An introduction to the kafka stream processing platform. The presentation gives a small introduction into stream processing and furthermore explains how kafka streams and kafka connect are used together to implement realtime stream processing flows.
This document discusses Apache Kafka and Confluent's Kafka Connect tool for large-scale streaming data integration. Kafka Connect allows importing and exporting data from Kafka to other systems like HDFS, databases, search indexes, and more using reusable connectors. Connectors use converters to handle serialization between data formats. The document outlines some existing connectors and upcoming improvements to Kafka Connect.
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.
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.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
Kafka Connect allows data ingestion into Kafka from external systems by using connectors. It provides scalability, fault tolerance, and exactly-once semantics. Connectors are run as tasks within workers that can run in either standalone or distributed mode. The Schema Registry works with Kafka Connect to handle schema validation and evolution.
Building Scalable and Extendable Data Pipeline for Call of Duty Games (Yarosl...confluent
What’s easier than building a data pipeline nowadays? You add a few Apache Kafka clusters and a way to ingest data (probably over HTTP), design a way to route your data streams, add a few stream processors and consumers, integrate with a data warehouse… wait, this looks like a lot of things, doesn’t it? And you probably want to make it highly scalable and available too. Join this session to learn best practices for building a data pipeline, drawn from my experience at Activision/Demonware. I’ll share the lessons learned about scaling pipelines, not only in terms of volume, but also in terms of supporting more games and more use cases. You’ll also hear about message schemas and envelopes, Apache Kafka organization, topics naming conventions, routing, reliable and scalable producers and the ingestion layer, as well as stream processing.
Kafka is a high-throughput distributed messaging system with publish and subscribe capabilities. It provides persistence with replication to disk for fault tolerance. Kafka is simple to implement and runs efficiently on large clusters with low latency and high throughput. It was created at LinkedIn to process streaming data from the LinkedIn website and has since been open sourced.
This document discusses Apache Kafka and Confluent's Kafka Connect tool for large-scale streaming data integration. Kafka Connect allows importing and exporting data from Kafka to other systems like HDFS, databases, search indexes, and more using reusable connectors. Connectors use converters to handle serialization between data formats. The document outlines some existing connectors and upcoming improvements to Kafka Connect.
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.
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.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
Kafka Connect allows data ingestion into Kafka from external systems by using connectors. It provides scalability, fault tolerance, and exactly-once semantics. Connectors are run as tasks within workers that can run in either standalone or distributed mode. The Schema Registry works with Kafka Connect to handle schema validation and evolution.
Building Scalable and Extendable Data Pipeline for Call of Duty Games (Yarosl...confluent
What’s easier than building a data pipeline nowadays? You add a few Apache Kafka clusters and a way to ingest data (probably over HTTP), design a way to route your data streams, add a few stream processors and consumers, integrate with a data warehouse… wait, this looks like a lot of things, doesn’t it? And you probably want to make it highly scalable and available too. Join this session to learn best practices for building a data pipeline, drawn from my experience at Activision/Demonware. I’ll share the lessons learned about scaling pipelines, not only in terms of volume, but also in terms of supporting more games and more use cases. You’ll also hear about message schemas and envelopes, Apache Kafka organization, topics naming conventions, routing, reliable and scalable producers and the ingestion layer, as well as stream processing.
Kafka is a high-throughput distributed messaging system with publish and subscribe capabilities. It provides persistence with replication to disk for fault tolerance. Kafka is simple to implement and runs efficiently on large clusters with low latency and high throughput. It was created at LinkedIn to process streaming data from the LinkedIn website and has since been open sourced.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high throughput, low latency data ingestion and distribution. It provides reliability through replication, scalability by partitioning topics across brokers, and durability by persisting messages to disk. Common uses of Kafka include metrics collection, log aggregation, and stream processing using frameworks like Spark Streaming. Kafka's architecture includes brokers that store topics which are partitions distributed across a cluster, with ZooKeeper for coordination. Producers write messages to topics and consumers read messages in a subscriber model.
This document discusses data integration and architectures for processing both batch and streaming data. It covers topics like data ingestion using tools like Flume, Sqoop and Kafka to move data into data lakes and warehouses. It also discusses batch processing using MapReduce on Hadoop and stream processing using real-time technologies like Kafka and architectures like lambda and kappa for serving queries on both real-time and batch-processed views of the data.
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Data Pipeline with Kafka, This slide include
Kafka Introduction, Topic / Partitions, Produce / Consumer, Quick Start, Offset Monitoring, Example Code, Camus
Real Time Streaming Data with Kafka and TensorFlow (Yong Tang, MobileIron) Ka...confluent
This document discusses using Kafka and TensorFlow for real-time streaming machine learning. It introduces TensorFlow 2.0 capabilities for data processing and machine learning. It then discusses challenges in integrating streaming data with machine learning frameworks and formats. It proposes using KafkaDataset, a TensorFlow dataset for reading and writing from Kafka. KafkaDataset allows streaming data in and out of TensorFlow models for real-time inference and prediction.
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
Introduction to Apache Kafka and Confluent... and why they matterconfluent
Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017.
Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBaseHBaseCon
Blackbird is a large-scale object store built at Rocket Fuel, which stores 100+ TB of data and provides real time access to 10 billion+ objects in a 2-3 milliseconds at a rate of 1 million+ times per second. In this talk (an update from HBaseCon 2014), we will describe Blackbird's comprehensive collections API and various examples of how it can be used to model collections like sets, maps, and aggregates on these collections like counters, etc. We will also illustrate the flexibility and power of the API by modeling custom collection types that are unique to the Rocket Fuel context.
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.
An evening with Jay Kreps; author of Apache Kafka, Samza, Voldemort & Azkaban.Data Con LA
Jay Kreps, Open Source Visionary and Co Founder of Confluent and several open source projects will be visiting LA. I have asked him to come present at our group. He will present his vision and will answer questions regarding Kafka and other projects
Bio:-
Jay is the co-founder and CEO at Confluent a company built around realtime data streams and the open source messaging system Apache Kafka. He is the original author of several of open source projects including Apache Kafka, Apache Samza, Voldemort, and Azkaban.
Flume is an Apache project for log aggregation and movement, optimized for Hadoop ecosystems. It uses a push model with agents and channels. Kafka is a distributed publish-subscribe messaging system optimized for high throughput and availability. It uses a pull model and supports multiple consumers. Kafka generally has higher throughput than Flume. Flume and Kafka can be combined, with Flume using Kafka as a channel or source/sink, to take advantage of both systems.
This document discusses the evolution of Kafka clusters at AppsFlyer over time. The initial cluster had 4 brokers and handled hundreds of millions of messages with low partitioning and replication. A new cluster was designed with more brokers, replication across availability zones, and higher partitioning to support billions of messages. However, this led to issues like uneven leader distribution and failures. Various solutions were implemented like increasing brokers, splitting topics, and hardware upgrades. Ongoing testing and monitoring helped identify more problems and improvements around replication, partitioning, and automation. Key lessons learned included balancing replication and leaders, supporting dynamic changes, and thorough testing of failure scenarios.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
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.
Tapad's data pipeline is an elastic combination of technologies (Kafka, Hadoop, Avro, Scalding) that forms a reliable system for analytics, realtime and batch graph-building, and logging. In this talk, I will speak about the creation and evolution of the pipeline, and a concrete example – a day in the life of an event tracking pixel. We'll also talk about common challenges that we've overcome such as integrating different pieces of the system, schema evolution, queuing, and data retention policies.
Architecture of a Kafka camus infrastructuremattlieber
This document summarizes the results of a performance evaluation of Kafka and Camus to ingest streaming data into Hadoop. It finds that Kafka can ingest data at rates from 15,000-50,000 messages per second depending on data format (Avro is fastest). Camus can move the data to HDFS at rates from 54,000-662,000 records per second. Once in HDFS, queries on Avro-formatted data are fastest, with count and max aggregation queries completing in under 100 seconds for 20 million records. The customer's goal of 5000 events per second can be easily achieved with this architecture.
How Tencent Applies Apache Pulsar to Apache InLong - Pulsar Summit Asia 2021StreamNative
1) Apache InLong is an open source data integration framework that provides automatic, secure, and reliable data transmission. It supports both batch and stream processing using different message queues like Apache Pulsar.
2) Apache Pulsar is used with Apache InLong because it offers very low latency, high throughput, reliable data transmission, and multi-tenancy. KoP allows migrating Kafka workloads to Pulsar.
3) Apache InLong contributes to Apache Pulsar through over 60 contributors and 50 pull requests to the KoP project. It uses Pulsar for auto disaster tolerance, multi-tenancy of data streams, and auditing data streams.
Bullet is an open sourced, lightweight, pluggable querying system for streaming data without a persistence layer implemented on top of Storm. It allows you to filter, project, and aggregate on data in transit. It includes a UI and WS. Instead of running queries on a finite set of data that arrived and was persisted or running a static query defined at the startup of the stream, our queries can be executed against an arbitrary set of data arriving after the query is submitted. In other words, it is a look-forward system.
Bullet is a multi-tenant system that scales independently of the data consumed and the number of simultaneous queries. Bullet is pluggable into any streaming data source. It can be configured to read from systems such as Storm, Kafka, Spark, Flume, etc. Bullet leverages Sketches to perform its aggregate operations such as distinct, count distinct, sum, count, min, max, and average.
An instance of Bullet is currently running at Yahoo against its user engagement data pipeline. We’ll highlight how it is powering internal use-cases such as web page and native app instrumentation validation. Finally, we’ll show a demo of Bullet and go over query performance numbers.
This document summarizes Kafka internals including how Zookeeper is used for coordination, how brokers store partitions and messages, how producers and consumers interact with brokers, how to ensure data integrity, and new features in Kafka 0.9 like security enhancements and the new consumer API. It also provides an overview of operating Kafka clusters including adding and removing brokers through reassignment.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Apache Kafka is a distributed publish-subscribe messaging system that allows for scalable message processing. It provides high throughput, fault tolerance, and guarantees delivery. Kafka maintains feeds of messages in topics which can be consumed by applications or services. It is commonly used for processing real-time data streams and event-driven architectures. Confluent provides a platform for Apache Kafka with additional tools for monitoring, management, and integration with other data systems.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high throughput, low latency data ingestion and distribution. It provides reliability through replication, scalability by partitioning topics across brokers, and durability by persisting messages to disk. Common uses of Kafka include metrics collection, log aggregation, and stream processing using frameworks like Spark Streaming. Kafka's architecture includes brokers that store topics which are partitions distributed across a cluster, with ZooKeeper for coordination. Producers write messages to topics and consumers read messages in a subscriber model.
This document discusses data integration and architectures for processing both batch and streaming data. It covers topics like data ingestion using tools like Flume, Sqoop and Kafka to move data into data lakes and warehouses. It also discusses batch processing using MapReduce on Hadoop and stream processing using real-time technologies like Kafka and architectures like lambda and kappa for serving queries on both real-time and batch-processed views of the data.
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Data Pipeline with Kafka, This slide include
Kafka Introduction, Topic / Partitions, Produce / Consumer, Quick Start, Offset Monitoring, Example Code, Camus
Real Time Streaming Data with Kafka and TensorFlow (Yong Tang, MobileIron) Ka...confluent
This document discusses using Kafka and TensorFlow for real-time streaming machine learning. It introduces TensorFlow 2.0 capabilities for data processing and machine learning. It then discusses challenges in integrating streaming data with machine learning frameworks and formats. It proposes using KafkaDataset, a TensorFlow dataset for reading and writing from Kafka. KafkaDataset allows streaming data in and out of TensorFlow models for real-time inference and prediction.
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
Introduction to Apache Kafka and Confluent... and why they matterconfluent
Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017.
Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBaseHBaseCon
Blackbird is a large-scale object store built at Rocket Fuel, which stores 100+ TB of data and provides real time access to 10 billion+ objects in a 2-3 milliseconds at a rate of 1 million+ times per second. In this talk (an update from HBaseCon 2014), we will describe Blackbird's comprehensive collections API and various examples of how it can be used to model collections like sets, maps, and aggregates on these collections like counters, etc. We will also illustrate the flexibility and power of the API by modeling custom collection types that are unique to the Rocket Fuel context.
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.
An evening with Jay Kreps; author of Apache Kafka, Samza, Voldemort & Azkaban.Data Con LA
Jay Kreps, Open Source Visionary and Co Founder of Confluent and several open source projects will be visiting LA. I have asked him to come present at our group. He will present his vision and will answer questions regarding Kafka and other projects
Bio:-
Jay is the co-founder and CEO at Confluent a company built around realtime data streams and the open source messaging system Apache Kafka. He is the original author of several of open source projects including Apache Kafka, Apache Samza, Voldemort, and Azkaban.
Flume is an Apache project for log aggregation and movement, optimized for Hadoop ecosystems. It uses a push model with agents and channels. Kafka is a distributed publish-subscribe messaging system optimized for high throughput and availability. It uses a pull model and supports multiple consumers. Kafka generally has higher throughput than Flume. Flume and Kafka can be combined, with Flume using Kafka as a channel or source/sink, to take advantage of both systems.
This document discusses the evolution of Kafka clusters at AppsFlyer over time. The initial cluster had 4 brokers and handled hundreds of millions of messages with low partitioning and replication. A new cluster was designed with more brokers, replication across availability zones, and higher partitioning to support billions of messages. However, this led to issues like uneven leader distribution and failures. Various solutions were implemented like increasing brokers, splitting topics, and hardware upgrades. Ongoing testing and monitoring helped identify more problems and improvements around replication, partitioning, and automation. Key lessons learned included balancing replication and leaders, supporting dynamic changes, and thorough testing of failure scenarios.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
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.
Tapad's data pipeline is an elastic combination of technologies (Kafka, Hadoop, Avro, Scalding) that forms a reliable system for analytics, realtime and batch graph-building, and logging. In this talk, I will speak about the creation and evolution of the pipeline, and a concrete example – a day in the life of an event tracking pixel. We'll also talk about common challenges that we've overcome such as integrating different pieces of the system, schema evolution, queuing, and data retention policies.
Architecture of a Kafka camus infrastructuremattlieber
This document summarizes the results of a performance evaluation of Kafka and Camus to ingest streaming data into Hadoop. It finds that Kafka can ingest data at rates from 15,000-50,000 messages per second depending on data format (Avro is fastest). Camus can move the data to HDFS at rates from 54,000-662,000 records per second. Once in HDFS, queries on Avro-formatted data are fastest, with count and max aggregation queries completing in under 100 seconds for 20 million records. The customer's goal of 5000 events per second can be easily achieved with this architecture.
How Tencent Applies Apache Pulsar to Apache InLong - Pulsar Summit Asia 2021StreamNative
1) Apache InLong is an open source data integration framework that provides automatic, secure, and reliable data transmission. It supports both batch and stream processing using different message queues like Apache Pulsar.
2) Apache Pulsar is used with Apache InLong because it offers very low latency, high throughput, reliable data transmission, and multi-tenancy. KoP allows migrating Kafka workloads to Pulsar.
3) Apache InLong contributes to Apache Pulsar through over 60 contributors and 50 pull requests to the KoP project. It uses Pulsar for auto disaster tolerance, multi-tenancy of data streams, and auditing data streams.
Bullet is an open sourced, lightweight, pluggable querying system for streaming data without a persistence layer implemented on top of Storm. It allows you to filter, project, and aggregate on data in transit. It includes a UI and WS. Instead of running queries on a finite set of data that arrived and was persisted or running a static query defined at the startup of the stream, our queries can be executed against an arbitrary set of data arriving after the query is submitted. In other words, it is a look-forward system.
Bullet is a multi-tenant system that scales independently of the data consumed and the number of simultaneous queries. Bullet is pluggable into any streaming data source. It can be configured to read from systems such as Storm, Kafka, Spark, Flume, etc. Bullet leverages Sketches to perform its aggregate operations such as distinct, count distinct, sum, count, min, max, and average.
An instance of Bullet is currently running at Yahoo against its user engagement data pipeline. We’ll highlight how it is powering internal use-cases such as web page and native app instrumentation validation. Finally, we’ll show a demo of Bullet and go over query performance numbers.
This document summarizes Kafka internals including how Zookeeper is used for coordination, how brokers store partitions and messages, how producers and consumers interact with brokers, how to ensure data integrity, and new features in Kafka 0.9 like security enhancements and the new consumer API. It also provides an overview of operating Kafka clusters including adding and removing brokers through reassignment.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Apache Kafka is a distributed publish-subscribe messaging system that allows for scalable message processing. It provides high throughput, fault tolerance, and guarantees delivery. Kafka maintains feeds of messages in topics which can be consumed by applications or services. It is commonly used for processing real-time data streams and event-driven architectures. Confluent provides a platform for Apache Kafka with additional tools for monitoring, management, and integration with other data systems.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Presentation @ Oracle Code Berlin.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and 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 analysed, often with many consumers or systems interested in all or part of the events. How can we make sure that all these events are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amounts of messages between a source and a target. This session will start with an introduction of Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table.
Multitenancy: Kafka clusters for everyone at LINEkawamuray
Yuto Kawamura from LINE Corporation presented on their use of Apache Kafka clusters to provide multitenancy for different internal teams. They face challenges in ensuring isolation between client workloads and preventing abusive clients. Their solutions include request quotas to limit client resource usage, slow logs to identify slow requests, and changes to the broker code to pre-warm caches and minimize the impact of disk reads during message fetching. With these approaches, they are able to reliably operate shared Kafka clusters with high throughput and multiple tenants.
Kafka Multi-Tenancy—160 Billion Daily Messages on One Shared Cluster at LINE confluent
(Yuto Kawamura, LINE Corporation) Kafka Summit SF 2018
LINE is a messaging service with 160+ million active users. Last year I talked about how we operate our Kafka cluster that receives more than 160 billion messages daily, dealing with performance problems to meet our tight requirement. Since last year we have deployed three more new clusters each for different purposes, such as one in different datacenter, one for security sensitive usages and so on, still keeping the fundamental concept: one cluster for everyone to use. While letting many projects using few multi-tenancy clusters greatly saves our operational cost and enables us to concentrate our engineering resources for maximizing their reliability, hosting multiple topics of different kinds of workload led us through a lot of challenges, too.
In this talk I will introduce how we operate Kafka clusters shared among different services, solving troubles we met to maximize its reliability. Especially, one of the most critical issues we’ve solved—delayed consumer Fetch request causing a broker’s network threads to be blocked—should be very interesting because it could have worse overall performance of brokers in a very common situation, and we have managed to solve it leveraging advanced technique such as dynamic tracing and tricky patch to control in-kernel behavior from Java code.
Kafka Multi-Tenancy - 160 Billion Daily Messages on One Shared Cluster at LINEkawamuray
This document summarizes a presentation about Kafka multi-tenancy at LINE Corporation. The presentation discusses how LINE runs a single shared Kafka cluster to handle over 100 billion messages per day from many independent services. It describes the hardware used, requirements for multi-tenancy like protecting against abusive workloads and providing isolation. It then discusses specific issues identified like slow response times caused by disk reads, and the solutions implemented like request quotas, metrics, and pre-loading data into memory to avoid blocking. The presentation concludes that after addressing these issues, their shared Kafka hosting model works efficiently while maintaining a single data hub.
Apache Kafka - A modern Stream Processing PlatformGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Streaming in Practice - Putting Apache Kafka in Productionconfluent
This presentation focuses on how to integrate all these components into an enterprise environment and what things you need to consider as you move into production.
We will touch on the following topics:
- Patterns for integrating with existing data systems and applications
- Metadata management at enterprise scale
- Tradeoffs in performance, cost, availability and fault tolerance
- Choosing which cross-datacenter replication patterns fit with your application
- Considerations for operating Kafka-based data pipelines in production
What's new in Confluent 3.2 and Apache Kafka 0.10.2 confluent
With the introduction of connect and streams API in 2016, Apache Kafka is becoming the defacto solution for anyone looking to build a streaming platform. The community continues to add additional capabilities to make it the complete solution for streaming data.
Join us as we review the latest additions in Apache Kafka 0.10.2. In addition, we’ll cover what’s new in Confluent Enterprise 3.2 that makes it possible for running Kafka at scale.
Apache Kafka is a distributed streaming platform and distributed publish-subscribe messaging system. It uses a log abstraction to order events and replicate data across clusters. Kafka allows developers to publish and subscribe to streams of records known as topics. Producers publish data to topics and consumers subscribe to topics to process streams of records. The Kafka ecosystem includes tools like KStreams for stream processing and KSQL for querying streams of data.
Apache Kafka is a distributed streaming platform. It provides a high-throughput distributed messaging system with publish-subscribe capabilities. The document discusses Kafka producers and consumers, Kafka clients in different programming languages, and important configuration settings for Kafka brokers and topics. It also demonstrates sending messages to Kafka topics from a Java producer and consuming messages from the console consumer.
This document provides an introduction to Apache Kafka. It discusses why Kafka is needed for real-time streaming data processing and real-time analytics. It also outlines some of Kafka's key features like scalability, reliability, replication, and fault tolerance. The document summarizes common use cases for Kafka and examples of large companies that use it. Finally, it describes Kafka's core architecture including topics, partitions, producers, consumers, and how it integrates with Zookeeper.
Kafka Connect & Kafka Streams/KSQL - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
DevOps Fest 2020. Сергій Калінець. Building Data Streaming Platform with Apac...DevOps_Fest
Apache Kafka зараз на хайпі. Все більше компаній починають використовувати її, як message bus. Проте Kafka може набагато більше, аніж бути просто транспортом. Її реальна міць і краса розкриваються, коли Kafka стає центральною нервовою системою вашої архітектури. Вона швидка, надійна і доволі гнучка для різних сценаріїв використання.
На цій доповіді Сергій поділитися досвідом побудови data streaming платформи. Ми поговоримо про те, як Kafka працює, як її потрібно конфігурувати і в які халепи можна потрапити, якщо Kafka використовується неоптимально.
Streaming ETL with Apache Kafka and KSQLNick Dearden
Companies new and old are all recognizing the importance of a low-latency, scalable, fault-tolerant data backbone - in the form of the Apache Kafka streaming platform. With Kafka developers can integrate multiple systems and data sources to enable low-latency analytics, event-driven architectures, and the population of downstream systems. What's more, these data pipelines can be built using configuration alone.
In this talk, we'll see how easy it is to capture a stream of data changes in real-time from a database such as MySQL into Kafka using the Kafka Connect framework and then use KSQL to filter, aggregate and join it to other data, and finally stream the results from Kafka out into multiple targets such as Elasticsearch and MySQL. All of this can be accomplished without a single line of Java code!
Set your Data in Motion with Confluent & Apache Kafka Tech Talk Series LMEconfluent
Confluent Platform is supporting London Metal Exchange’s Kafka Centre of Excellence across a number of projects with the main objective to provide a reliable, resilient, scalable and overall efficient Kafka as a Service model to the teams across the entire London Metal Exchange estate.
AI&BigData Lab 2016. Сарапин Виктор: Размер имеет значение: анализ по требова...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Как устроить анализ данных 40 млн. человек за 5 лет так, чтобы это выглядело почти в реальном времени.
Kafka meetup JP #3 - Engineering Apache Kafka at LINEkawamuray
This document summarizes a presentation about engineering Apache Kafka at LINE. Some key points:
- LINE uses Apache Kafka as a central data hub to pass data between services, handling over 140 billion messages per day.
- Data stored in Kafka includes application logs, data mutations, and task requests. This data is used for tasks like data replication, analytics, and asynchronous processing.
- Performance optimizations have led to target latencies below 1ms for 50% of produces and below 10ms for 99% of produces.
- SystemTap, a Linux tracing tool, helped identify slow disk reads causing delayed Kafka responses, improving performance.
- Having a single Kafka cluster as a data hub makes inter-service
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and 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 analysed, often with many consumers or systems interested in all or part of the events. How can me make sure that all these event are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
Ever wondered how you could process any kind of data you can get your hands on? This presentation outlines a blueprint for a bigdata architecture to process any data fragment as an event, allowing to slice and dice your data as you see fit.
Learning about and working with distributed technologies can be quite a hassle. We think we found a solution for that and at strata + hadoop Europe 2014 in Barcelona we got the opportunity to explain our ideas and the motivation behind bigboards.io
This document discusses big data and the Internet of Things (IoT). It begins with an overview of big data, including how it was started by Google and the need to store and process large amounts of data from various sources that is generated quickly. It then discusses Hadoop and Apache Storm as platforms for batch and real-time processing. The next section discusses using data from "Things" in IoT as a data source for big data systems, how to control devices based on the data, and storing data for complex analysis. Finally, it raises some ideas, risks, and gains regarding using big data platforms for IoT.
This document discusses big data architectures for analytical and operational use cases. It presents two main architectures:
1. Analytical big data architecture focuses on enriching structured data to optimize business processes. It involves ingesting data from various sources, enriching the data, and storing it in a distributed database for analysis.
2. Operational big data architecture processes both structured and unstructured data in real-time to innovate business operations. It ingests and combines batch and real-time data, stores the results in a real-time datastore, and provides real-time views and batch reports.
The document advises starting with pilot projects to test technologies and be prepared to change technologies as needed. It
The document discusses how TransCo, an international transport company, can become more event-driven by treating all business events as immutable records. It proposes storing all events and generating different views of the data for various uses. These views would be read-only and generated from the events, allowing the company to discover trends, perform real-time and batch processing, and scale to large volumes of data by leveraging big data technologies like HDFS and Storm. Becoming fully event-driven in this way makes the system more robust, scalable and able to regenerate past states.
Big data technologies allow companies of all sizes to store and analyze large amounts of data at lower costs than traditional systems. Specifically, big data can help companies analyze data with higher precision by storing all data without aggregation, analyze historical facts by storing all data first before defining parameters, prevent data loss by surviving failures without losing information, and eliminate data silos by consolidating data from different departments into a single repository. While big data requires an understanding of new techniques and tools, it provides solutions to common business problems that were previously difficult to address.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
Do People Really Know Their Fertility Intentions? Correspondence between Sel...Xiao Xu
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
5. Stream Processing
Get real-time insights
Lower processing latency
Easier to test
Easier to maintain
Easier to scale
Different way of thinking
At-least-once vs exactly-
once
Time
+ -
6. Every company is already
doing stream processing
(more or less ... )
7. A Stream
Key 1 -> value 1
Key 2 -> value 2
Key 1 -> value 3
...
20. Kafka Platform
Streams and Connect apps are just (java) apps
Streams and Connect are libraries
Can be deployed like any other (java) app
Multiple instances of the same app can be launched
Use tools like Mesos, kubernetes, Docker Swarm, ...
30. Sequential disk access is fast*
* Don’t believe me? Read http://kafka.apache.org/documentation#persistence
31. Producer
Puts messages onto kafka
Determines the partition to write to
Can be implemented in many, many languages
32. Consumer
Gets messages from kafka
Can be grouped into Consumer Groups
Allows for round robin message delivery
Enables scaling of consumers
Have a persisted offset per Consumer Group
Stored in Zookeeper
Or in Kafka
43. Kafka Streams
KStream for a stream of data
KTable to keep the latest value for each key
KTable state is distributed across app instances
Transform from streams to tables and tables to streams
Choose which field to use as “timestamp”
44. TOPIC A
TOPIC B
TOPIC C
Kafka
Connect
App
Kafka
Streams
App
Kafka
Streams
App
Kafka
Connect
App
TOPIC C
TOPIC B
TOPIC A
47. TOPIC A
TOPIC B
TOPIC C
Sales JDBC
Kafka
Connect
Top
Products
Ranker
Emailer
TOPIC C
TOPIC B
TOPIC A
Low Stock
Notifier
Kafka
Connect
App
Slack Poster