Multi tier, multi-tenant, multi-problem kafka

Kafka is a distributed publish-subscribe messaging system that allows both streaming and storage of data feeds. It is designed to be fast, scalable, durable, and fault-tolerant. Kafka maintains feeds of messages called topics that can be published to by producers and subscribed to by consumers. A Kafka cluster typically runs on multiple servers called brokers that store topics which may be partitioned and replicated for fault tolerance. Producers publish messages to topics which are distributed to consumers through consumer groups that balance load.

An introduction to Kafka cruise control which performs dynamic workload balancing for Kafka clusters.

Apache Kafka's rise in popularity as a streaming platform has demanded a revisit of its traditional at-least-once message delivery semantics. In this talk, we present the recent additions to Kafka to achieve exactly-once semantics (EoS) including support for idempotence and transactions in the Kafka clients. The main focus will be the specific semantics that Kafka distributed transactions enable and the underlying mechanics which allow them to scale efficiently.

Kafka is well known for high throughput ingestion. However, to get the best latency characteristics without compromising on throughput and durability, we need to tune Kafka. In this talk, we share our experiences to achieve the optimal combination of latency, throughput and durability for different scenarios.

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.

It covers a brief introduction to Apache Kafka Connect, giving insights about its benefits,use cases, motivation behind building Kafka Connect.And also a short discussion on its architecture.

Watch this talk here: https://www.confluent.io/online-talks/apache-kafka-architecture-and-fundamentals-explained-on-demand This session explains Apache Kafka’s internal design and architecture. Companies like LinkedIn are now sending more than 1 trillion messages per day to Apache Kafka. Learn about the underlying design in Kafka that leads to such high throughput. This talk provides a comprehensive overview of Kafka architecture and internal functions, including: -Topics, partitions and segments -The commit log and streams -Brokers and broker replication -Producer basics -Consumers, consumer groups and offsets This session is part 2 of 4 in our Fundamentals for Apache Kafka series.

Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process 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 Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.

Kafka is an open-source message broker that provides high-throughput and low-latency data processing. It uses a distributed commit log to store messages in categories called topics. Processes that publish messages are producers, while processes that subscribe to topics are consumers. Consumers can belong to consumer groups for parallel processing. Kafka guarantees order and no lost messages. It uses Zookeeper for metadata and coordination.

The document discusses Kafka, an open-source distributed event streaming platform. It provides an introduction to Kafka and describes how it is used by many large companies to process streaming data in real-time. Key aspects of Kafka explained include topics, partitions, producers, consumers, consumer groups, and how Kafka is able to achieve high performance through its architecture and design.

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.

Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming apps. It provides a unified, scalable, and durable platform for handling real-time data feeds. Kafka works by accepting streams of records from one or more producers and organizing them into topics. It allows both storing and forwarding of these streams to consumers. Producers write data to topics which are replicated across clusters for fault tolerance. Consumers can then read the data from the topics in the order it was produced. Major companies like LinkedIn, Yahoo, Twitter, and Netflix use Kafka for applications like metrics, logging, stream processing and more.

What is Kafka What problem does Kafka solve How does Kafka work What are the benefits of Kafka Conclusion

This document discusses using Apache Kafka as a data hub to capture changes from various data sources using change data capture (CDC). It outlines several common CDC patterns like using modification dates, database triggers, or log files to identify changes. It then discusses using Kafka Connect to integrate various data sources like MongoDB, PostgreSQL and replicate changes. The document provides examples of open source CDC connectors and concludes with suggestions for getting involved in the Apache Kafka community.

PPT Focus on Core Kafka concept: Why Kafka, Kafka Eco System, Topics and Partitions, Broker, Replication Factor, Segments, Leaders, Producer, Consumer, Kafka Connect: Connector Architecture, Demo of File and JDBC Connector Kafka Streams: Stream Processing, Kstream, and KTable, Demo

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.

Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable. Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden. This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents. We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse. Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.

Presented at Kafka Summit 2016 Operating out of multiple datacenters is a large part of most disaster recovery plans, but it brings extra complications to our data pipelines. Instead of having a straight path from front to back, it now has forks and dead ends and odd little use cases that don’t match up with a perfect view of the world. This talk will focus on how to best utilize Apache Kafka in this world, including basic architectures for multi-datacenter and multi-tier clusters. We will also touch on how to assure messages make it from producer to consumer, and how to monitor the entire ecosystem.

The document discusses Cruise Control, a tool for managing Apache Kafka clusters. It was created by LinkedIn to handle their large Kafka deployment consisting of over 2,000 brokers and 4 trillion messages per day. Cruise Control monitors broker loads, detects anomalies, and generates proposals to optimize resource usage and replica distributions. It supports operations like adding or removing brokers and performing automatic rebalances without downtime or data loss. The architecture includes components for load monitoring, analysis, execution and an API for administration.

First presentation for Savi's sponsorship of the Washington DC Spark Interactive. Discusses tips and lessons learned using Spark Streaming (24x7) to ingest and analyze Industrial Internet of Things (IIoT) data as part of a Lambda Architecture

The document discusses modern software development tools and practices, including: - Using Git for version control and GitHub for collaboration between developers. - Tools like Jenkins, Trello, and Slack to enable continuous integration, project management, and team communication. - Following architectural approaches like microservices and implementing infrastructure as code using tools from the HashiCorp stack like Vagrant, Consul, and Terraform. - Achieving continuous delivery by integrating development and operations to reliably release software through an automated deployment process.

Symantec deployed an SDN using OpenStack with the following key aspects: 1. They created different "Classes of Service" including a development environment and a production environment to onboard teams and manage workloads. 2. They provided self-service user onboarding through Horizon with automatic network creation to hide complexities. 3. They offered load balancing as a service using HA Proxy with various optimizations to achieve high performance. 4. They attached baremetal servers to the overlay network by launching them in network namespaces. 5. They aimed for over 99.95% control plane availability using a distributed controller and Cassandra setup with automation and monitoring.

The evolving complexity of the data center is placing increased demand on the network and security teams to come up with inventive methods for enforcing security policies in these ever-changing environments. The goal of this session is to provide participants with an understanding of features and design recommendations for integrating security into the data center environment. This session will focus on recommendations for securing next-generation data center architectures. Areas of focus include security services integration, leveraging device virtualization, and considerations and recommendations for server virtualization. The target audience are security and data center administrators.

Discussion on the needs and impact of data movement on hybrid clouds (where public and private clouds, along with legacy datacenters) collide.

This document discusses Oracle Ravello Cloud and provides an overview, live demonstration, and summary. Oracle Ravello Cloud allows users to migrate VMware workloads to public clouds without modification by lifting and shifting the virtual machines. The live demonstration shows importing an existing Primavera environment into Oracle Ravello Cloud and publishing the virtual machines to the cloud with one click. The summary notes that Oracle Ravello Cloud solves issues like compatibility, lock-in, and labor costs by allowing lift and shift of workloads to the cloud in an agnostic manner.

Many organizations anticipate significant growth in WAN bandwidth and Public Cloud usage. Leveraging the Internet to provide extra WAN bandwidth and to offload Public Cloud traffic is compelling, however network reliability, application performance and security are the primary roadblocks. Cisco IWAN transport solution is the most full featured architecture to support the Software Defined Wide Area Network (SD-WAN) requirements that are emerging in standards bodies like the Open Networking User Group (ONUG) to address these issues. Many enterprises are looking for the benefits these technologies deliver, but without the costs associated with owning and operating those technologies. Here is where VMS for IWAN meets market need. Cisco VMS is a full featured management platform for both virtual and physical devices. This session will cover a full description of the VMS platform and how it can be used to deliver exceptional customer experience when supporting a managed offering of IWAN. The roles of Customer and Resource Facing Services will be covered, along with integration between the IWAN service and SP operations. This session will also cover the topic of how Virtual Network Functions (VNFs) can be placed optimally in the network from the CPE to SP datacenter, along with a demo of the end user and operator experience.

Two large enterprise AEM implementations were presented and compared. Anshul Chhabra from Symantec presented their implementation handling 3.3 billion requests per month. Anil Kalbag from Cisco presented their implementation handling 375 million monthly page views. Both implementations utilized multiple data centers for high availability and disaster recovery. Key architecture decisions around virtual/physical infrastructure, storage, caching, and multi-tenancy were discussed and compared between the two organizations.

An integrated approach to network traffic management provides benefits over traditional point solutions. A single-point solution can monitor all types of network traffic, support multiple protocols, provide insightful dashboards and reports, and analyze network behavior. This saves troubleshooting time, improves service levels, and maximizes return on investment by enabling better resource allocation and optimization.

The operations engineer is often seen as the hero, toiling away late nights on call to keep the systems running through failures of hardware and of code. While developers try as hard as possible to move quickly and break things, we stand as the voice of reason urging caution. We’re the only ones who truly understand the systems, but you’ll rarely find documentation because it’s just too complex and changeable to write down. When we’re doing our jobs well, we’re unappreciated because nobody understands how difficult it is. When things break, everyone thinks we’re doing our jobs badly. These are not the things we aspire to. At LinkedIn, Site Reliability Engineers are one layer in a stack that starts with the way we manage our code and basic hardware, and is built with common systems for application management, monitoring, and alerting. Each layer has its own specialist engineers, focused on making their piece as resilient as it can be and building it to integrate with the rest of the stack. This lets Software Engineers concentrate on developing their applications, without having to spend time building systems to build, package, and distribute their code. SREs can dedicate their time to integrating applications with the stack, architecting and scaling deployments, as well as developing tools and documentation to make the job easier. When the inevitable failure happens, many experts come together to quickly identify and resolve the problem and improve the entire stack for everyone. Description: Presentation at the International Industry-Academia Workshop on Cloud Reliability and Resilience. 7-8 November 2016, Berlin, Germany. Organized by EIT Digital and Huawei GRC, Germany. Twitter: @CloudRR2016

Want to move your Oracle Commerce infrastructure to the cloud? If you are running your commerce business on Oracle Commerce (formerly ATG) stack, and want to take it to the cloud, check out this webinar slide deck. Two industry innovators are joined by the leading retail analyst to discuss why now is the time for the large retailers to transition to the cloud and how they can help.