Blockchain and Kafka - A Modern Love Story | Suhavi Sandhu, Guidewire SoftwareHostedbyConfluent
After familiarizing myself with blockchain over the past couple years, I noticed a few caveats and setbacks that make blockchain a ‘not-so-ideal’ solution to record tracking in a P2P network. It’s not as scalable and it’s quite slow in a busy network. Enter Apache Kafka, with its high performance and immutable logging. In this talk, I want to explore the relationship between Blockchain and Kafka and demonstrate how the two technologies can benefit from each other. If you’re interested in the future of blockchain and love Kafka, this is definitely up your alley.
Leveraging Data in Motion | Jun Rao, Co-Founder, Confluent | Kafka Summit APA...HostedbyConfluent
This document discusses leveraging data in motion using Apache Kafka, especially in a cloud-first world. It notes that Kafka has become the de facto standard for data in motion and is used by over 100,000 organizations globally, including 60% of Fortune 100 companies. The document introduces Confluent Cloud, which re-engineers Kafka for the cloud, providing a fully-managed platform for data in motion that offers infinite storage, elastic scaling, and other advantages over self-managed Kafka deployments. Confluent Cloud aims to unify data at rest and data in motion by keeping real-time data in cloud-native Kafka for long periods of time.
Extracting Value from IOT using Azure Cosmos DB, Azure Synapse Analytics and ...HostedbyConfluent
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.
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...HostedbyConfluent
Apache Druid is a high-performance distributed analytics store for modern analytics applications. It supports ingesting millions of events per second and sub-second query processing. Druid supports various types of data sources for ingestion, including Apache Kafka. You can immediately query on stream events once they get ingested into Druid. Since Kafka provides scalable and robust data delivery while Druid supports advanced complex analysis on streams, Kafka and Druid are widely used together for BI and operational analytics use cases, which require interactivity, scalability, real-time, and performance.
This talk is based on our real-world experiences building out streaming analytics stacks powering production use cases across many industries.
Kafka & InfluxDB: BFFs for Enterprise Data Applications | Russ Savage, Influx...HostedbyConfluent
Modern data processing applications built on Kafka and InfluxDB deliver the performance, reliability, and flexibility that customers need for robust real-time data pipeline solutions. As the saying goes, the pipeline is greater than the sum of its Kafka and InfluxDB parts. In this session, Russ Savage, Director of Product Management at InfluxData will discuss basic concepts of integrating Kafka and InfluxDB while highlighting how companies are creating fault-tolerant, scalable and fast data pipelines with the power of InfluxDB and Kafka.
Kafka Summit NYC 2017 - The Rise of the Streaming Platformconfluent
The document discusses the rise of streaming platforms and Apache Kafka. It describes how Fortune 500 companies and major global banks, insurance, and telecom companies are adopting streaming platforms. It then discusses the technical capabilities of streaming platforms, including their abilities to store, process, and publish/subscribe to data in real-time at large scales. Finally, it envisions the future of streaming platforms and their potential to support a wide range of applications from databases and key-value stores to monitoring, search, data warehousing, Hadoop, stream processing, and real-time analytics on a single, open platform.
Use Apache Gradle to Build and Automate KSQL and Kafka Streams (Stewart Bryso...confluent
KSQL is an easy-to-use and easy-to-understand streaming SQL engine for Apache Kafka built on top of Kafka Streams. The ability to write streaming applications using only SQL makes Apache Kafka available to a whole range of new developers and potential use cases, either as a stand-alone solution, or as a single component to a broader Kafka Streams implementation. Inspired by a customer project now in production, experience the lifecycle of a streaming application developed using KSQL and Kafka Streams. With Apache Gradle as our build framework, we’ll explore the open-source Gradle plugin we built during this project to improve developer efficiency and automate the deployment of KSQL pipelines, user-defined functions, and Kafka Streams microservices.
We’ll demonstrate the deployment process live, and discuss design decisions around incorporating SQL-based processes into an overall streaming application.
Key Takeaways
1. KSQL is a natural choice for expressing data-driven applications, but it may not naturally fit into established DevOps processes and automations.
2. We built an open-source Gradle plugin to handle all aspects of deploying a Kafka-based streaming application: KSQL pipelines, KSQL user-defined functions, and Kafka Streams microservices.
3. KSQL pipelines can be deployed using either a server start script, or the KSQL REST API, and our Gradle plugin fully supports both options.
Confluent Cloud for Apache Kafka® | Google Cloud Next ’19confluent
Google Cloud Next ’19
Speakers:
Gaetan Castelein, Confluent Product Marketing
Kir Titievsky, Google Product Management
Confluent Cloud for Apache Kafka® was a session conducted at Google Cloud Next ’19 on the topic of how Confluent and Google are partnering to give you a complete event-streaming platform that extends Kafka with essential capabilities for developers and enterprises. Confluent is available as a fully managed, first class service on GCP, or can be deployed on-premises on Google Cloud Services Platform. Developers can deploy Confluent Cloud™ in minutes right from the Google Cloud Console to start building event-driven applications. Enterprises can build hybrid cloud streaming solutions with a common platform that spans from on-premises to GCP, streaming data to GCP to leverage best-of-breed services such as BigQuery and TensorFlow. Review this presentation to learn about Confluent and GCP services, and see how you can get started in just minutes with no upfront commitment.
Blockchain and Kafka - A Modern Love Story | Suhavi Sandhu, Guidewire SoftwareHostedbyConfluent
After familiarizing myself with blockchain over the past couple years, I noticed a few caveats and setbacks that make blockchain a ‘not-so-ideal’ solution to record tracking in a P2P network. It’s not as scalable and it’s quite slow in a busy network. Enter Apache Kafka, with its high performance and immutable logging. In this talk, I want to explore the relationship between Blockchain and Kafka and demonstrate how the two technologies can benefit from each other. If you’re interested in the future of blockchain and love Kafka, this is definitely up your alley.
Leveraging Data in Motion | Jun Rao, Co-Founder, Confluent | Kafka Summit APA...HostedbyConfluent
This document discusses leveraging data in motion using Apache Kafka, especially in a cloud-first world. It notes that Kafka has become the de facto standard for data in motion and is used by over 100,000 organizations globally, including 60% of Fortune 100 companies. The document introduces Confluent Cloud, which re-engineers Kafka for the cloud, providing a fully-managed platform for data in motion that offers infinite storage, elastic scaling, and other advantages over self-managed Kafka deployments. Confluent Cloud aims to unify data at rest and data in motion by keeping real-time data in cloud-native Kafka for long periods of time.
Extracting Value from IOT using Azure Cosmos DB, Azure Synapse Analytics and ...HostedbyConfluent
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.
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...HostedbyConfluent
Apache Druid is a high-performance distributed analytics store for modern analytics applications. It supports ingesting millions of events per second and sub-second query processing. Druid supports various types of data sources for ingestion, including Apache Kafka. You can immediately query on stream events once they get ingested into Druid. Since Kafka provides scalable and robust data delivery while Druid supports advanced complex analysis on streams, Kafka and Druid are widely used together for BI and operational analytics use cases, which require interactivity, scalability, real-time, and performance.
This talk is based on our real-world experiences building out streaming analytics stacks powering production use cases across many industries.
Kafka & InfluxDB: BFFs for Enterprise Data Applications | Russ Savage, Influx...HostedbyConfluent
Modern data processing applications built on Kafka and InfluxDB deliver the performance, reliability, and flexibility that customers need for robust real-time data pipeline solutions. As the saying goes, the pipeline is greater than the sum of its Kafka and InfluxDB parts. In this session, Russ Savage, Director of Product Management at InfluxData will discuss basic concepts of integrating Kafka and InfluxDB while highlighting how companies are creating fault-tolerant, scalable and fast data pipelines with the power of InfluxDB and Kafka.
Kafka Summit NYC 2017 - The Rise of the Streaming Platformconfluent
The document discusses the rise of streaming platforms and Apache Kafka. It describes how Fortune 500 companies and major global banks, insurance, and telecom companies are adopting streaming platforms. It then discusses the technical capabilities of streaming platforms, including their abilities to store, process, and publish/subscribe to data in real-time at large scales. Finally, it envisions the future of streaming platforms and their potential to support a wide range of applications from databases and key-value stores to monitoring, search, data warehousing, Hadoop, stream processing, and real-time analytics on a single, open platform.
Use Apache Gradle to Build and Automate KSQL and Kafka Streams (Stewart Bryso...confluent
KSQL is an easy-to-use and easy-to-understand streaming SQL engine for Apache Kafka built on top of Kafka Streams. The ability to write streaming applications using only SQL makes Apache Kafka available to a whole range of new developers and potential use cases, either as a stand-alone solution, or as a single component to a broader Kafka Streams implementation. Inspired by a customer project now in production, experience the lifecycle of a streaming application developed using KSQL and Kafka Streams. With Apache Gradle as our build framework, we’ll explore the open-source Gradle plugin we built during this project to improve developer efficiency and automate the deployment of KSQL pipelines, user-defined functions, and Kafka Streams microservices.
We’ll demonstrate the deployment process live, and discuss design decisions around incorporating SQL-based processes into an overall streaming application.
Key Takeaways
1. KSQL is a natural choice for expressing data-driven applications, but it may not naturally fit into established DevOps processes and automations.
2. We built an open-source Gradle plugin to handle all aspects of deploying a Kafka-based streaming application: KSQL pipelines, KSQL user-defined functions, and Kafka Streams microservices.
3. KSQL pipelines can be deployed using either a server start script, or the KSQL REST API, and our Gradle plugin fully supports both options.
Confluent Cloud for Apache Kafka® | Google Cloud Next ’19confluent
Google Cloud Next ’19
Speakers:
Gaetan Castelein, Confluent Product Marketing
Kir Titievsky, Google Product Management
Confluent Cloud for Apache Kafka® was a session conducted at Google Cloud Next ’19 on the topic of how Confluent and Google are partnering to give you a complete event-streaming platform that extends Kafka with essential capabilities for developers and enterprises. Confluent is available as a fully managed, first class service on GCP, or can be deployed on-premises on Google Cloud Services Platform. Developers can deploy Confluent Cloud™ in minutes right from the Google Cloud Console to start building event-driven applications. Enterprises can build hybrid cloud streaming solutions with a common platform that spans from on-premises to GCP, streaming data to GCP to leverage best-of-breed services such as BigQuery and TensorFlow. Review this presentation to learn about Confluent and GCP services, and see how you can get started in just minutes with no upfront commitment.
Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...HostedbyConfluent
Studying the ""how"" of Kafka makes you better at using Kafka, but studying its ""whys"" makes you better at so much more. In looking at the tradeoffs behind a system like Kafka, we learn to reason more clearly about distributed systems and to make high-stakes technology adoption decisions more effectively. These are skills we all want to improve!
In this talk, we'll examine trade-offs on which our favorite distributed messaging system takes opinionated positions:
- Whether to store data contiguously or using an index
- How many storage tiers are best?
- Where should metadata live?
- And more.
It's always useful to dissect a modern distributed system with the goal of understanding it better, and it's even better to learn to deeper architectural principles in the process. Come to this talk for a generous helping of both.
Bridge to Cloud: Using Apache Kafka to Migrate to GCPconfluent
Watch this talk here: https://www.confluent.io/online-talks/bridge-to-cloud-apache-kafka-migrate-gcp
Most companies start their cloud journey with a new use case, or a new application. Sometimes these applications can run independently in the cloud, but often times they need data from the on premises datacenter. Existing applications will slowly migrate, but will need a strategy and the technology to enable a multi-year migration.
In this session, we will share how companies around the world are using Confluent Cloud, a fully managed Apache Kafka® service, to migrate to Google Cloud Platform. By implementing a central-pipeline architecture using Apache Kafka to sync on-prem and cloud deployments, companies can accelerate migration times and reduce costs.
Register now to learn:
-How to take the first step in migrating to GCP
-How to reliably sync your on premises applications using a persistent bridge to cloud
-How Confluent Cloud can make this daunting task simple, reliable and performant
TBD Data Governance | David Araujo and Michael Agnich, Confluent HostedbyConfluent
The document discusses Confluent Stream Governance, a solution for governing data in motion with metadata. It introduces tools for managing schemas, classifying metadata, tracking lineage, and monitoring data quality. This helps bring order to what would otherwise be a "giant mess" of ungoverned data by enforcing standards and providing visibility into data flows and definitions.
Technical Deep Dive: Using Apache Kafka to Optimize Real-Time Analytics in Fi...confluent
Watch this talk here: https://www.confluent.io/online-talks/using-apache-kafka-to-optimize-real-time-analytics-financial-services-iot-applications
When it comes to the fast-paced nature of capital markets and IoT, the ability to analyze data in real time is critical to gaining an edge. It’s not just about the quantity of data you can analyze at once, it’s about the speed, scale, and quality of the data you have at your fingertips.
Modern streaming data technologies like Apache Kafka and the broader Confluent platform can help detect opportunities and threats in real time. They can improve profitability, yield, and performance. Combining Kafka with Panopticon visual analytics provides a powerful foundation for optimizing your operations.
Use cases in capital markets include transaction cost analysis (TCA), risk monitoring, surveillance of trading and trader activity, compliance, and optimizing profitability of electronic trading operations. Use cases in IoT include monitoring manufacturing processes, logistics, and connected vehicle telemetry and geospatial data.
This online talk will include in depth practical demonstrations of how Confluent and Panopticon together support several key applications. You will learn:
-Why Apache Kafka is widely used to improve performance of complex operational systems
-How Confluent and Panopticon open new opportunities to analyze operational data in real time
-How to quickly identify and react immediately to fast-emerging trends, clusters, and anomalies
-How to scale data ingestion and data processing
-Build new analytics dashboards in minutes
Real-Time Market Data Analytics Using Kafka Streamsconfluent
(Lei Chen, Bloomberg, L.P.) Kafka Summit SF 2018
At Bloomberg, we are building a streaming platform with Apache Kafka, Kafka Streams and Spark Streaming to handle high volume, real-time processing with rapid derivative market data. In this talk, we’ll share the experience of how we utilize Kafka Streams Processor API to build pipelines that are capable of handling millions of market movements per second with ultra-low latency, as well as performing complex analytics like outlier detection, source confidence evaluation (scoring), arbitrage detection and other financial-related processing.
We’ll cover:
-Our system architecture
-Best practices of using the Processor API and State Store API
-Dynamic gap session implementation
-Historical data re-processing practice in KStreams app
-Chaining multiple KStreams apps with Spark Streaming job
Event: https://www.meetup.com/de-DE/Vienna-Kafka-meetup/events/262314643/
Speaker: Patrik Kleindl (patrik.kleindl@bearingpoint.com)
Slides of the introduction to Apache Kafka and some popular use cases.
Slides were provided by Confluent (confluent.io)
Testing Event Driven Architectures: How to Broker the Complexity | Frank Kilc...HostedbyConfluent
This document discusses testing event-driven architectures. It begins by defining common event-driven architecture patterns like event notifications and event sourcing. It then discusses brokering the complexity of event-driven architectures by describing how events are communicated between producers and consumers via channels. The document outlines what information should be included in events like payloads and headers. It also discusses the difference between orchestration and choreography in event-driven systems. It provides an example of how events can be used to mediate changes within a system using order validation. Finally, it demonstrates how to test event-driven architectures using specifications and discusses accelerating API quality through testing tools that support multiple protocols and definitions.
Elastically Scaling Kafka Using Confluentconfluent
This document discusses how Confluent Platform provides elastic scaling for Apache Kafka. It offers fully managed cloud services through Confluent Cloud or self-managed software. Confluent Cloud allows users to easily scale Kafka workloads from 0 MBps to GBps without complex provisioning. It also offers pay-for-use pricing where customers only pay for the data streamed, with the ability to scale to zero. For self-managed deployments, Confluent Platform enables dynamic scaling of Kafka clusters on Kubernetes through features like tiered storage and self-balancing clusters that can rebalance partitions in seconds versus hours for other Kafka services.
Building a Codeless Log Pipeline w/ Confluent Sink Connector | Pollyanna Vale...HostedbyConfluent
Kubernetes became the de-facto standard for running cloud-native applications. And many users turn to it also to run stateful applications such as Apache Kafka. You can use different tools to deploy Kafka on Kubernetes - write your own YAML files, use Helm Charts, or go for one of the available operators. But there is one thing all of these have in common. You still need very good knowledge of Kubernetes to make sure your Kafka cluster works properly in all situations. This talk will cover different Kubernetes features such as resources, affinity, tolerations, pod disruption budgets, topology spread constraints and more. And it will explain why they are important for Apache Kafka and how to use them. If you are interested in running Kafka on Kubernetes and do not know all of these, this is a talk for you.
Data Mess to Data Mesh | Jay Kreps, CEO, Confluent | Kafka Summit Americas 20...HostedbyConfluent
Companies are increasingly becoming software-driven, requiring new approaches to software architecture and data integration. The "data mesh" architectural pattern decentralizes data management by organizing it around domain experts and treating data as products that can be accessed on-demand. This helps address issues with centralized data warehouses by evolving data modeling with business needs, avoiding bottlenecks, and giving autonomy to domain teams. Key principles of the data mesh include domain ownership of data, treating data as self-service products, and establishing federated governance to coordinate the decentralized system.
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.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
Apache Kafka® and Analytics in a Connected IoT Worldconfluent
Apache Kafka® and Analytics in a Connected IoT World, Kai Waehner, Sr. Solutions Engineer Advanced Technology Group, Confluent
https://www.meetup.com/Berlin-Apache-Kafka-Meetup-by-Confluent/events/273166575/
Sub-Second SQL Search, Aggregations and Joins with Kafka and Rockset | Dhruba...HostedbyConfluent
We often need to build applications that analyze Kafka data to unlock the most value from event streams, so how can organizations build these real-time analytics applications? In this talk, we examine an indexing approach that enables fast SQL analytics on data from Kafka, without data flattening or denormalization. Rockset is the real-time indexing database that builds an inverted index, a columnar index and a row index on all fields of your Kafka messages, including nested fields and arrays. This Converged Index accelerates various types of analytic queries–search, aggregations and joins–without the need to denormalize or transform data for performance reasons. With indexing delivering significant gains in query performance, we also need to index new data in a timely manner. We discuss several strategies used for efficient ingestion and indexing from Kafka, including rollups, write optimizations on the underlying RocksDB storage engine, and the disaggregation of ingest and query compute.
How Apache Kafka helps to create Data Culture – How to Cross the Kafka Chasmconfluent
In this webinar we want to share our experience on how the Swiss Mobiliar, the biggest Swiss household insurance enterprise, introduced Kafka and led it to enterprise-wide adoption with the help of AGOORA.com.
Maximizing the Capabilities of Kafka – Real-Time Streaming of Event-Driven Da...HostedbyConfluent
Push Technology, a verified gold technology partner, developed the Diffusion Kafka Adapter to meet the evolving requirements of the Kafka community. Diffusion is an Intelligent Event-Data Platform. Using Push’s Kafka Adapter, developers extend Kafka solutions efficiently and securely over the Internet, streaming real-time, event-driven data to millions of end-user apps and IoT devices. The adapter automatically maps Kafka message types to JSON, allowing web, mobile, and IoT clients to securely consume the data stored as rich data structures within Kafka. The adapter enables developers to configure how imported topics are structured, and to import topics that match a regular expression. The adapter can translate data from Kafka topics to Diffusion topics, and from Diffusion to Kafka. The adapter is designed it to make it quick and easy to integrate Kafka with Diffusion. The presentation will provide real-world examples of how the adapter is used to power Kafka in an event-driven world.
Choose Right Stream Storage: Amazon Kinesis Data Streams vs MSKSungmin Kim
This presentation compares Amazon Kinesis Data Streams to Managed Streaming for Kafka (MSK) in both architectural perspective and operational perspective. In addition, it shows common architectural patterns: (1) Data Hub: Event-Bus, (2) Log Aggregation, (3) IoT, (4) Event sourcing and CQRS.
Chris D'Agostino | Kafka Summit 2018 Keynote (Building an Enterprise Streamin...confluent
This document summarizes Chris D'Agostino's presentation on enabling real-time event processing at scale. The presentation covers event-based architecture, self-service streaming and data governance, and complex event processing (CEP) and IFTTT capabilities. It discusses goals like data democracy, shared infrastructure, and making data tools and platforms user-centered. It also provides examples of how their streaming data platform supports features like stream design and management, data validation, and automatic data enrichment.
Building a Streaming Pipeline on Kubernetes Using Kafka Connect, KSQLDB & Apa...HostedbyConfluent
Managing Apache Kafka sometimes could be cumbersome, and that's something that we would like to avoid, especially for developers and data engineers that need to build and develop data pipelines.
Luckily, Kubernetes and Kafka's combination helps us reduce everyday tasks tremendously by adding myriad capabilities to lessen the complexity of managing clusters.
Kafka Connect and KSQLDB are a fantastic combo to add to your streaming stack. These two soldiers can facilitate data acquisition and processing and also provide outstanding real-time ETL capabilities. But what if you need an OLAP datastore to answer complex queries with a low-latency response, that's where Apache Pinot comes to play.
At this session, you're going to learn:
- Effective Kafka deployment on Kubernetes
- How to properly configure Kafka Connect and KSQLDB
- Integrate Apache Pinot to answer OLAP queries
The document provides an agenda for the Government Track at the Kafka Summit 2021. The agenda includes sessions on topics like improving veteran benefit services through efficient data streaming, Kafka migration for satellite event streaming data, Kafka powered near real-time data pipelines at extreme scale, transformation during a global pandemic, securing the message bus with Kafka streams, Kafka for connected vehicle research, and driving a digital thread program in manufacturing with Apache Kafka. Speakers include representatives from Booz Allen Hamilton, ASRC Federal, University of California San Diego, Confluent, Raft LLC, Leidos, Ohio Department of Transportation, and Mercury Systems.
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
"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."
"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"
Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...HostedbyConfluent
Studying the ""how"" of Kafka makes you better at using Kafka, but studying its ""whys"" makes you better at so much more. In looking at the tradeoffs behind a system like Kafka, we learn to reason more clearly about distributed systems and to make high-stakes technology adoption decisions more effectively. These are skills we all want to improve!
In this talk, we'll examine trade-offs on which our favorite distributed messaging system takes opinionated positions:
- Whether to store data contiguously or using an index
- How many storage tiers are best?
- Where should metadata live?
- And more.
It's always useful to dissect a modern distributed system with the goal of understanding it better, and it's even better to learn to deeper architectural principles in the process. Come to this talk for a generous helping of both.
Bridge to Cloud: Using Apache Kafka to Migrate to GCPconfluent
Watch this talk here: https://www.confluent.io/online-talks/bridge-to-cloud-apache-kafka-migrate-gcp
Most companies start their cloud journey with a new use case, or a new application. Sometimes these applications can run independently in the cloud, but often times they need data from the on premises datacenter. Existing applications will slowly migrate, but will need a strategy and the technology to enable a multi-year migration.
In this session, we will share how companies around the world are using Confluent Cloud, a fully managed Apache Kafka® service, to migrate to Google Cloud Platform. By implementing a central-pipeline architecture using Apache Kafka to sync on-prem and cloud deployments, companies can accelerate migration times and reduce costs.
Register now to learn:
-How to take the first step in migrating to GCP
-How to reliably sync your on premises applications using a persistent bridge to cloud
-How Confluent Cloud can make this daunting task simple, reliable and performant
TBD Data Governance | David Araujo and Michael Agnich, Confluent HostedbyConfluent
The document discusses Confluent Stream Governance, a solution for governing data in motion with metadata. It introduces tools for managing schemas, classifying metadata, tracking lineage, and monitoring data quality. This helps bring order to what would otherwise be a "giant mess" of ungoverned data by enforcing standards and providing visibility into data flows and definitions.
Technical Deep Dive: Using Apache Kafka to Optimize Real-Time Analytics in Fi...confluent
Watch this talk here: https://www.confluent.io/online-talks/using-apache-kafka-to-optimize-real-time-analytics-financial-services-iot-applications
When it comes to the fast-paced nature of capital markets and IoT, the ability to analyze data in real time is critical to gaining an edge. It’s not just about the quantity of data you can analyze at once, it’s about the speed, scale, and quality of the data you have at your fingertips.
Modern streaming data technologies like Apache Kafka and the broader Confluent platform can help detect opportunities and threats in real time. They can improve profitability, yield, and performance. Combining Kafka with Panopticon visual analytics provides a powerful foundation for optimizing your operations.
Use cases in capital markets include transaction cost analysis (TCA), risk monitoring, surveillance of trading and trader activity, compliance, and optimizing profitability of electronic trading operations. Use cases in IoT include monitoring manufacturing processes, logistics, and connected vehicle telemetry and geospatial data.
This online talk will include in depth practical demonstrations of how Confluent and Panopticon together support several key applications. You will learn:
-Why Apache Kafka is widely used to improve performance of complex operational systems
-How Confluent and Panopticon open new opportunities to analyze operational data in real time
-How to quickly identify and react immediately to fast-emerging trends, clusters, and anomalies
-How to scale data ingestion and data processing
-Build new analytics dashboards in minutes
Real-Time Market Data Analytics Using Kafka Streamsconfluent
(Lei Chen, Bloomberg, L.P.) Kafka Summit SF 2018
At Bloomberg, we are building a streaming platform with Apache Kafka, Kafka Streams and Spark Streaming to handle high volume, real-time processing with rapid derivative market data. In this talk, we’ll share the experience of how we utilize Kafka Streams Processor API to build pipelines that are capable of handling millions of market movements per second with ultra-low latency, as well as performing complex analytics like outlier detection, source confidence evaluation (scoring), arbitrage detection and other financial-related processing.
We’ll cover:
-Our system architecture
-Best practices of using the Processor API and State Store API
-Dynamic gap session implementation
-Historical data re-processing practice in KStreams app
-Chaining multiple KStreams apps with Spark Streaming job
Event: https://www.meetup.com/de-DE/Vienna-Kafka-meetup/events/262314643/
Speaker: Patrik Kleindl (patrik.kleindl@bearingpoint.com)
Slides of the introduction to Apache Kafka and some popular use cases.
Slides were provided by Confluent (confluent.io)
Testing Event Driven Architectures: How to Broker the Complexity | Frank Kilc...HostedbyConfluent
This document discusses testing event-driven architectures. It begins by defining common event-driven architecture patterns like event notifications and event sourcing. It then discusses brokering the complexity of event-driven architectures by describing how events are communicated between producers and consumers via channels. The document outlines what information should be included in events like payloads and headers. It also discusses the difference between orchestration and choreography in event-driven systems. It provides an example of how events can be used to mediate changes within a system using order validation. Finally, it demonstrates how to test event-driven architectures using specifications and discusses accelerating API quality through testing tools that support multiple protocols and definitions.
Elastically Scaling Kafka Using Confluentconfluent
This document discusses how Confluent Platform provides elastic scaling for Apache Kafka. It offers fully managed cloud services through Confluent Cloud or self-managed software. Confluent Cloud allows users to easily scale Kafka workloads from 0 MBps to GBps without complex provisioning. It also offers pay-for-use pricing where customers only pay for the data streamed, with the ability to scale to zero. For self-managed deployments, Confluent Platform enables dynamic scaling of Kafka clusters on Kubernetes through features like tiered storage and self-balancing clusters that can rebalance partitions in seconds versus hours for other Kafka services.
Building a Codeless Log Pipeline w/ Confluent Sink Connector | Pollyanna Vale...HostedbyConfluent
Kubernetes became the de-facto standard for running cloud-native applications. And many users turn to it also to run stateful applications such as Apache Kafka. You can use different tools to deploy Kafka on Kubernetes - write your own YAML files, use Helm Charts, or go for one of the available operators. But there is one thing all of these have in common. You still need very good knowledge of Kubernetes to make sure your Kafka cluster works properly in all situations. This talk will cover different Kubernetes features such as resources, affinity, tolerations, pod disruption budgets, topology spread constraints and more. And it will explain why they are important for Apache Kafka and how to use them. If you are interested in running Kafka on Kubernetes and do not know all of these, this is a talk for you.
Data Mess to Data Mesh | Jay Kreps, CEO, Confluent | Kafka Summit Americas 20...HostedbyConfluent
Companies are increasingly becoming software-driven, requiring new approaches to software architecture and data integration. The "data mesh" architectural pattern decentralizes data management by organizing it around domain experts and treating data as products that can be accessed on-demand. This helps address issues with centralized data warehouses by evolving data modeling with business needs, avoiding bottlenecks, and giving autonomy to domain teams. Key principles of the data mesh include domain ownership of data, treating data as self-service products, and establishing federated governance to coordinate the decentralized system.
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.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
Apache Kafka® and Analytics in a Connected IoT Worldconfluent
Apache Kafka® and Analytics in a Connected IoT World, Kai Waehner, Sr. Solutions Engineer Advanced Technology Group, Confluent
https://www.meetup.com/Berlin-Apache-Kafka-Meetup-by-Confluent/events/273166575/
Sub-Second SQL Search, Aggregations and Joins with Kafka and Rockset | Dhruba...HostedbyConfluent
We often need to build applications that analyze Kafka data to unlock the most value from event streams, so how can organizations build these real-time analytics applications? In this talk, we examine an indexing approach that enables fast SQL analytics on data from Kafka, without data flattening or denormalization. Rockset is the real-time indexing database that builds an inverted index, a columnar index and a row index on all fields of your Kafka messages, including nested fields and arrays. This Converged Index accelerates various types of analytic queries–search, aggregations and joins–without the need to denormalize or transform data for performance reasons. With indexing delivering significant gains in query performance, we also need to index new data in a timely manner. We discuss several strategies used for efficient ingestion and indexing from Kafka, including rollups, write optimizations on the underlying RocksDB storage engine, and the disaggregation of ingest and query compute.
How Apache Kafka helps to create Data Culture – How to Cross the Kafka Chasmconfluent
In this webinar we want to share our experience on how the Swiss Mobiliar, the biggest Swiss household insurance enterprise, introduced Kafka and led it to enterprise-wide adoption with the help of AGOORA.com.
Maximizing the Capabilities of Kafka – Real-Time Streaming of Event-Driven Da...HostedbyConfluent
Push Technology, a verified gold technology partner, developed the Diffusion Kafka Adapter to meet the evolving requirements of the Kafka community. Diffusion is an Intelligent Event-Data Platform. Using Push’s Kafka Adapter, developers extend Kafka solutions efficiently and securely over the Internet, streaming real-time, event-driven data to millions of end-user apps and IoT devices. The adapter automatically maps Kafka message types to JSON, allowing web, mobile, and IoT clients to securely consume the data stored as rich data structures within Kafka. The adapter enables developers to configure how imported topics are structured, and to import topics that match a regular expression. The adapter can translate data from Kafka topics to Diffusion topics, and from Diffusion to Kafka. The adapter is designed it to make it quick and easy to integrate Kafka with Diffusion. The presentation will provide real-world examples of how the adapter is used to power Kafka in an event-driven world.
Choose Right Stream Storage: Amazon Kinesis Data Streams vs MSKSungmin Kim
This presentation compares Amazon Kinesis Data Streams to Managed Streaming for Kafka (MSK) in both architectural perspective and operational perspective. In addition, it shows common architectural patterns: (1) Data Hub: Event-Bus, (2) Log Aggregation, (3) IoT, (4) Event sourcing and CQRS.
Chris D'Agostino | Kafka Summit 2018 Keynote (Building an Enterprise Streamin...confluent
This document summarizes Chris D'Agostino's presentation on enabling real-time event processing at scale. The presentation covers event-based architecture, self-service streaming and data governance, and complex event processing (CEP) and IFTTT capabilities. It discusses goals like data democracy, shared infrastructure, and making data tools and platforms user-centered. It also provides examples of how their streaming data platform supports features like stream design and management, data validation, and automatic data enrichment.
Building a Streaming Pipeline on Kubernetes Using Kafka Connect, KSQLDB & Apa...HostedbyConfluent
Managing Apache Kafka sometimes could be cumbersome, and that's something that we would like to avoid, especially for developers and data engineers that need to build and develop data pipelines.
Luckily, Kubernetes and Kafka's combination helps us reduce everyday tasks tremendously by adding myriad capabilities to lessen the complexity of managing clusters.
Kafka Connect and KSQLDB are a fantastic combo to add to your streaming stack. These two soldiers can facilitate data acquisition and processing and also provide outstanding real-time ETL capabilities. But what if you need an OLAP datastore to answer complex queries with a low-latency response, that's where Apache Pinot comes to play.
At this session, you're going to learn:
- Effective Kafka deployment on Kubernetes
- How to properly configure Kafka Connect and KSQLDB
- Integrate Apache Pinot to answer OLAP queries
The document provides an agenda for the Government Track at the Kafka Summit 2021. The agenda includes sessions on topics like improving veteran benefit services through efficient data streaming, Kafka migration for satellite event streaming data, Kafka powered near real-time data pipelines at extreme scale, transformation during a global pandemic, securing the message bus with Kafka streams, Kafka for connected vehicle research, and driving a digital thread program in manufacturing with Apache Kafka. Speakers include representatives from Booz Allen Hamilton, ASRC Federal, University of California San Diego, Confluent, Raft LLC, Leidos, Ohio Department of Transportation, and Mercury Systems.
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
"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."
"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 TrendyolHostedbyConfluent
"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 TechniquesHostedbyConfluent
"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 KafkaHostedbyConfluent
"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."
"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."
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 WhyHostedbyConfluent
"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 ...HostedbyConfluent
"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 ...HostedbyConfluent
"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 ClustersHostedbyConfluent
"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 PlatformHostedbyConfluent
"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 PubHostedbyConfluent
"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"
"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."
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 PerformanceHostedbyConfluent
"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 BeyondHostedbyConfluent
"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."
"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 EcosystemHostedbyConfluent
"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 DisksHostedbyConfluent
"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."
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
7. The Paradigm for Data in Motion: Event Streams
7
Rich front-end customer
experiences
Real-time
Data
Real-time
Stream Processing
Real-time backend
operations
QUERY
A Sale
A shipment
A Trade
A Customer Experience