest.
-Authentication: Without authentication, anyone would be able to write to any topic in a Kafka cluster, do anything and remain anonymous. We’ll explore the available authentication mechanisms and their suitability for different types of deployment, including mutual SSL authentication, SASL/GSSAPI, SASL/SCRAM and SASL/PLAIN.
-Authorization: How ACLs work in Kafka, ZooKeeper security (risks and mitigations) and how to manage ACLs at scale
Deploying Apache CloudStack from API to UIJoe Brockmeier
For most organizations with a large computing footprint, it's not a matter of if you'll need a private cloud - it's when, and what kind. One of the most mature and widely deployed options is Apache CloudStack, a robust, turnkey cloud that includes everything you need to set up a private, public, or hybrid cloud. We'll cover Apache CloudStack from API to UI, and a little of everything in between.
This presentation is the introduction to the monthly CloudStack.org demonstration. The presentation details the latest features in the CloudStack open source project as well as project news. To attend a future presentation, with live demo and Q&A visit:
http://www.slideshare.net/cloudstack/introduction-to-cloudstack-12590733
CloudStack is an open source cloud computing platform that provides infrastructure as a service. It supports various hypervisors (KVM, Xen, VMware), has APIs for self-service provisioning, measures resource usage, and allows for rapid elasticity. CloudStack can be deployed as public, private or hybrid clouds and manages networks, storage, security and high availability of virtual machines.
Big Data means big hardware, and the less of it we can use to do the job properly, the better the bottom line. Apache Kafka makes up the core of our data pipelines at many organizations, including LinkedIn, and we are on a perpetual quest to squeeze as much as we can out of our systems, from Zookeeper, to the brokers, to the various client applications. This means we need to know how well the system is running, and only then can we start turning the knobs to optimize it. In this talk, we will explore how best to monitor Kafka and its clients to assure they are working well. Then we will dive into how to get the best performance from Kafka, including how to pick hardware and the effect of a variety of configurations in both the broker and clients. We’ll also talk about setting up Kafka for no data loss.
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.
Presented at SF Big Analytics Meetup
Online event processing applications often require the ability to ingest, store, dispatch and process events. Until now, supporting all of these needs has required different systems for each task -- stream processing engines, messaging queuing middleware, and pub/sub messaging systems. This has led to the unnecessary complexity for the development of such applications and operations leading to increased barrier to adoption in the enterprises. In this talk, Karthik will outline the need to unify these capabilities in a single system and make it easy to develop and operate at scale. Karthik will delve into how Apache Pulsar was designed to address this need with an elegant architecture. Apache Pulsar is a next generation distributed pub-sub system that was originally developed and deployed at Yahoo and running in production in more than 100+ companies. Karthik will explain how the architecture and design of Pulsar provides the flexibility to support developers and applications needing any combination of queuing, messaging, streaming and lightweight compute for events. Furthermore, he will provide real life use cases how Apache Pulsar is used for event processing ranging from data processing tasks to web processing applications.
Whether you are developing a greenfield data project or migrating a legacy system,
there are many critical design decisions to be made. Often, it is advantageous to not only
consider immediate requirements, but also the future requirements and technologies you may
want to support. Your project may start out supporting batch analytics with the vision of adding
realtime support. Or your data pipeline may feed data to one technology today, but tomorrow
an entirely new system needs to be integrated. Apache Kafka can help decouple these
decisions and provide a flexible core to your data architecture. This talk will show how building
Kafka into your pipeline can provide the flexibility to experiment, evolve and grow. It will also
cover a brief overview of Kafka, its architecture, and terminology.
This document discusses tuning Kafka for performance. It covers optimizing Zookeeper configurations like using SSDs; using RAID or JBOD for Kafka broker disks with testing showing XFS performs best; scaling Kafka clusters by considering disk capacity, network capacity, and partition counts; configuring topics for retention settings and partition balancing; and tuning Mirror Maker for network locality and producer/consumer settings.
Optimizing Regulatory Compliance with Big DataCloudera, Inc.
The document discusses optimizing regulatory compliance through next-generation data management and visualization. It outlines trends like increasing data volumes, sources, and regulatory requirements that are challenging traditional compliance architectures. A modern approach is proposed using big data platforms to ingest diverse data sources, perform automated preparation and analysis, and enable flexible reporting and visualization. This can help reduce costs, speed reporting, and improve auditability versus manual spreadsheet-based processes. Examples show how data preparation platforms combined with data storage, analytics, and visualization tools help financial firms more efficiently meet regulatory obligations like the SEC's Form PF.
Kafka Reliability Guarantees ATL Kafka User GroupJeff Holoman
The document discusses reliability guarantees in Apache Kafka. It explains that Kafka ensures reliability through replication, where each partition has leader and follower replicas. Producers receive acknowledgments when data is committed to the in-sync replicas. Consumers can commit offsets to ensure they don't miss data on rebalance. The document provides best practices for configuration of producers, consumers, and monitoring to prevent data loss in Kafka.
Building Realtim Data Pipelines with Kafka Connect and Spark StreamingGuozhang Wang
Spark Streaming makes it easy to build scalable, robust stream processing applications — but only once you’ve made your data accessible to the framework. Spark Streaming solves the realtime data processing problem, but to build large scale data pipeline we need to combine it with another tool that addresses data integration challenges. The Apache Kafka project recently introduced a new tool, Kafka Connect, to make data import/export to and from Kafka easier.
Kudu is a storage engine for Hadoop designed to address gaps in Hadoop's ability to handle workloads that require both high-throughput data ingestion and low-latency random access. It is a columnar storage engine that uses a log-structured merge tree to store data and provides APIs for NoSQL and SQL access. Kudu aims to provide high performance for both scans and random access through its columnar design and tablet architecture that partitions data across servers.
Kafka Reliability - When it absolutely, positively has to be thereGwen (Chen) Shapira
Kafka provides reliability guarantees through replication and configuration settings. It replicates data across multiple brokers to protect against failures. Producers can ensure data is committed to all in-sync replicas through configuration settings like request.required.acks. Consumers maintain offsets and can commit after processing to prevent data loss. Monitoring is also important to detect any potential issues or data loss in the Kafka system.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
Gartner Data and Analytics Summit: Bringing Self-Service BI & SQL Analytics ...Cloudera, Inc.
For self-service BI and exploratory analytic workloads, the cloud can provide a number of key benefits, but the move to the cloud isn’t all-or-nothing. Gartner predicts nearly 80 percent of businesses will adopt a hybrid strategy. Learn how a modern analytic database can power your business-critical workloads across multi-cloud and hybrid environments, while maintaining data portability. We'll also discuss how to best leverage the increased agility cloud provides, while maintaining peak performance.
Big Data Day LA 2015 - Introduction to Apache Kafka - The Big Data Message Bu...Data Con LA
Big Data systems keeps getting bigger. Types and counts of components involved in the system to get critical answers for businesses just keep increasing. It is like increasing number of houses or businesses in any metropolitan city. With more places people can be, more transportation is required. Requirement of more transportation is either met by more cars or a better public transportation system. More individual vehicles add to the traffic chaos. Apache Kafka is like public transportation system of the city of Big Data or even better a distributed system. This talk will go over basic overview of Apache Kafka, various components involved in it and the What and the How of the problem it solves.
This document provides an overview and agenda for a presentation on Apache Kafka. The presentation will cover Kafka concepts and architecture, how it compares to traditional messaging systems, using Kafka with Cloudera, and a demo of installing and configuring Kafka on a Cloudera cluster. It will also discuss Kafka's role in ingestion pipelines and data integration use cases.
This document discusses Apache Kafka and how it can be used by Oracle DBAs. It begins by explaining how Kafka builds upon the concept of a database redo log by providing a distributed commit log service. It then discusses how Kafka is a publish-subscribe messaging system and can be used to log transactions from any database, application logs, metrics and other system events. Finally, it discusses how schemas are important for Kafka since it only stores messages as bytes, and how Avro can be used to define and evolve schemas for Kafka messages.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
This document provides a summary of Amazon Kinesis and Apache Kafka, two platforms for processing real-time streaming data at large scale. It describes key features of each system such as durability, interfaces, processing options, and deployment. Kinesis is a fully managed cloud service that provides high durability for data across AWS availability zones. Kafka is an open source platform that offers lower latency and more flexibility in how data is processed but requires more operational overhead. The document also includes a deep dive on concepts and internals of the Kafka platform.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
Unleashing Real-time Power with Kafka.pptxKnoldus Inc.
Unlock the potential of real-time data streaming with Kafka in this session. Learn the fundamentals, architecture, and seamless integration with Scala, empowering you to elevate your data processing capabilities. Perfect for developers at all levels, this hands-on experience will equip you to harness the power of real-time data streams effectively.
3 Things to Learn About:
-How Kudu is able to fill the analytic gap between HDFS and Apache HBase
-The trade-offs between real-time transactional access and fast analytic performance
-How Kudu provides an option to achieve fast scans and random access from a single API
The document discusses integrating Apache Flink with Apache Bigtop to provide packaged Flink binaries. It describes Bigtop's role in standardizing packaging of big data components, and how it builds Flink from source and creates Linux packages. An example application called BigPetStore is implemented using Flink APIs and packaged with Bigtop. Finally, it outlines converting the Bigtop packages to Cloudera parcels for management by Cloudera Manager.
Not Your Mother's Kafka - Deep Dive into Confluent Cloud Infrastructure | Gwe...HostedbyConfluent
Confluent Cloud runs a modified version of Apache Kafka - redesigned to be cloud-native and deliver a serverless user experience. In this talk, we will discuss key improvements we've made to Kafka and how they contribute to Confluent Cloud availability, elasticity, and multi-tenancy. You'll learn about innovations that you can use on-prem, and everything you need to make the most of Confluent Cloud.
Availability of Kafka - Beyond the Brokers | Andrew Borley and Emma Humber, IBMHostedbyConfluent
While Kafka has guarantees around the number of server failures a cluster can tolerate, to avoid service interruptions, or even data loss, it is prudent to have infrastructure in place for when an environment becomes unavailable during a planned or unplanned outage.
This talk describes the architectures available to you when planning for an outage. We will examine configurations including active/passive and active/active as well as availability zones and debate the benefits and limitations of each. We will also cover how to set up each configuration using the tools in Kafka.
Whether downtime while you fail over clients to a backup is acceptable or you require your Kafka clusters to be highly available, this talk will give you an understanding of the options available to mitigate the impact of the loss of an environment.
The document provides an overview of Confluent Control Center and how it can be used to monitor Apache Kafka deployments. It discusses how Control Center provides visibility into key metrics for brokers, topics, consumers and connectors. It also describes how Control Center helps answer important business questions about whether applications are receiving all data, showing the latest data, if the applications or cluster need to scale, and ensures data is not lost. Control Center provides dashboards, alerts and visibility to help operators effectively manage Kafka clusters and identify and address issues.
This document discusses MySQL Fabric, a framework from Oracle for managing high availability and sharding of MySQL servers. MySQL Fabric provides transparent failover between primary and secondary MySQL servers using asynchronous replication. It also allows optional sharding of data across multiple server groups for horizontal scaling out. The document outlines the key capabilities and architecture of MySQL Fabric.
Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard
This document discusses building near-real-time analytics pipelines using Apache Spark Streaming and Apache Kudu on the Cloudera platform. It defines near-real-time analytics, describes the relevant components of the Cloudera stack (Kafka, Spark, Kudu, Impala), and how they can work together. The document then outlines the typical stages involved in implementing a Spark Streaming to Kudu pipeline, including sourcing from a queue, translating data, deriving storage records, planning mutations, and storing the data. It provides performance considerations and introduces Envelope, a Spark Streaming application on Cloudera Labs that implements these stages through configurable pipelines.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Kafka is a distributed, replicated, and partitioned platform for handling real-time data feeds. It allows both publishing and subscribing to streams of records, and is commonly used for applications such as log aggregation, metrics, and streaming analytics. Kafka runs as a cluster of one or more servers that can reliably handle trillions of events daily.
BDW Chicago 2016 - Jayesh Thakrar, Sr. Software Engineer, Conversant - Data...Big Data Week
We all know that Kafka is a fast, scalable, durable and distributed publish-subscribe messaging system.
From large internet companies to startups are using it as part of their streaming data pipeline for its speed, durability and ease of use/administration. Kafka has a strong durability promise, yet, there are certain situations when there can be data loss or data duplication. While data duplication is easy to detect, data loss is not when you are dealing with hundreds of millions of messages.
This session will look into those scenarios and possible ways to detect and minimize data loss and data duplication.
Lambda architecture on Spark, Kafka for real-time large scale MLhuguk
Sean Owen – Director of Data Science @Cloudera
Building machine learning models is all well and good, but how do they get productionized into a service? It's a long way from a Python script on a laptop, to a fault-tolerant system that learns continuously, serves thousands of queries per second, and scales to terabytes. The confederation of open source technologies we know as Hadoop now offers data scientists the raw materials from which to assemble an answer: the means to build models but also ingest data and serve queries, at scale.
This short talk will introduce Oryx 2, a blueprint for building this type of service on Hadoop technologies. It will survey the problem and the standard technologies and ideas that Oryx 2 combines: Apache Spark, Kafka, HDFS, the lambda architecture, PMML, REST APIs. The talk will touch on a key use case for this architecture -- recommendation engines.
This document introduces Akka, an open-source toolkit for building distributed, concurrent, and resilient message-driven applications for Java and Scala. It discusses how application requirements have changed to require clustering, concurrency, elasticity, and resilience. Akka uses an actor model with message-driven actors that can be distributed and made fault-tolerant. The document provides examples of creating and communicating between actors using messages, managing failures with supervision, and load balancing with routers.
- Perspectives in Eclipse workbench are visual containers that group related views and editors to accomplish a specific task.
- Creating a perspective involves defining an extension in plugin.xml and implementing the perspective class to control initial layout.
- Views display data from the domain model and are created by defining an extension point and implementing the view class.
- The entry point for an RCP application is a class that implements IApplication to control application execution and create the workbench.
The document discusses various types of dialogs in Eclipse including modal dialogs, message dialogs, confirmation dialogs, error dialogs, warning dialogs, tray dialogs, title area dialogs, and wizard dialogs. It provides details on how to create and use these different dialog types, including creating buttons, button bars, and wizard pages.
The document provides an overview of using JFace to build cross-platform user interfaces in Java. It discusses how JFace includes convenience classes for building SWT applications and a framework for displaying and editing business model objects using SWT widgets like TableViewer and TreeViewer. It then walks through an example of using these components to build a to-do list application, including creating model objects, initializing the TableViewer, and implementing the various event handler objects like ContentProvider, LabelProvider, CellEditor, CellModifier, and RowSorter.
This document provides an introduction and overview of the Standard Widget Toolkit (SWT), including:
- What SWT is and its advantages/disadvantages over Swing
- SWT's technical relationship to Swing
- An example of basic SWT coding
- Descriptions of key SWT concepts like Display, Shell, Composite, Control and Layout
The document provides an overview of Eclipse, an open-source integrated development environment (IDE). It discusses that Eclipse is built on an extensible plugin framework called OSGi/Equinox that allows new functionality to be easily added without disrupting existing features. It also describes key components of the Eclipse platform including SWT for cross-platform user interface elements, JFace for common UI tasks, and the workbench for the Eclipse environment. The document highlights that Eclipse can be used as not just an IDE but also as a general application framework and tools framework through its extensibility.