DataOps Automation for a Kafka Streaming Platform (Andrew Stevenson + Spiros Economakis, Lenses) Kafka Summit 2020
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DataOps challenges us to build data experiences in a repeatable way. For those with Kafka, this means finding a means of deploying flows in an automated and consistent fashion. The challenge is to make the deployment of Kafka flows consistent across different technologies and systems: the topics, the schemas, the monitoring rules, the credentials, the connectors, the stream processing apps. And ideally not coupled to a particular infrastructure stack. In this talk we will discuss the different approaches and benefits/disadvantages to automating the deployment of Kafka flows including Git operators and Kubernetes operators. We will walk through and demo deploying a flow on AWS EKS with MSK and Kafka Connect using GitOps practices: including a stream processing application, S3 connector with credentials held in AWS Secrets Manager.
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In this talk, I’m going to go over the core concepts of Kafka offsets and lags, and explain why lag even matters and is an important KPI to measure. I’ll also talk about the kind of research we did to find the right tool, what the options in the market were at the time, and eventually why we chose Linkedin’s Burrow as the right tool for us. And finally, I’ll take a closer look at Burrow, its building blocks, how we build and deploy it, how we monitor better with it, and eventually the most important improvement - how we transformed its output from numbers to time-based metrics.
Kafka in Context, Cloud, & Community (Simon Elliston Ball, Cloudera) Kafka Su...
Kafka has fast become the center of streaming analytics applications in the modern digital enterprise. Kafka operates in the context of a broad ecosystem of data lifecycle components which need a consistent platform of security, monitoring, management and governance. This problem becomes paramount when your streaming architectures go hybrid by spanning from on-premises to the cloud. Throw in the reality of a multi-cloud setup that a lot of enterprises are facing and now, you have a complex streaming architecture that is difficult to operationally manage, monitor, secure or govern.
Cloudera remains committed to an open community driven approach and increasing the ease of use and visibility for Kafka based solutions. Attend this session to understand more about how streaming architectures can be extended easily to the hybrid cloud and multi-cloud. Also, learn about our plans for further community contributions.
Keep your Metadata Repository Current with Event-Driven Updates using CDC and...
The data science techniques and machine learning models that provide the greatest business value and insights require data that spans enterprise silos. To integrate this data, and ensure you’re joining on the right fields, you need a comprehensive, enterprise-wide metadata repository. More importantly, you need it to be always up to date. Nightly updates are simply not good enough when customers and users expect near-real-time responsiveness.
The challenge with keeping a metadata repository up to date lies not with cloud services or distributed storage frameworks, but rather with the relational database management systems (RDBMSs) that dot the enterprise landscape. At Comcast, we’ve found it relatively easy to feed our Apache Atlas metadata repo incrementally from Hadoop and AWS, using event-driven pushes to a dedicated Apache Kafka topic that Atlas listens to. Such pushes are not practical with RDBMSs, however, since the event-driven technique there is the database trigger. Triggers are so invasive and potentially detrimental to performance that your DB admin likely won’t allow one for detecting metadata changes.
Triggers are out. Pulling the complete current state of metadata from a RDBMS at regular intervals and calculating the deltas is too slow and unworkable. And, it turns out that out-of-the-box log-based change data capture (CDC) is also dead-end because metadata changes are represented in transaction logs as SQL DDL strings, not as atomic insert/update/delete operations as for data.
So, how do you keep your metadata repository always up to date with the current state of your RDBMS metadata? Our group solved this challenge by creating an alternate method for CDC on RDBMS metadata based on database system tables. Our query-based CDC serves as a Kafka Connect source for our Apache Atlas sink, providing event-driven, continuous updates to RDBMS metadata in our repository, but does not suffer from the usual limitations/disadvantages of vanilla query-based CDC. If you’re facing a similar challenge, join us at this session to learn more about the obstacles you’ll likely face and how you can overcome them using the method we implemented.
Extracting Value from IOT using Azure Cosmos DB, Azure Synapse Analytics and ...
Due to explosion of IoT, we have streaming data that needs to be processed in real-time. This needs to be made available for applications as well as analytics scenarios such as anomaly detection. This workshop presents a solution using Confluent Cloud on Azure, Azure Cosmos DB and Azure Synapse Analytics which can be connected in a secure way within Azure VNET using Azure Private link configured on Kafka clusters.
Event & Data Mesh as a Service: Industrializing Microservices in the Enterpri...
Kafka is widely positioned as the proverbial "central nervous system" of the enterprise. In this session, we explore how the central nervous system can be used to build a mesh topology & unified catalog of enterprise wide events, enabling development teams to build event driven architectures faster & better.
The central theme of this topic is also aligned to seeking idioms from API Management, Service Meshes, Workflow management and Service orchestration. We compare how these approaches can be harmonized with Kafka.
We will also touch upon the topic of how this relates to Domain Driven Design, CQRS & other patterns in microservices.
Some potential takeaways for the discerning audience:
1. Opportunities in a platform approach to Event Driven Architecture in the enterprise
2. Adopting a product mindset around Data & Event Streams
3. Seeking harmony with allied enterprise applications
Keynote: Jay Kreps, Confluent | Kafka ♥ Cloud | Kafka Summit 2020
In this talk, Confluent co-founder and CEO, Jay Kreps will cover the rise of two trends:
1. The rise of Apache Kafka and event streams
2. The rise of the public cloud and cloud-native data systems
... and the problems we need to solve as these two trends come together.
PCAP Graphs for Cybersecurity and System Tuning
Cybersecurity is a broad topic and many commercial products are related to it. We demonstrate a fundamental concept in network analysis: re-construction and visualization of temporal networks. Furthermore, we apply the method to describe operational conditions of a Hadoop cluster. Our experiments provide first results and allow a classification of the cluster state related to current workloads. The temporal networks show significant differences for different operation modes. In reallity we would expect mixed workloads. If such workload parameters are known, we are able to handle a-typical events accordingly - which means, we are able to create alerts based on context information, rather than only the package content. We show an end-to-end example: (1) Data collection is done via python, using the sniffer script; (2) using Apache Hive and Apache Spark we analyze the network traffic data and create the temporary network. Finally, we are able to visualize the results using Gephi in step (3). In a next step, we plan to contribute to the Apache Spot project.
user Behavior Analysis with Session Windows and Apache Kafka's Streams API
For many industries the need to group together related events based on a period of activity or inactivity is key. Advertising businesses, content producers are just a few examples of where session windows can be used to better understand user behavior.
While such sessionization has been possible in Apache Kafka up to this point, implementing it has been rather complex and required leveraging low-level APIs. In the most recent release of Kafka, however, new capabilities have been added making session windows much easier to implement.
In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...
Are you looking for a cloud-based architecture that includes the best of breed streaming and database technologies? In this session you will learn how to setup and configure the Confluent Cloud with MongoDB Atlas. We'll start the journey learning about the basic connectivity between the two cloud services and end with a brief discovery of what you can do with data once it is in MongoDB Atlas. By the end of this session you will know how to securely setup and configure the MongoDB Atlas connectors in the Confluent Cloud in both a source and sink configuration.
Data Integration with Apache Kafka: What, Why, How
Presented at Orange County Advanced Analytics and Big Data Meetup, June 21 2019.
Apache Kafka has fast become the dominant messaging technology for the enterprise; if you're a data scientist or data engineer and you have not yet worked with Kafka, that situation will likely change soon! In this session, Pat Patterson, director of evangelism at StreamSets, explains what Kafka is, why it has disrupted the previous generation of messaging products, and how you can use open source products to build dataflow pipelines with Kafka, without writing code.
INTRODUCING: CREATE PIPELINE
Exactly-one semantics with Kafka and MemSQL Pipelines.
MemSQL webcast 20 October, 16
Enterprise Metadata Integration
EMI supports knowledge management for better Data Science.
Presented @GraphConnect 2017 (QEII Centre London)
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms.
Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.
Achieve Sub-Second Analytics on Apache Kafka with Confluent and Imply
Presenters: Rachel Pedreschi, Senior Director, Solutions Engineering, Imply.io + Josh Treichel, Partner Solutions Architect, Confluent
Analytic pipelines running purely on batch processing systems can suffer from hours of data lag, resulting in accuracy issues with analysis and overall decision-making. Join us for a demo to learn how easy it is to integrate your Apache Kafka® streams in Apache Druid (incubating) to provide real-time insights into the data.
In this online talk, you’ll hear about ingesting your Kafka streams into Imply’s scalable analytic engine and gaining real-time insights via a modern user interface.
Register now to learn about:
-The benefits of combining a real-time streaming platform with a comprehensive analytics stack
-Building an analytics pipeline by integrating Confluent Platform and Imply
-How KSQL, streaming SQL for Kafka, can easily transform and filter streams of data in real time
-Querying and visualizing streaming data in Imply
-Practical ways to implement Confluent Platform and Imply to address common use cases such as analyzing network flows, collecting and monitoring IoT data and visualizing clickstream data
Confluent Platform, developed by the creators of Kafka, enables the ingest and processing of massive amounts of real-time event data. Imply, the complete analytics stack built on Druid, can ingest, store, query and visualize streaming data from Confluent Platform, enabling end-to-end real-time analytics. Together, Confluent and Imply can provide low latency data delivery, data transform, and data querying capabilities to power a range of use cases.
Project Ouroboros: Using StreamSets Data Collector to Help Manage the StreamS...
On a typical day we see hundreds of downloads of StreamSets Data Collector, our open source data integration tool. We used to wrangle our download logs using a combination of the AWS S3 command line, sed, grep, awk and other tools, all run from a shell script (on my laptop!) once a week. This was a classic example of a brittle, hard to maintain, custom data integration. One day it dawned on me, "This is crazy, we have a tool that can do all this!". In this session, I'll explain how I built a dataflow pipeline to stream content delivery network (CDN) logs from S3 to MySQL in real-time, allowing us to gain valuable insights into our open source community. You'll also learn how we use the same techniques to not only gain insights into our community on Slack, but also build tools to better serve them.
Flattening the Curve with Kafka (Rishi Tarar, Northrop Grumman Corp.) Kafka S...
Responding to a global pandemic presents a unique set of technical and public health challenges. The real challenge is the ability to gather data coming in via many data streams in variety of formats influences the real-world outcome and impacts everyone. The Centers for Disease Control and Prevention CELR (COVID Electronic Lab Reporting) program was established to rapidly aggregate, validate, transform, and distribute laboratory testing data submitted by public health departments and other partners. Confluent Kafka with KStreams and Connect play a critical role in program objectives to:
o Track the threat of COVID-19 virus
o Provide comprehensive data for local, state, and federal response
o Better understand locations with an increase in incidence
Operational Analytics on Event Streams in Kafka
Speaker: Anirudh Ramanthan, Product Manager, Rockset
Tracking key events and analyzing these event streams are critical to many enterprises. We highlight how organizations are using Apache Kafka® as a fast, reliable event streaming platform alongside Rockset, a serverless search and analytics engine, to create stateful microservices to analyze their event streams.
In this talk, we will discuss a stateful microservices architecture, where events from multiple channels are collected and streamed into Kafka and continuously ingested into Rockset with no explicit schema or metadata specification required. Developers then use serverless compute frameworks, like AWS Lambda, in conjunction with serverless data management from Rockset to build microservices to derive insights on the data from Kafka. Organizations can leverage this pattern to support low-latency queries on event streams, providing immediate insight on their business.
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How a distributed graph analytics platform uses Apache Kafka for data ingesti...
How a distributed graph analytics platform uses Apache Kafka for data ingesti...
Kafka Lag Monitoring For Human Beings (Elad Leev, AppsFlyer) Kafka Summit 2020
Kafka Lag Monitoring For Human Beings (Elad Leev, AppsFlyer) Kafka Summit 2020
Kafka in Context, Cloud, & Community (Simon Elliston Ball, Cloudera) Kafka Su...
Kafka in Context, Cloud, & Community (Simon Elliston Ball, Cloudera) Kafka Su...
Keep your Metadata Repository Current with Event-Driven Updates using CDC and...
Keep your Metadata Repository Current with Event-Driven Updates using CDC and...
Extracting Value from IOT using Azure Cosmos DB, Azure Synapse Analytics and ...
Extracting Value from IOT using Azure Cosmos DB, Azure Synapse Analytics and ...
Event & Data Mesh as a Service: Industrializing Microservices in the Enterpri...
Event & Data Mesh as a Service: Industrializing Microservices in the Enterpri...
Keynote: Jay Kreps, Confluent | Kafka ♥ Cloud | Kafka Summit 2020
Keynote: Jay Kreps, Confluent | Kafka ♥ Cloud | Kafka Summit 2020
user Behavior Analysis with Session Windows and Apache Kafka's Streams API
user Behavior Analysis with Session Windows and Apache Kafka's Streams API
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...
Data Integration with Apache Kafka: What, Why, How
Data Integration with Apache Kafka: What, Why, How
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
Disrupting Big Data with Apache Spark in the Cloud
Disrupting Big Data with Apache Spark in the Cloud
Achieve Sub-Second Analytics on Apache Kafka with Confluent and Imply
Achieve Sub-Second Analytics on Apache Kafka with Confluent and Imply
Project Ouroboros: Using StreamSets Data Collector to Help Manage the StreamS...
Project Ouroboros: Using StreamSets Data Collector to Help Manage the StreamS...
Flattening the Curve with Kafka (Rishi Tarar, Northrop Grumman Corp.) Kafka S...
Flattening the Curve with Kafka (Rishi Tarar, Northrop Grumman Corp.) Kafka S...
Similar to DataOps Automation for a Kafka Streaming Platform (Andrew Stevenson + Spiros Economakis, Lenses) Kafka Summit 2020
All the Ops: DataOps with GitOps for Streaming data on Kafka and Kubernetes
Running Apache Kafka and Kubernetes is synonymous with containerized real time data. Many users have adopted the pairing to deploy and manage individual distributed real time applications.
While Kubernetes allows developers to scale applications in microservices quicker, there are still productivity blockers such as visibility and governance.
Enter DataOps.
In this webinar, you'll learn how to:
Enhance the productivity of your Kafka & Kubernetes stream with DataOps
Enable enterprise adoption and scaling
Govern & secure your stream
[Capitole du Libre] #serverless - mettez-le en oeuvre dans votre entreprise...
Tout comme le Cloud IaaS avant lui, le serverless promet de faciliter le succès de vos projets en accélérant le Time to Market et en fluidifiant les relations entre Devs et Ops.
Mais sa mise en œuvre au sein d’une entreprise reste complexe et coûteuse.
Après 2 ans à mettre en place des plateformes managées de ce type, nous partagons nos expériences de ce qu’il faut faire pour mettre en œuvre du serverless en entreprise, en évitant les douleurs et en limitant les contraintes au maximum.
Tout d’abord l’architecture technique, avec 2 implémentations très différentes : Kubernetes et Helm d’un côté, Clever Cloud on-premise de l’autre.
Ensuite, la mise en place et l’utilisation d’OpenFaaS. Comment tester et versionner du Function as a Service. Mais aussi les problématiques de blue/green deployment, de rolling update, d’A/B testing. Comment diagnostiquer rapidement les dépendances et les communications entre services.
Enfin, en abordant les sujets chers à la production : * vulnerability management et patch management, * hétérogénéïté du parc, * monitoring et alerting, * gestion des stacks obsolètes, etc.
Building Data Intensity with AWS MSK & Lenses.io
Lenses.io presentation at Amazon Head Office, February 2020.
2016 - Open Mic - IGNITE - Open Infrastructure = ANY Infrastructure
Open Mic Ignite Presentation by Rob Hirschfeld
OpenStack Preso: DevOps on Hybrid Infrastructure
Discusses the approach for making hybrid DevOps workable including what obstacles must be overcome. Includes demo of multiple OpenStack clouds & Kubernetes deploy on AWS, Google and OpenStack
Gluecon Preso: Hybrid Container Infrastructure
My Gluecon presentation about hybrid infrastructure and container orchestration deployment. I talk about why composability matters and how AWS sets the standard.
Continuous Lifecycle London 2018 Event Keynote
Today it’s all about delivering velocity without compromising on quality, yet it’s becoming increasingly difficult for organisations to keep up with the challenges of current release management and traditional operations. The demand for developers to own the end-to-end delivery, including operational ownership, is increasing. A “you build it, you own it” development process requires tools that developers know and understand. So I’d like to introduce “GitOps”- an agile software lifecycle for modern applications.
In this session, I will discuss these industry challenges, including current CICD trends and how they’re converging with operations and monitoring. I’ll also illustrate the GitOps model, identify best practices and tools to use, and explain how you can benefit from adopting this methodology inherited from best practices going back 10-15 years.
Wasp2 - IoT and Streaming Platform
WASP is a framework to develop big data pipelines, working with streaming analytics, multi model storages and machine learning models. Everything is in real time.
Yannis Zarkadas. Enterprise data science workflows on kubeflow
Yannis Zarkadas. Enterprise data science workflows on kubeflow
http://aiconf.com.ua/
Yannis Zarkadas. Stefano Fioravanzo. Enterprise data science workflows on kub...
Yannis Zarkadas. Stefano Fioravanzo. Enterprise data science workflows on kubeflow
http://aiconf.com.ua/
Enterprise Application Migration
SpringOne Platform 2019
Session Title: Enterprise Application Migration
Speakers: Ashley Eckard, Senior Systems Engineer, The Home Depot; Michael Wright, Sr Manager, Platform Architects, Pivotal
Youtube: https://youtu.be/3DgUQgLZC9c
Episode 4: Operating Kubernetes at Scale with DC/OS
You’ve installed your Kubernetes cluster on DC/OS — now what? Operating Kubernetes efficiently can be challenging. In the final episode of our Kubernetes series, we will share best practices for operating your DC/OS Kubernetes cluster and maintaining performance. During this presentation, Joerg Schad and Chris Gaun show you how to successfully operate Kubernetes at scale in your environment.
During this session, we discuss:
1. How to upgrade DC/OS and Kubernetes with no downtime
2. How DC/OS guards against failure and enables fault domains that are resistant to outages within racks, availability zones, or cloud environments
3. How the monitoring and metrics capabilities on DC/OS improve operational analytics and help you get the most from your cluster
4. How cloud bursting extends your on-prem environment with resources from the cloud to handle spikes in your workload
Best Practices for Building Hybrid-Cloud Architectures | Hans Jespersen
Best Practices for building Hybrid-Cloud Architectures - Hans Jespersen
Afternoon opening presentation during Confluent’s streaming event in Paris, presented by Hans Jespersen, VP WW Systems Engineering at Confluent.
Containers and Kubernetes
The combination of StackPointCloud with NetApp creates NetApp Kubernetes Service, the industry’s first complete Kubernetes platform for multi-cloud deployments and a complete cloud-based stack for Azure, Google Cloud, AWS, and NetApp HCI. Further, Trident is a fully supported open source project maintained by NetApp, designed from the ground up to help meet the sophisticated persistence demands of containerized applications.
Netflix Cloud Architecture and Open Source
A presentation on the Netflix Cloud Architecture and NetflixOSS open source. For the All Things Open 2015 conference in Raleigh 2015/10/19. #ATO2015 #NetflixOSS
The Never Landing Stream with HTAP and Streaming
The Never Landing Stream with HTAP and Streaming
Apache NiFi
Apache Kudu
Apache Flink
Apache Kafka
PingCap
Streaming
Cloudera
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
With the breadth of sheer functionalities which need to be addressed in the Machine Learning world around building, training, serving and managing models, getting it done in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying ML workloads. Kubeflow is designed to take advantage of these benefits. In this talk, we are going to address how to make it easy for everyone to develop, deploy, and manage portable, scalable ML everywhere and support the full lifecycle Machine Learning using open source technologies like Kubeflow, Tensorflow, PyTorch,Tekton, Knative, Istio and others. We are going to discuss how to enable distributed training of models, model serving, canary rollouts, drift detection, model explainability, metadata management, pipelines and others. Additionally we will discuss Watson productization in progress based on Kubeflow Pipelines and Tekton, and point to Kubeflow Dojo materials and follow-on workshops.
How to build streaming data pipelines with Akka Streams, Flink, and Spark usi...
The Lightbend project family has grown: Introducing Cloudflow, the fastest way to build streaming data pipelines with Akka Streams, Flink, and Spark.
Similar to DataOps Automation for a Kafka Streaming Platform (Andrew Stevenson + Spiros Economakis, Lenses) Kafka Summit 2020 (20)
All the Ops: DataOps with GitOps for Streaming data on Kafka and Kubernetes
All the Ops: DataOps with GitOps for Streaming data on Kafka and Kubernetes
[Capitole du Libre] #serverless - mettez-le en oeuvre dans votre entreprise...
[Capitole du Libre] #serverless - mettez-le en oeuvre dans votre entreprise...
2016 - Open Mic - IGNITE - Open Infrastructure = ANY Infrastructure
2016 - Open Mic - IGNITE - Open Infrastructure = ANY Infrastructure
Yannis Zarkadas. Enterprise data science workflows on kubeflow
Yannis Zarkadas. Enterprise data science workflows on kubeflow
Yannis Zarkadas. Stefano Fioravanzo. Enterprise data science workflows on kub...
Yannis Zarkadas. Stefano Fioravanzo. Enterprise data science workflows on kub...
Episode 4: Operating Kubernetes at Scale with DC/OS
Episode 4: Operating Kubernetes at Scale with DC/OS
Best Practices for Building Hybrid-Cloud Architectures | Hans Jespersen
Best Practices for Building Hybrid-Cloud Architectures | Hans Jespersen
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
How to build streaming data pipelines with Akka Streams, Flink, and Spark usi...
How to build streaming data pipelines with Akka Streams, Flink, and Spark usi...
More from HostedbyConfluent
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
"In this talk, attendees will be provided with an introduction to Kafka Connect and the basics of Single Message Transforms (SMTs) and how they can be used to transform data streams in a simple and efficient way. SMTs are a powerful feature of Kafka Connect that allow custom logic to be applied to individual messages as they pass through the data pipeline. The session will explain how SMTs work, the types of transformations they can be used for, and how they can be applied in a modular and composable way.
Further, the session will discuss where SMTs fit in with Kafka Connect and when they should be used. Examples will be provided of how SMTs can be used to solve common data integration challenges, such as data enrichment, filtering, and restructuring. Attendees will also learn about the limitations of SMTs and when it might be more appropriate to use other tools or frameworks.
Additionally, an overview of the alternatives to SMTs, such as Kafka Streams and KSQL, will be provided. This will help attendees make an informed decision about which approach is best for their specific use case.
Whether attendees are developers, data engineers, or data scientists, this talk will provide valuable insights into how Kafka Connect and SMTs can help streamline data processing workflows. Attendees will come away with a better understanding of how these tools work and how they can be used to solve common data integration challenges."
Renaming a Kafka Topic | Kafka Summit London
"While Apache Kafka lacks native support for topic renaming, there are scenarios where renaming topics becomes necessary. This presentation will delve into the utilization of MirrorMaker 2.0 as a solution for renaming Kafka topics. It will illustrate how MirrorMaker 2.0 can efficiently facilitate the migration of messages from the old topic to the new one and how Kafka Connect Metrics can be employed to monitor the mirroring progress. The discussion will encompass the complexity of renaming Kafka topics, addressing certain limitations, and exploring potential workarounds when using MirrorMaker 2.0 for this purpose. Despite not being originally designed for topic renaming, MirrorMaker 2.0 has a suitable solution for renaming Kafka topics.
Blog Post : https://engineering.hellofresh.com/renaming-a-kafka-topic-d6ff3aaf3f03"
Evolution of NRT Data Ingestion Pipeline at Trendyol
"Trendyol, Turkey's leading e-commerce company, is committed to positively impacting the lives of millions of customers. Our decision-making processes are entirely driven by data. As a data warehouse team, our primary goal is to provide accurate and up-to-date data, enabling the extraction of valuable business insights.
We utilize the benefits provided by Kafka and Kafka Connect to facilitate the transfer of data from the source to our analytical environment. We recently transitioned our Kafka Connect clusters from on-premise VMs to Kubernetes. This shift was driven by our desire to effectively manage rapid growth(marked by a growing number of producers, consumers, and daily messages), ensuring proper monitoring and consistency. Consistency is crucial, especially in instances where we employ Single Message Transforms to manipulate records like filtering based on their keys or converting a JSON Object into a JSON string.
Monitoring our cluster's health is key and we achieve this through Grafana dashboards and alerts generated through kube-state-metrics. Additionally, Kafka Connect's JMX metrics, coupled with NewRelic, are employed for comprehensive monitoring.
The session will aim to explain our approach to NRT data ingestion, outlining the role of Kafka and Kafka Connect, our transition journey to K8s, and methods employed to monitor the health of our clusters."
Ensuring Kafka Service Resilience: A Dive into Health-Checking Techniques
"Join our lightning talk to delve into the strategies vital for maintaining a resilient Kafka service.
While proactive monitoring is key for issue prevention, failures will still occur. Rapid detection tools will enable you to identify and resolve problems before they impact end-users. This session explores the techniques employed by Kafka cloud providers for this detection, many of which are also applicable if you are managing independent Kafka clusters or applications.
The talk focuses on health-checking, a powerful tool that encompasses an application and its monitoring to validate Kafka environment availability. The session navigates through Kafka health-check methods, sharing best practices, identifying common pitfalls, and highlighting the monitoring of critical performance metrics like throughput and latency for early issue detection.
Attendees will gain valuable insights into the art of health-checking their Kafka environment, equipping them with the tools to identify and address issues before they escalate into critical problems. We invite all Kafka enthusiasts to join us in this talk to foster a deeper understanding of Kafka health-checking and ensure the continued smooth operation of your Kafka environment."
Exactly-once Stream Processing with Arroyo and Kafka
"Stream processing systems traditionally gave their users the choice between at least once processing and at most once processing: accepting duplicate data or missing data. But ideally we would provide exactly-once processing, where every event in the input data is represented exactly once in the output.
Kafka provides a transaction API that enables exactly-once when using Kafka as your source and sink. But this API has turned out to not be well suited for use by high level streaming systems, requiring various work arounds to still provide transactional processing.
In this talk, I’ll cover how the transaction API works, and how systems like Arroyo and Flink have used it to build exactly-once support, and how improvements to the transactional API will enable better end-to-end support for consistent stream processing."
Fish Plays Pokemon | Kafka Summit London
"In this talk, we will explore the exciting world of IoT and computer vision by presenting a unique project: Fish Plays Pokemon. Using an ESP Eye camera connected to an ESP32 and other IoT devices, to monitor fish's movements in an aquarium.
This project showcases the power of IoT and computer vision, demonstrating how even a fish can play a popular video game. We will discuss the challenges we faced during development, including real-time processing, IoT device integration, and Kafka message consumption.
By the end of the talk, attendees will have a better understanding of how to combine IoT, computer vision, and the usage of a serverless cloud to create innovative projects. They will also learn how to integrate IoT devices with Kafka to simulate keyboard behavior, opening up endless possibilities for real-time interactions between the physical and digital worlds."
Tiered Storage 101 | Kafla Summit London
What is tiered storage and what is it good for? After this session you will know how to leverage the tiered storage feature to enable longer retention than the storage attached to brokers allows. You will get acquainted with the different configuration options and know what to expect when you enable the feature, like for example when will the first upload to the remote object storage take place.
Building a Self-Service Stream Processing Portal: How And Why
"Real-time 24/7 monitoring and verification of massive data is challenging – even more so for the world’s second largest manufacturer of memory chips and semiconductors. Tolerance levels are incredibly small, any small defect needs to be identified and dealt with immediately. The goal of semiconductor manufacturing is to improve yield and minimize unnecessary work.
However, even with real-time data collection, the data was not easy to manipulate by users and it took many days to enable stream processing requests – limiting its usefulness and value to the business.
You’ll hear why SK hynix switched to Confluent and how we developed a self-service stream process portal on top of it. Now users have an easy-to-use service to manipulate the data they want.
Results have been impressive, stream processing requests are available the same day – previously taking 5 days! We were also able to drive down costs by 10% as stream processing requests no longer require additional hardware.
What you’ll take away from our talk:
- What were the pain points in the previous environment
- How we transitioned to Confluent without service downtime
- Creating a self-service stream processing portal built on top of Connect and ksqlDB
- Use case of stream process portal"
From the Trenches: Improving Kafka Connect Source Connector Ingestion from 7 ...
"Discover how default configurations might impact ingestion times, especially when dealing with large files. We'll explore a real-world scenario with a 20,000,000+ line file, assessing metrics and exploring the bottleneck in the default setup. Understand the intricacies of batch size calculations and how to optimize them based on your unique data characteristics.
Walk away with actionable insights as we showcase a practical example, turning a 7-hour ingestion process into a mere 30 minutes for over 30,000,000 records in a Kafka topic. Uncover metrics, configurations, and best practices to elevate the performance of your Kafka Connect CSV source connectors. Don't miss this opportunity to optimize your data pipeline and ensure smooth, efficient data flow."
Future with Zero Down-Time: End-to-end Resiliency with Chaos Engineering and ...
"In order to meet the current and ever-increasing demand for near-zero RPO/RTO systems, a focus on resiliency is critical. While Kafka offers built-in resiliency features, a perfect blend of client and cluster resiliency is necessary in order to achieve a highly resilient Kafka client application.
At Fidelity Investments, Kafka is used for a variety of event streaming needs such as core brokerage trading platforms, log aggregation, communication platforms, and data migrations. In this lightening talk, we will discuss the governance framework that has enabled producers and consumers to achieve their SLAs during unprecedented failure scenarios. We will highlight how we automated resiliency tests through chaos engineering and tightly integrated observability dashboards for Kafka clients to analyze and optimize client configurations. And finally, we will summarize the chaos test suite and the ""test, test and test"" mantra that are helping Fidelity Investments reach its goal of a future with zero down-time."
Navigating Private Network Connectivity Options for Kafka Clusters
"There are various strategies for securely connecting to Kafka clusters between different networks or over the public internet. Many cloud providers even offer endpoints that privately route traffic between networks and are not exposed to the internet. But, depending on your network setup and how you are running Kafka, these options ... might not be an option!
In this session, we’ll discuss how you can use SSH bastions or a self managed PrivateLink endpoint to establish connectivity to your Kafka clusters without exposing brokers directly to the internet. We explain the required network configuration, and show how we at Materialize have contributed to librdkafka to simplify these scenarios and avoid fragile workarounds."
Apache Flink: Building a Company-wide Self-service Streaming Data Platform
"In my talk, we will examine all the stages of building our self-service Streaming Data Platform based on Apache Flink and Kafka Connect, from the selection of a solution for stateful streaming data processing, right up to the successful design of a robust self-service platform, covering the challenges that we’ve met.
I will share our experience in providing non-Java developers with a company-wide self-service solution, which allows them to quickly and easily develop their streaming data pipelines.
Additionally, I will highlight specific business use cases that would not have been implemented without our platform.0 characters0 characters"
Explaining How Real-Time GenAI Works in a Noisy Pub
"Almost everyone has heard about large language models, and tens of millions of people have tried out OpenAI ChatGPT and Google Bard. However, the intricate architecture and underlying mathematics driving these remarkable systems remain elusive to many.
LLM's are fascinating - so let's grab a drink and find out how these systems are built and dive deep into their inner workings. In the length of time it to enjoy a round of drinks, you'll understand the inner workings of these models. We'll take our first sip of word vectors, enjoy the refreshing taste of the transformer, and drain a glass understanding how these models are trained on phenomenally large quantities of data.
Large language models for your streaming application - explained with a little maths and a lot of pub stories"
TL;DR Kafka Metrics | Kafka Summit London
"Monitoring is a fundamental operation when running Kafka and Kafka applications in production. There are numerous metrics available when using Kafka, however the sheer number is overwhelming, making it challenging to know where to start and how to properly utilise them.
This session will introduce you to some of the key metrics that should be monitored and best practices in fine tuning your monitoring. We will delve into which metrics are the indicators for cluster’s availability and performance and are the most helpful when debugging client applications."
A Window Into Your Kafka Streams Tasks | KSL
Kafka Streams relies on state restoration for maintaining standby tasks as failure recovery mechanism as well as for restoring the state after rebalance scenarios. When you are scaling up or down your application instances, it is necessary to know the current state of the restoration process for each active and standby task in order to prevent a long restoration process as much as possible. During this presentation, you will get an understanding of how KIP-869 provides valuable information about the current active task restoration after a rebalance and KIP-988 opens a window to the continuous process of standby restoration. When you encounter a situation in which you need to choose whether or not to scale up or down your application instances, both KIPs will be an invaluable ally for you.
Mastering Kafka Producer Configs: A Guide to Optimizing Performance
"In this talk, we will dive into the world of Kafka producer configs and explore how to understand and optimize them for better performance. We will cover the different types of configs, their impact on performance, and how to tune them to achieve the best results. Whether you're new to Kafka or a seasoned pro, this session will provide valuable insights and practical tips for improving your Kafka producer performance.
- Introduction to Kafka producer internal and workflow
- Understanding the producer configs like linger.ms, batch.size, buffer.memory and their impact on performance
- Learning about producer configs like max.block.ms, delivery.timeout.ms, request.timeout.ms and retries to make producer more resilient.
- Discuss configs like enable.idempotence, max.in.flight.requests.per.connection and transaction related configs to achieve delivery guarantees.
- Q&A session with attendees to address specific questions and concerns."
Data Contracts Management: Schema Registry and Beyond
"Data contracts are one of the hottest topics in the data management community. A data contract is a formal agreement between a data producer and its consumers, aimed at reducing data downtime and improving data quality. Schemas are an important part of data contracts, but they are not the only relevant element.
In this talk, we’ll:
1. see why data contracts are so important but also difficult to implement;
2. identify the characteristics of a well-designed data contract:
discuss the anatomy of a data contract, its main elements and, how to formally describe them;
3. show how to manage the lifecycle of a data contract leveraging Confluent Platform's services."
Code-First Approach: Crafting Efficient Flink Apps
"In the realm of stateful stream processing, Apache Flink has emerged as a powerful and versatile platform. However, the conventional SQL-based approach often limits the full potential of Flink applications.
We will delve into the benefits of adopting a code-first approach, which provides developers with greater control over application logic, facilitates complex transformations, and enables more efficient handling of state and time. We will also discuss how the code-first approach can lead to more maintainable and testable code, ultimately improving the overall quality of your Flink applications.
Whether you're a seasoned Flink developer or just starting your journey, this talk will provide valuable insights into how a code-first approach can revolutionize your stream processing applications."
Debezium vs. the World: An Overview of the CDC Ecosystem
"Change Data Capture (CDC) has become a commodity in data engineering, much in part due to the ever-rising success of Debezium [1]. But is that all there is? In this lightning talk, we’ll outline the current state of the CDC ecosystem, and understand why adopting a Debezium alternative is still a hard sell. If you’ve ever wondered what else is out there, but can’t keep up with the sprawling of new tools in the ecosystem; we’ll wrap it up for you!
[1] https://debezium.io/"
Beyond Tiered Storage: Serverless Kafka with No Local Disks
"Separation of compute and storage has become the de-facto standard in the data industry for batch processing.
The addition of tiered storage to open source Apache Kafka is the first step in bringing true separation of compute and storage to the streaming world.
In this talk, we'll discuss in technical detail how to take the concept of tiered storage to its logical extreme by building an Apache Kafka protocol compatible system that has zero local disks.
Eliminating all local disks in the system requires not only separating storage from compute, but also separating data from metadata. This is a monumental task that requires reimagining Kafka's architecture from the ground up, but the benefits are worth it.
This approach enables a stateless, elastic, and serverless deployment model that minimizes operational overhead and also drives inter-zone networking costs to almost zero."
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https://alandix.com/academic/papers/synergy2024-epistemic/
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- 1. for a Kafka streaming platform DataOps automation
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- 3. Delivering data intensity on Kafka Lenses.io
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