Azure Cosmos DB Kafka Connectors | Abinav Rameesh, Microsoft
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The document discusses Kafka connectors for Cosmos DB that allow for seamless integration between the two services without requiring complex application code. It provides an overview of Kafka Connect and connectors, use cases for integrating Cosmos DB and Kafka, and the architecture of source and sink connectors that can read from and write to Cosmos DB and Kafka. It also previews a demo of the connectors and suggests ways to take integration further.
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Being a pioneer in the interactive gaming industry, SONY PlayStation has played a vital role in implementing technological advancements thus help bringing global video gaming community together. With the recent launch of next generation console PS-5 into the market by partnering with thousands of game developers and millions of video gamers across the globe, humongous volumes of data generation in playstation servers is quite inevitable. This presentation talks about how we leveraged big data technologies along with Apache Kafka to solve some of the realtime data analytical problems. Two important case studies we carryout recently are: ""Competitive pricing analysis of game titles across online video game marketplaces"" & ""understand the gamers sentiment by streaming data from social feeds and perform NLP""
Along with Apache Kafka, the technologies that we have used to architect the solution are: REST API, ZooKeeper, D3.js visualization, DoMo, Python, SQL, NLP, AWS Cloud & JSON.
Kafka at the core of an AIOps pipeline | Sunanda Kommula, Selector.ai and Ala...
Large networks consist of a diverse range of equipment, across private, public, hybrid clouds and partner networks. A hierarchical network has layers of infrastructure, catering to access, core, or distribution roles, managed by different organizations specialized to architect the right network hardware, software, and features for that network layer. The nature of data generated by each component can vary in type and form, including logs, events, metrics, or alarms.
The diversity of data generated by a large network is beyond human scale. Apache Kafka® is a critical hub in large networks, empowering AIOps to enhance decision making, improve analysis and insights by contextualizing large volumes of operational data. Kafka solved the big problem of collecting, processing, storing and normalizing data at scale, allowing us to focus on building the AIOps pipeline.
Our platform connects the dots across relevant operations data and provides operations teams with simple and powerful access to insights, from within increasingly popular collaboration environments like Slack and Microsoft teams. The pipeline must also integrate with automation solutions.
This session will cover how large volumes of streaming messages can be received by parallel Kafka consumers, and turned into action by network operations teams, dramatically reducing downtime and improving performance.
Feed Your SIEM Smart with Kafka Connect (Vitalii Rudenskyi, McKesson Corp) Ka...
SIEM platforms are essential to the new cybersecurity paradigm and data collection layer is a very important piece of it.
When you deliver a new platform, you can easily get lost in a variety of different vendors and solutions, too many challenges are facing. What if I change vendors, will I keep my data? How to feed multiple tools with the same data? How to collect data from custom apps and services? How to pay less for an expensive platform? How to keep data without a huge cost?
Join us if you are looking for the answers. In this session, you will learn how we replaced the vendor-provided data collection layer with kafka connect and the lessons we learnt. After the talk you will know:
- architecture and real-life examples of the flexible and highly available data collection platform
- custom connectors that do most of the work for us and how to extend the connectors to consume new data, we made them open sourced
- easy way to receive data from thousands of servers and many cloud services
- how to archive data at low cost
You will leave armed with a set of free tools and recipes to build a truly vendor-agnostic data collection platform. It will allow you to take you SIEM costs under control. You will feed your analytics tools with what they need and archive the rest at low cost. You will feed your SIEM smart!
5 lessons learned for successful migration to Confluent cloud | Natan Silinit...
Confluent Cloud makes Devops engineers lives a lot more easier.
Yet moving 1500 microservices, 10K topics and 100K partitions to a multi-cluster Confluent cloud can be a challenge.
In this talk you will hear about 5 lessons that Wix has learned in order to successfully meet this challenge.
These lessons include:
1. Automation, Automation, Automation - all the process has to be completely automated at such scale
2. Prefer a gradual approach - E.g. migrate topics in small chunks and not all at once. Reduces risks if things go bad
3. Cleanup first - avoid migrating unused topics or topics with too many unnecessary partitions
Nordstrom's Event-Sourced Architecture and Kafka-as-a-Service | Adam Weyant a...
As a 120 year-old company, Nordstrom was facing numerous challenges as a result of an aging, service-oriented, architecture. Developers needing to implement reporting for analytics separately from core functionality resulted in questionable data quality for analytical purposes. Scaling dependent services in harmony to not overwhelm each other was a struggle faced by many, if not most, teams. Several years into a company-wide transition to an event-sourced architecture, Nordstrom has solved these and various other problems. By leveraging the capabilities of Apache Kafka and Confluent, combined with a deep organizational focus on well-defined business event schemas, a singular event can be used for analytical, functional, operational, and model building purposes. This session will describe this architecture and the lessons learned while building it, with a focus on the internally built, multi-tenant, multi-cluster, Kafka-as-a-Service platform that enables it.
Devops Days, 2019 - Charlotte
Presentation on journey of Continuous Integration and Continuous Deployment at Botsplash
DataOps Automation for a Kafka Streaming Platform (Andrew Stevenson + Spiros ...
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.
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.
Developing custom transformation in the Kafka connect to minimize data redund...
Compacted topics grow over time and are often utilizing high performance, low latency and relatively expensive storage solutions. Reducing duplicated data plays a critical role in the size of compacted topics. with less data on the topics, the Kafka cluster consumes less disk space which in turn it leads to lower operation cost.
in this use case-driven talk, we are going to demonstrate how our team at UnitedHealth Group leveraged existing transformers to extract data from the message metadata in the topic as well as how we developed our customized transformers to minimize the amount of duplicated data in each message in the topic.
Streaming data in the cloud with Confluent and MongoDB Atlas | Robert Waters,...
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.
apidays LIVE New York 2021 - Service reliability through autoscaling workload...
apidays LIVE New York 2021 - API-driven Regulations for Finance, Insurance, and Healthcare
July 28 & 29, 2021
Service reliability through autoscaling workloads on Kubernetes
Ara Pulido, Technical Evangelist at Datadog
Data in Motion: Building Stream-Based Architectures with Qlik Replicate & Kaf...
The challenge with today’s “data explosion” is finding the most appropriate answer to the question, “So where do I put my data?” while avoiding the longer-term problem: data warehouses, data lakes, cloud storage, NoSQL databases, … are often the places where “big” data goes to die.
Enter Physics 101, and my corollary to Newton’s First Law of Motion:
Data in motion tends to stay in motion until it comes rest on disk. Similarly, if data is at rest, it will remain at rest until an external “force” puts it in motion again.
Data inevitably comes to rest at some point. Without “external forces”, data often gets lost or becomes stale where it lands. “Modern” architectures tend to involve data pipelines where downstream consumers of data make use of data generated upstream, often with built-for-purpose repositories at each stage. This session will explore how data that has come to rest can be put in motion again; how Kafka can keep it in motion longer; and how pipelined architectures might be created to make use of that data.
Kafka and Kafka Streams in the Global Schibsted Data Platform
In this talk we will present how we in Schibsted have set up a new global streaming data platform using Kafka and Kafka Streams, replacing a homegrown solution based on Kinesis and micro batches in Amazon S3.
Talk presented at Kafka Summit 2018 in San Francisco: https://kafka-summit.org/sessions/kafka-kafka-streams-global-schibsted-data-platform/
Lessons from the field: Catalog of Kafka Deployments | Joseph Niemiec, Cloudera
Streaming architectures have been on the rise steadily and as a result, we have seen the adoption of Kafka go up too. With the diverse spread of use cases across multiple industries, we have seen a variety of Kafka deployments across our hundreds of Kafka customers. Along the way, we have learnt some best practices as well as what not to do in mission-critical architectures. Join Joe Niemiec, Sr. Product Manager at Cloudera, as he shares these insights in this session that covers topics such as - The many ways that Kafka has been deployed in the field Standalone clusters, multiple clusters in a single data center and multiple clusters geographically distributed performing replication Clusters of all sizes small and large, few messages to hundreds of thousands per second Discussion about architecture failure domains Configurations tuned and used in specific deployments
Building Microservices with Apache Kafka by Colin McCabe
Abstract:- Building distributed systems is challenging. Luckily, Apache Kafka provides a powerful toolkit for putting together big services as a set of scalable, decoupled components. In this talk, I'll describe some of the design tradeoffs when building microservices, and how Kafka's powerful abstractions can help. I'll also talk a little bit about what the community has been up to with Kafka Streams, Kafka Connect, and exactly-once semantics.
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Mainframe Integration, Offloading and Replacement with Apache Kafka | Kai Wae...
Mainframe Integration, Offloading and Replacement with Apache Kafka | Kai Wae...
Using Kafka as a Database For Real-Time Transaction Processing | Chad Preisle...
Using Kafka as a Database For Real-Time Transaction Processing | Chad Preisle...
Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, Preset
Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, Preset
Taming a massive fleet of Python-based Kafka apps at Robinhood | Chandra Kuch...
Taming a massive fleet of Python-based Kafka apps at Robinhood | Chandra Kuch...
Moving 150 TB of data resiliently on Kafka With Quorum Controller on Kubernet...
Moving 150 TB of data resiliently on Kafka With Quorum Controller on Kubernet...
Accelerating Innovation with Apache Kafka, Heikki Nousiainen | Heikki Nousiai...
Accelerating Innovation with Apache Kafka, Heikki Nousiainen | Heikki Nousiai...
Kafka at the core of an AIOps pipeline | Sunanda Kommula, Selector.ai and Ala...
Kafka at the core of an AIOps pipeline | Sunanda Kommula, Selector.ai and Ala...
Feed Your SIEM Smart with Kafka Connect (Vitalii Rudenskyi, McKesson Corp) Ka...
Feed Your SIEM Smart with Kafka Connect (Vitalii Rudenskyi, McKesson Corp) Ka...
5 lessons learned for successful migration to Confluent cloud | Natan Silinit...
5 lessons learned for successful migration to Confluent cloud | Natan Silinit...
Nordstrom's Event-Sourced Architecture and Kafka-as-a-Service | Adam Weyant a...
Nordstrom's Event-Sourced Architecture and Kafka-as-a-Service | Adam Weyant a...
DataOps Automation for a Kafka Streaming Platform (Andrew Stevenson + Spiros ...
DataOps Automation for a Kafka Streaming Platform (Andrew Stevenson + Spiros ...
Extracting Value from IOT using Azure Cosmos DB, Azure Synapse Analytics and ...
Extracting Value from IOT using Azure Cosmos DB, Azure Synapse Analytics and ...
Developing custom transformation in the Kafka connect to minimize data redund...
Developing custom transformation in the Kafka connect to minimize data redund...
Streaming data in the cloud with Confluent and MongoDB Atlas | Robert Waters,...
Streaming data in the cloud with Confluent and MongoDB Atlas | Robert Waters,...
apidays LIVE New York 2021 - Service reliability through autoscaling workload...
apidays LIVE New York 2021 - Service reliability through autoscaling workload...
Data in Motion: Building Stream-Based Architectures with Qlik Replicate & Kaf...
Data in Motion: Building Stream-Based Architectures with Qlik Replicate & Kaf...
Kafka and Kafka Streams in the Global Schibsted Data Platform
Kafka and Kafka Streams in the Global Schibsted Data Platform
Lessons from the field: Catalog of Kafka Deployments | Joseph Niemiec, Cloudera
Lessons from the field: Catalog of Kafka Deployments | Joseph Niemiec, Cloudera
Building Microservices with Apache Kafka by Colin McCabe
Building Microservices with Apache Kafka by Colin McCabe
Similar to Azure Cosmos DB Kafka Connectors | Abinav Rameesh, Microsoft
Azure Labs: Confluent on Azure Container Services & Real-time Search with Red...
Azure Labs: Confluent on Azure Container Services & Real-time Search with Redis | Alicia Moniz, Confluent and Ramya Orunganti, Microsoft
Kafka Summit SF 2017 - Kafka Connect Best Practices – Advice from the Field
Presented at Kafka Summit SF by Randall Hauch
Data Pipelines with Kafka Connect
In this presentation we describe the design and implementation of Kafka Connect, Kafka’s new tool for scalable, fault-tolerant data import and export. First we’ll discuss some existing tools in the space and why they fall short when applied to data integration at large scale. Next, we will explore Kafka Connect’s design and how it compares to systems with similar goals, discussing key design decisions that trade off between ease of use for connector developers, operational complexity, and reuse of existing connectors. Finally, we’ll discuss how standardizing on Kafka Connect can ultimately lead to simplifying your entire data pipeline, making ETL into your data warehouse and enabling stream processing applications as simple as adding another Kafka connector.
How Kafka and MemSQL Became the Dynamic Duo (Sarung Tripathi, MemSQL) Kafka S...
Kafka and MemSQL are the perfect combination of speed, scale, and power to take on the world’s most complex operational analytics challenges. In this session, you will learn how Kafka and MemSQL have become the dynamic duo, and how you can use them together to achieve ingest of tens of millions of records per second and enable highly concurrent, real-time analytics. In the last few months, Kafka and MemSQL have been hard at work, devising a plan to take on the world’s next set of streaming data challenges. So stay tuned: there may just be an announcement!
Build on AWS: Migrating And Platforming
Adrian De Luca, Head of Solution Architecture, APAC presenting on Migration and Platforming on AWS during AWS TechShift.
Build on AWS: Migrating and Platforming
AWS presents 'Build on AWS: Migrating and Platforming' at AWS TechShift, Sydney.
Adam Lynch, Partner Solutions Architect, AWS
Kafka connect 101
Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector configuration for common source and sink Connectors.
Actual CCDAK Questions with Practice Tests and braindumps
The CCDAK (Confluent Certified Developer for Apache Kafka) exam is designed for individuals who want to validate their expertise in developing applications using Apache Kafka and the Confluent Platform. This certification demonstrates your proficiency in designing, developing, and deploying Kafka-based solutions.
Exam Objectives:
The CCDAK exam assesses your knowledge and skills in various aspects of Apache Kafka and the Confluent Platform. The following are the key exam objectives:
Kafka Core Concepts:
- Understand the architecture of Apache Kafka.
- Know the key components, such as brokers, topics, partitions, and consumer groups.
- Demonstrate knowledge of Kafka messaging semantics, including ordering, at-least-once delivery, and message retention.
Kafka APIs and Clients:
- Understand how to use Kafka APIs to produce and consume messages.
- Demonstrate proficiency in using Kafka clients in different programming languages.
- Implement features such as message compression, serialization, and authentication.
Kafka Connect:
- Understand the purpose and architecture of Kafka Connect.
- Know how to configure and use Kafka Connect connectors.
- Demonstrate knowledge of common Kafka Connect use cases.
Kafka Streams:
- Understand the core concepts and architecture of Kafka Streams.
- Demonstrate proficiency in building stream processing applications using Kafka Streams.
- Implement common stream transformations, aggregations, and windowing operations.
Schema Management and the Confluent Schema Registry:
- Understand the importance of schema management in Kafka.
- Know how to use the Confluent Schema Registry for schema evolution and compatibility.
- Demonstrate knowledge of Avro schemas and their usage in Kafka.
Exam Tips:
Study the Documentation: The official documentation of Apache Kafka and the Confluent Platform is a valuable resource for exam preparation. Familiarize yourself with the key concepts, features, and APIs. Pay special attention to topics related to Kafka Connect, Kafka Streams, and schema management.
Hands-on Experience: Practical experience is crucial for success in the CCDAK exam. Set up a local Kafka cluster and work on real-world projects to gain hands-on experience with the various components and features of Kafka. This will help you better understand the concepts and reinforce your learning.
Exam Preparation Resources: You should use authentic and up to date exam preparation resources like killexams.com. By registering at killexams.com you will be able to download CCDAK question bank that contains up to date PDF and practice tests that will help you improve your knowledge and pass your exam. These resources are listed as under;
PDF Download: https://killexams.com/demo-download/CCDAK.pdf
Full Version: https://killexams.com/exam-price-comparison/CCDAK
CCDAK Exam Detail: https://killexams.com/pass4sure/exam-detail/CCDAK
Practice Killexams Questions: Practice killexams CCDAK questions that cover the exam objectives.
Confluent Enterprise Datasheet
Get an overview of the Confluent complete solution for real-time streams. Visit www.confluent.io for more information.
New Features in Confluent Platform 6.0 / Apache Kafka 2.6
New Features in Confluent Platform 6.0 / Apache Kafka 2.6, including REST Proxy and API, Tiered Storage for AWS S3 and GCP GCS, Cluster Linking (On-Premise, Edge, Hybrid, Multi-Cloud), Self-Balancing Clusters), ksqlDB.
🏗️Improve database performance with connection pooling and load balancing tec...
🏗️Improve database performance with connection pooling and load balancing techniques
What is Apache Kafka®?
How Apache Kafka is similar to databases you are probably already used to using. Specifically PostgreSQL and MongoDB.
What is apache Kafka?
How Apache Kafka is similar to databases you are probably already used to using. Specifically PostgreSQL and MongoDB
AWS re:Invent 2016: How to Launch a 100K-User Corporate Back Office with Micr...
Learn how to build a scalable, compliance-ready, and automated deployment of the Microsoft “backoffice” servers for 100K users running on AWS. In this session, we show a reference architecture deployment of Exchange, SharePoint, Skype for Business, SQL Server and Active Directory in a single VPC. We discuss the following: (1) how the solution is automated for 100K users, (2) how the solution is enabled for compliance (e.g., FedRAMP, HIPAA, PCI), and (3) how the solution is built from modular 10K user blocks. Attendees should have knowledge of AWS CloudFormation, PowerShell, instance bootstrapping, VPCs, and Amazon Route 53, as well as the relevant Microsoft technologies.
Apache kafka
Apache kafka is a scalable, fast, durable, and fault-tolerant subscribe messaging system on later stage.
Applying ML on your Data in Motion with AWS and Confluent | Joseph Morais, Co...
Event-driven application architectures are becoming increasingly common as a large number of users demand more interactive, real-time, and intelligent responses. Yet it can be challenging to decide how to capture and perform real-time data analysis and deliver differentiating experiences. Join experts from Confluent and AWS to learn how to build Apache Kafka®-based streaming applications backed by machine learning models. Adopting the recommendations will help you establish repeatable patterns for high performing event-based apps.
Similar to Azure Cosmos DB Kafka Connectors | Abinav Rameesh, Microsoft (20)
Azure Labs: Confluent on Azure Container Services & Real-time Search with Red...
Azure Labs: Confluent on Azure Container Services & Real-time Search with Red...
Kafka Summit SF 2017 - Kafka Connect Best Practices – Advice from the Field
Kafka Summit SF 2017 - Kafka Connect Best Practices – Advice from the Field
How Kafka and MemSQL Became the Dynamic Duo (Sarung Tripathi, MemSQL) Kafka S...
How Kafka and MemSQL Became the Dynamic Duo (Sarung Tripathi, MemSQL) Kafka S...
Actual CCDAK Questions with Practice Tests and braindumps
Actual CCDAK Questions with Practice Tests and braindumps
New Features in Confluent Platform 6.0 / Apache Kafka 2.6
New Features in Confluent Platform 6.0 / Apache Kafka 2.6
Why you should(n't) run your databases in the cloud
Why you should(n't) run your databases in the cloud
🏗️Improve database performance with connection pooling and load balancing tec...
🏗️Improve database performance with connection pooling and load balancing tec...
AWS re:Invent 2016: How to Launch a 100K-User Corporate Back Office with Micr...
AWS re:Invent 2016: How to Launch a 100K-User Corporate Back Office with Micr...
Applying ML on your Data in Motion with AWS and Confluent | Joseph Morais, Co...
Applying ML on your Data in Motion with AWS and Confluent | Joseph Morais, Co...
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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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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As presented at the Agile Venture Bolzano, 4.06.2024
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
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While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Azure Cosmos DB Kafka Connectors | Abinav Rameesh, Microsoft
- 1. Cosmos DB – Kafka Connectors Abinav Rameesh Program Manager, Cosmos DB
- 2. 01 Kafka Connect Overview 02 Kafka Integration Use Cases for Cosmos DB 03 Cosmos DB Source & Sink Architecture Overview 04 Demo 05 Taking It Further
- 3. What is a Connector? Confluent Platform offers 120+ pre-built connectors to help you quickly and reliably integrate with Apache Kafka®. Connectors import and export data from some of the most commonly used data systems. Connectors either run as a managed resource on Confluent Cloud or as a self managed resource on a self managed kafka cluster. Kafka Connect runs under the Java virtual machine (JVM) as a process known as a worker. Each worker can execute multiple connectors.
- 4. Connect Architecture •Connectors are responsible for the interaction between Kafka Connect and the external technology being integrated with •Converters handle the serialization and deserialization of data •Transformations can optionally apply one or more transformations to the data passing through the pipeline
- 5. Kafka Cosmos DB SHARDING SCALING CDC TUNABILITY MANAGED Kafka is horizontally partitioned with brokers serving as leaders and followers, each owning it’s own logical range of data. Kafka can be scaled seamlessly by simply increasing the number of brokers in the cluster. Kafka provides Consumer library to retrieve changes from each of the physical partitions of the topic. Kafka performance can be tuned for batch sizes, memory thresholds, polling frequencies etc. Kafka can be self provisioned or fully managed through Confluent Cloud. Cosmos DB is horizontally partitioned with partitions composing a replica set. Cosmos DB can be scaled elastically by simply increasing the throughput for dataset. Cosmos DB provides a Change Feed Processor, which contains inbuilt logic to retrieve changes from the physical partitions for the container. Cosmos DB can be tuned for query performance, RU consumption, batch sizes for writes and reads etc. Cosmos DB is a fully managed service.
- 6. 01 Source and Sink Connectors for Cosmos DB facilitate seamless integration without the need to write complex application code to migrate data to and from Kafka. SOURCE, SINK ZERO CODE DATA FORMATS FULLY MANAGED 02 Only configurations are needed to point to the Cosmos DB account and Kafka cluster with additional customization options. 03 JSON and AVRO data formats are supported with additional format options to come based on user feedback. 04 Fully managed (through Confluent Cloud) as well as self managed (using the connector directly) are available.
- 7. Cosmos DB – Kafka Use Cases
- 9. Cosmos DB – Kafka Connector Architecture …. …. Managed Kafka Connect Cluster (Source Connector) Change Feed Processor Kafka Producer Reading from Cosmos DB’s physical partitions Writing to the Kafka topic’s physical partitions
- 10. Cosmos DB – Kafka Connector Architecture …. Managed Kafka Connect Cluster (Sink Connector) Cosmos DB Java Client issuing writes to the Cosmos container Kafka Consumer pulling from the topic’s brokers Writing to Cosmos DB’s physical partitions ….
- 11. Demo