What’s the distinction between the Spring Framework and Spring Boot? If you are building a car, the Spring Framework is the engine while Spring Boot gives you the vehicle that you ride in. With experience teaching and answering questions on how to use Spring and Apache Kafka® together, Viktor Gamov (Principal Developer Advocate, Kong) designed a free course on Confluent Developer and previews it in this episode. Not only this, but he also explains why the opinionated Spring Framework would be a good hero in Marvel. Spring is an ever-evolving framework that embraces modern, cloud-native technologies with cross-language options, such as Kotlin integration. Unlike its predecessors, the Spring Framework supports a modern version of Java and the requirements of the Twelve-Factor App manifesto for you to move an application between environments without changing the code. With that engine in place, Spring Boot introduces a microservices architecture. Spring Boot contains databases and messaging systems integrations, reducing development time and increasing overall productivity. Spring for Apache Kafka applies best practices of the Spring community to the Kafka ecosystem, including features that abstract away infrastructure code for you to focus on programming logic that is important for your application. Spring for Apache Kafka provides a wrapper around the producer and consumer to ease Kafka configuration with APIs, including KafkaTemplate, MessageListenerContainer, @KafkaListener, and TopicBuilder.The Spring Framework and Apache Kafka course will equip you with the knowledge you need in order to build event-driven microservices using Spring and Kafka on Confluent Cloud. Tim and Viktor also discuss Spring Cloud Stream as well as Spring Boot integration with Kafka Streams and more. EPISODE LINKSSpring Framework and Apache Kafka courseLiveStreams with Viktor GamovUse kafkaa35 to get 30% off "Kafka in Action"Watch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Streaming Audio: a Confluent podcast about Apache Kafka

Getting Started with Spring for Apache Kafka ft. Viktor Gamov

When you order a pizza, what if you knew every step of the process from the moment it goes in the oven to being delivered to your doorstep? Event-Driven Architecture is a modern, data-driven approach that describes “events” (i.e., something that just happened). A real-time data infrastructure enables you to provide such event-driven data insights in real time. Israel Ekpo (Principal Cloud Solutions Architect, Microsoft Global Partner Solutions, Microsoft) and Alicia Moniz (Cloud Partner Solutions Architect, Confluent) discuss use cases on leveraging Confluent Cloud and Microsoft Azure to power real-time, event-driven architectures. As an Apache Kafka® community stalwart, Israel focuses on helping customers and independent software vendor (ISV) partners build solutions for the cloud and use open source databases and architecture solutions like Kafka, Kubernetes, Apache Flink, MySQL, and PostgreSQL on Microsoft Azure. He’s worked with retailers and those in the IoT space to help them adopt processes for inventory management with Confluent. Having a cloud-native, real-time architecture that can keep an accurate record of supply and demand is important in keeping up with the inventory and customer satisfaction. Israel has also worked with customers that use Confluent to integrate with Cosmos DB, Microsoft SQL Server, Azure Cognitive Search, and other integrations within the Azure ecosystem. Another important use case is enabling real-time data accessibility in the public sector and healthcare while ensuring data security and regulatory compliance like HIPAA. Alicia has a background in AI, and she expresses the importance of moving away from the monolithic, centralized data warehouse to a more flexible and scalable architecture like Kafka. Building a data pipeline leveraging Kafka helps ensure data security and consistency with minimized risk.The Confluent and Azure integration enables quick Kafka deployment with out-of-the-box solutions within the Kafka ecosystem. Confluent Schema Registry captures event streams with a consistent data structure, ksqlDB enables the development of real-time ETL pipelines, and Kafka Connect enables the streaming of data to multiple Azure services.EPISODE LINKSMicrosoft Azure at Kafka Summit AmericasIzzyAcademy Kafka on Azure Learning Series by Alicia MonizWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Powering Event-Driven Architectures on Microsoft Azure with Confluent

Autonomy is key in building a sustainable and motivated team, and this core principle also applies to DevOps. Building self-serve Apache Kafka® and Confluent Platform deployments require a streamlined process with unrestricted tools—a centralized processing tool that allows teams in large or mid-sized organizations to automate infrastructure changes while ensuring shared standards are met. With more than 15 years of engineering and technology consulting experience, Pere Urbón-Bayes (Senior Solution Architect, Professional Services, Confluent) built an open source solution—JulieOps—to enable a self-serve Kafka platform as a service with data governance. JulieOps is one of the first solutions available to realize self-service for Kafka and Confluent with automation. Development, operations, security teams often face hurdles when deploying Kafka. How can a user request the topics that they need for their applications? How can the operations team ensure compliance and role-based access controls? How can schemas be standardized and structured across environments? Manual processes can be cumbersome with long cycle times. Automation reduces unnecessary interactions and shortens processing time, enabling teams to be more agile and autonomous in solving problems from a localized team level. Similar to Terraform, JulieOps is declarative. It&apos;s a centralized agent that uses the GitOps philosophy, focusing on a developer-centric experience with tools that developers are already familiar with, to provide abstractions to each product personas. All changes are documented and approved within the change management process to streamline deployments with timely and effective audits, as well as ensure security and compliance across environments.  The implementation of a central software agent, such as JulieOps, helps you automate the management of topics, configuration, access controls, Confluent Schema Registry, and more within Kafka. It’s multi tenant out of the box and supports on-premises clusters and the cloud with CI/CD practices. Tim and Pere also discuss the steps necessary to build a self-service Kafka with an automatic Jenkins process that will empower development teams to be autonomous.EPISODE LINKSJulieOps on GitHubJulieOps documentationBuilding a Self-Service Kafka Platform as a Service with GitOps with Pere Urbón-BayesOpen Service Broker APIDrive | Daniel H. Pink Watch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Automating DevOps for Apache Kafka and Confluent ft. Pere Urbón-Bayes

Kafka Connect is a streaming integration framework between Apache Kafka® and external systems, such as databases and cloud services. With expertise in ksqlDB and Kafka Connect, Robin Moffatt (Staff Developer Advocate, Confluent) helps and supports the developer community in understanding Kafka and its ecosystem. Recently, Robin authored a Kafka Connect 101 course that will help you understand the basic concepts of Kafka Connect, its key features, and how it works.What’s Kafka Connect, and how does it work with Kafka and brokers? Robin explains that Kafka Connect is a Kafka API that runs separately from the Kafka brokers, running on its own Java virtual machine (JVM) process known as the Kafka Connect worker. Kafka Connect is essential for streaming data from different sources into Kafka and from Kafka to various targets. With Connect, you don’t have to write programs using Java and instead specify your pipeline using configuration. Kafka Connect.As a pluggable framework, Kafka Connect has a broad set of more than 200 different connectors available on Confluent Hub, including but not limited to:NoSQL and document stores (Elasticsearch, MongoDB, and Cassandra)RDBMS (Oracle, SQL Server, DB2, PostgreSQL, and MySQL)Cloud object stores (Amazon S3, Azure Blob Storage, and Google Cloud Storage),Message queues (ActiveMQ, IBM MQ, and RabbitMQ)Robin and Tim also discuss single message transform (SMTs), as well as distributed and standalone deployment modes Kafka Connect. Tune in to learn more about Kafka Connect, and get a preview of the Kafka Connect 101 course.EPISODE LINKSKafka Connect 101 courseMeetup: From Zero to Hero with Kafka ConnectConfluent Hub: Discover Kafka connectors and more12 Days of SMTsWhy Kafka Connect? ft. Robin MoffattWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Intro to Kafka Connect: Core Components and Architecture ft. Robin Moffatt

As one of the top female coders of her Java coding class in high school, Twesha Modi is continuing to follow her passion for computer science as a senior at Cornell University. During Twesha&apos;s summer internship at Confluent, she contributed to designing a new service to automate Apache Kafka® cluster rollout management—a process that releases the latest Kafka versions to customer’s clusters in Confluent Cloud.During Twesha’s internship, she was part of the Platform team, which designed a cluster management rollout service—capable of automating cluster rollout and generating rollout plans that streamline Kafka operations in the cloud. The pre-existing manual process worked well in scenarios involving just a couple hundred clusters, but with growth and the need to upgrade a significantly larger cluster fleet to target versions in the cloud, the process needed to be automated in order to accelerate feature releases while ensuring security. Under the mentorship of Pablo Berton (Staff Software Engineer I, Product Infrastructure, Confluent), Nikhil Bhatia (Principal Engineer I, Product Infrastructure, Confluent), and Vaibhav Desai (Staff Software Engineer I, Confluent), Twesha supported the design of the rollouts management process from scratch. Twesha’s 12-week internship helped her learn more about software architecture and the design process that goes into software as a service and beyond. Not only did she acquire new skills and knowledge, but she also met mentors who are willing to teach, share their experiences, and help her succeed along the way. Tim and Twesha also talk about the importance of asking questions during internships for the best learning experience, in addition to discussing the Vert.x, Java, Spring, and Kubernetes APIs. EPISODE LINKSMulti-Cluster Apache Kafka with Cluster Linking ft. Nikhil BhatiaWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Designing a Cluster Rollout Management System for Apache Kafka ft. Twesha Modi

Apache Kafka® 3.0 is out! To spotlight major enhancements in this release, Tim Berglund (Apache Kafka Developer Advocate) provides a summary of what’s new in the Kafka 3.0 release from Krakow, Poland, including API changes and improvements to the early-access Kafka Raft (KRaft). KRaft is a built-in Kafka consensus mechanism that’s replacing Apache ZooKeeper going forward. It is recommended to try out new KRaft features in a development environment, as KRaft is not advised for production yet. One of the major features in Kafka 3.0 is the efficiency for KRaft controllers and brokers to store, load, and replicate snapshots into a Kafka cluster for metadata topic partitioning. The Kafka controller is now responsible for generating a Kafka producer ID in both ZooKeeper and KRaft, easing the transition from ZooKeeper to KRaft on the Kafka 3.X version line. This update also moves us closer to the ZooKeeper-to-KRaft bridge release. Additionally, this release includes metadata improvements, exactly-once semantics, and KRaft reassignments. To enable a stronger record delivery guarantee, Kafka producers turn on by default idempotency, together with acknowledgment delivery by all the replicas. This release also comprises enhancements to Kafka Connect task restarts, Kafka Streams timestamp based synchronization and more flexible configuration options for MirrorMaker2 (MM2). The first version of MirrorMaker has been deprecated, and MirrorMaker2 will be the focus for future developments. Besides that, this release drops support for older message formats, V0 and V1, as well as initiates the removal of Java 8 and Scala 2.12 across all components in Apache Kafka. The universal Java 8 and Scala 2.12 deprecation is anticipated to complete in the future Apache Kafka 4.0 release.Apache Kafka 3.0 is a major release and step forward for the Apache Kafka project!EPISODE LINKSApache Kafka 3.0 release notes Read the blog to learn moreDownload Apache Kafka 3.0Watch the video version of this podcastJoin the Confluent Community Slack

Apache Kafka 3.0 - Improving KRaft and an Overview of New Features

A developer community brings people with shared interests and purpose together. The fundamental elements of a community are to gather, learn, support, and create opportunities for collaboration. A developer community is also an effective and efficient instrument for exploring and solving problems together. The power of a community is its endless advantages, from knowledge sharing to support, interesting discussions, and much more. Tim Berglund invites Ale Murray (Global Community Manager, Confluent) and Robin Moffatt (Staff Developer Advocate, Confluent) on the show to discuss the art of Q&A in a global community, share tips for building a vibrant developer community, and highlight the five strategic pillars for running a successful global community:MeetupsConferencesMVP program (e.g., Confluent Community Catalysts)Community hackathonsDigital platforms Digital platforms, such as a community Slack and forum, often consist of members who are well versed on topics of interest. As a leader in the Apache Kafka® and Confluent communities, Robin expresses the importance of being respectful when asking questions and providing details to the problem at hand. A well-formulated and focused question will more likely lead to a helpful answer. Oftentimes, the cognitive process of composing the question actually helps iron out the problem and draw out a solution. This process is also known as the rubber duck debugging theory. In a global community with diverse cultures and languages, being kind and having empathy is crucial. The tone and meaning of words can sometimes get lost in translation. Using emojis can help transcend language barriers by adding another layer of tone to plain text. Ale and Robin also discuss the pros and cons of a community forum vs. a Slack group. Tune in to find out more tips and best practices on building and engaging a developer community.EPISODE LINKSUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)How to Ask Good QuestionsWhy We Launched a ForumGrowing the Event Streaming Community During COVID-19 ft. Ale MurrayMeetup HubAnnouncing the Confluent Community ForumWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with Confluent

How to Build a Strong Developer Community with Global Engagement ft. Robin Moffatt and Ale Murray

The data mesh architectural paradigm shift is all about moving analytical data away from a monolithic data warehouse or data lake into a distributed architecture—allowing data to be shared for analytical purposes in real time, right at the point of origin. The idea of data mesh was introduced by Zhamak Dehghani (Director of Emerging Technologies, Thoughtworks) in 2019.  Here, she provides an introduction to data mesh and the fundamental problems that it’s trying to solve. Zhamak describes that the complexity and ambition to use data have grown in today’s industry. But what is data mesh? For over half a century, we’ve been trying to democratize data to deliver value and provide better analytic insights. With the ever-growing number of distributed domain data sets, diverse information arrives in increasing volumes and with high velocity. To remove the friction and serve the requirement for data to be consumed by operational needs in various use cases, the best way is to mesh the data. This means connecting data through a peer-to-peer fashion and liberating data for analytics, machine learning, serving up data-intensive applications across the organization, and more. Data mesh tackles the deficiency of the traditional, centralized data lake and data warehouse platform architecture. The data mesh paradigm is founded on four principles: Domain-oriented ownershipData as a productData available everywhere in a self-serve data infrastructureData standardization governanceA decentralized, agnostic data structure enables you to synthesize data and innovate. The starting point is embracing the ideology that data can be anywhere. Source-aligned data should serve as a product available for people across the organization to combine, explore, and drive actionable insights. Zhamak and Tim also discuss the next steps we need to take in order to bring data mesh to life at the industry level.To learn more about the topic, you can visit the all-new Confluent Developer course: Data Mesh 101. Confluent Developer is a single destination with resources to begin your Kafka journey.  EPISODE LINKSZhamak Dehghani: How to Build the Data Mesh FoundationData Mesh 101 CourseSaxo Bank’s Best Practices for a Distributed Domain-Driven Architecture Founded on the Data MeshPlacing Apache Kafka at the Heart of a Data Revolution at Saxo BankWhat the Heck is a Data Mesh?! By Chris Riccomini Watch the video version of this podcastJoin the Confluent CommunityLearn Kafka on Confluent DeveloperLive demo: Event-Driven Microservices with ConfluentUse PODCAST100 to get $100 of Confluent Cloud usage (details)

What Is Data Mesh, and How Does it Work? ft. Zhamak Dehghani

Note: This episode was recorded when Cluster Linking was in preview mode. It’s now generally available as part of the Confluent Q3 ‘21 release on August 17, 2021. Infrastructure needs to react in real time to support globally distributed events, such as cloud migration, IoT, edge data collection, and disaster recovery. To provide a seamless yet cloud-native, cross-cluster topic replication experience, Nikhil Bhatia (Principal Engineer I, Product Infrastructure, Confluent) and the team engineered a solution called Cluster Linking. Available on Confluent Cloud, Cluster Linking is an API that enables Apache Kafka® to work across multi-datacenters, making it possible to design globally available distributed systems. As industries adopt multi-cloud usage and depart from on-premises and single cluster operations, we need to rethink how clusters operate across regions in the cloud. Cluster Linking as an inter-cluster replication layer into Confluent Server, allowing you to connect clusters together and replicate topics asynchronously without the need for Connect. Cluster Linking requires zero external components when moving messages from one cluster to another. It replicates data into its destination by partition and byte for byte, preserving offsets from the source cluster. Different from Confluent Replicator and MirrorMaker2, Cluster Linking simplifies failover in high availability and disaster recovery scenarios, improving overall efficiency by avoiding recompression. As a great cost-effective alternative to Multi-Region Cluster, Cluster Linking reduces traffic between data centers and enables inter-cluster replication without the need to deploy and manage a separate Connect cluster. With low recovery point objective (RPO) and recovery time objective (RTO), Cluster Linking enables scenarios such as: Migration to cloud: Remove the complexity layer of self-run datacenters with fully managed cloud services. Global reads: Enable users to connect to Kafka from around the globe and consume data locally. Empowering better performance and improving cost effectiveness. Disaster recovery: Prepare your system for fault tolerance, from datacenter, regional, or cloud-level disasters, ensuring zero data loss and high availability. Find out more about Cluster Linking architecture and set your data in motion with global Kafka.EPISODE LINKSAnnouncing the Confluent Q3 &apos;21 ReleaseIntroducing Cluster Linking in Confluent Platform 6.0What is Cluster Linking? Resurrecting In-Sync Replicas with Automatic Observer Promotion ft. Anna McDonaldWatch the video version of this podcastJoin the Confluent CommunityLearn Kafka at Confluent DeveloperLive demo: Event-Driven Microservices with ConfluentUse PODCAST100 to get $100 of Confluent Cloud usage (details)

Multi-Cluster Apache Kafka with Cluster Linking ft. Nikhil Bhatia

What does a ride-hailing app that offers micromobility and food delivery services have to do with data in motion? In this episode, Ruslan Gibaiev (Data Architect, Bolt) shares about Bolt’s road to adopting Apache Kafka® and ksqlDB for stream processing to replicate data from transactional databases to analytical warehouses. Rome wasn&apos;t built overnight, nor was the adoption of Kafka and  ksqlDB at Bolt. Initially, Bolt noticed the need for system standardization and replacing the unreliable query-based change data capture (CDC) process. As an experienced Kafka developer, Ruslan believed that Kafka is the solution for adopting change data capture as a company-wide event streaming solution. Persuading the team at Bolt to adopt and buy in was hard at first, but Ruslan made it possible. Eventually, the team replaced query-based CDC with log-based CDC from Debezium, built on top of Kafka. Shortly after the implementation, developers at Bolt began to see precise, correct, and real-time data. As Bolt continues to grow, they see the need to implement a data lake or a data warehouse for OTP system data replication and stream processing. After carefully considering several different solutions and frameworks such as ksqlDB, Apache Flink®, Apache Spark™, and Kafka Streams, ksqlDB shines most for their business requirement. Bolt adopted ksqlDB because it is native to the Kafka ecosystem, and it is a perfect fit for their use case. They found ksqlDB to be a particularly good fit for replicating all their data to a data warehouse for a number of reasons, including: Easy to deploy and manageLinearly scalableNatively integrates with Confluent Schema Registry Turn in to find out more about Bolt’s adoption journey with Kafka and ksqlDB. EPISODE LINKSInside ksqlDB Course ksqlDB 101 CourseHow Bolt Has Adopted Change Data Capture with Confluent PlatformAnalysing Changes with Debezium and Kafka StreamsNo More Silos: How to Integrate Your Databases with Apache Kafka and CDCChange Data Capture with Debezium ft. Gunnar MorlingAnnouncing ksqlDB 0.17.0Real-Time Data Replication with ksqlDBWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Using Apache Kafka and ksqlDB for Data Replication at Bolt

Monolithic applications present challenges for organizations like Saxo Bank, including difficulties when it comes to transitioning to cloud, data efficiency, and performing data management in a regulated environment. Graham Stirling, the head of data platforms at Saxo Bank and also a self-proclaimed recovering architect on the pathway to delivery, shares his experience over the last 2.5 years as Saxo Bank placed Apache Kafka® at the heart of their company—something they call a data revolution. Before adopting Kafka, Saxo Bank encountered scalability problems. They previously relied on a centralized data engineering team, using the database as an integration point and looking to their data warehouse as the center of the analytical universe. However, this needed to evolve. For a better data strategy, Graham turned his attention towards embracing a data mesh architecture: Create a self-serve platform that enables domain teams to publish and consume data assetsFederate ownership of domain data models and centralize oversights to allow a standard language to emerge while ensuring information efficiency Believe in the principle of data as a product to improve business decisions and processes Data mesh was first defined by Zhamak Dehghani in 2019, as a type of data platform architecture paradigm and has now become an integral part of Saxo Bank’s approach to data in motion. Using a combination of Kafka GitOps, pipelines, and metadata, Graham intended to free domain teams from having to think about the mechanics, such as connector deployment, language binding, style guide adherence, and data handling of personally identifiable information (PII). To reduce operational complexity, Graham recognized the importance of using Confluent Schema Registry as a serving layer for metadata. Saxo Bank authored schemes with Avro IDL for composability and standardization and later made a switch over to Uber’s Buf for strongly typed metadata. A further layer of metadata allows Saxo Bank to define FpML-like coding schemes to specify information classification, reference external standards, and link semantically related concepts. By embarking on the data mesh operating model, Saxo Bank scales data processing in a way that was previously unimaginable, allowing them to generate value sustainably and to be more efficient with data usage. Tune in to this episode to learn more about the following:Data meshTopic/schema as an APIData as a productKafka as a fundamental building block of data strategyEPISODE LINKSZhamak Dehghani Kafka Summit 2021 KeynoteData Mesh 101 CourseData Mesh Principles and Logical ArchitectureSaxo Bank’s Best Practices for a Distributed Domain-Driven ArchitectureWatch video version of this podcastJoin the Confluent CommunityLearn Kafka on Confluent DeveloperDemo: Event-Driven Microservices with ConfluentUse PODCAST100 to get $100 of free Confluent Cloud (details)

Placing Apache Kafka at the Heart of a Data Revolution at Saxo Bank

ksqlDB makes it easy to read, write, process, and transform data on Apache Kafka®, the de facto event streaming platform. With simple SQL syntax, pre-built connectors, and materialized views, ksqlDB’s powerful stream processing capabilities enable you to quickly start processing real-time data at scale. But how does ksqlDB work? In this episode, Michael Drogalis (Principal Product Manager, Product Management, Confluent) previews an all-new Confluent Developer course: Inside ksqlDB, where he provides a full overview of ksqlDB’s internal architecture and delves into advanced ksqlDB features. When it comes to ksqlDB or Kafka Streams, there’s one principle to keep in mind: ksqlDB and Kafka Streams share a runtime. ksqlDB runs its SQL queries by dynamically writing Kafka Streams typologies. Leveraging Confluent Cloud makes it even easier to use ksqlDB.Once you are familiar with ksqlDB’s basic design, you’ll be able to troubleshoot problems and build real-time applications more effectively. The Inside ksqlDB course is designed to help you advance in ksqlDB and Kafka. Paired with hands-on exercises and ready-to-use codes, the course covers topics including: ksqlDB architectureHow stateless and stateful operations workStreaming joins Table-table joinsElastic scaling High availabilityMichael also sheds light on ksqlDB’s roadmap: Building out the query layer so that is highly scalable, making it able to execute thousands of concurrent subscriptionsMaking Confluent Cloud the best place to run ksqlDB and process streamsTune in to this episode to find out more about the Inside ksqlDB course on Confluent Developer. The all-new website provides diverse and comprehensive resources for developers looking to learn about Kafka and Confluent. You’ll find free courses, tutorials, getting started guides, quick starts for 60+ event streaming patterns, and more—all in a single destination. EPISODE LINKSInside ksqlDB Course ksqlDB 101 CourseHow Real-Time Stream Processing Safely Scales with ksqlDB, AnimatedHow Real-Time Materialized Views Work with ksqlDB, AnimatedHow Real-Time Stream Processing Works with ksqlDB, AnimatedWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Advanced Stream Processing with ksqlDB ft. Michael Drogalis

Building a large, stateful Kafka Streams application that tracks the state of each outgoing email is crucial to marketing automation tools like Mailshimp. Joining us today in this episode, Mitch Seymour, staff engineer at Mailchimp, shares how ksqlDB and Kafka Streams handle the company’s largest source of streaming data.  Almost like a post office, except instead of sending physical parcels, Mailchimp sends billions of emails per day. Monitoring the state of each email can provide visibility into the core business function, and it also returns information about the health of both internal and remote message transfer agents (MTAs). Finding a way to track those MTA systems in real time is pivotal to the success of the business. Mailchimp is an early Apache Kafka® adopter that started using the technology in 2014, a time before ksqlDB, Kafka Connect, and Kafka Streams came into the picture. The stream processing applications that they were building faced many complexities and rough edges. As their use case evolved and scaled overtime at Mailchimp, a large number of applications deviated from the initial implementation and design so that different applications emerged that they had to maintain. To reduce cost, complexity, and standardize stream processing applications, adopting ksqlDB and Kafka Streams became the solution to their problems. This is what Mitch calls, “minimizing software speciation in our software” It&apos;s the idea when applications evolved into multiple systems to respond to failure-handling strategies, increased load, and the like. Using different scaling strategies and communication protocols creates system silos and can be challenging to maintain.Replacing the existing architecture that supported point-to-point communication, the new Mailchimp architecture uses Kafka as its foundation with scalable custom functions, such as a reusable and highly functional user-defined function (UDF). The reporting capabilities have also evolved from Kafka Streams’ interactive queries into enhanced queries with Elasticsearch. Turning experiences into books, Mitch is also an author of O’Reilly’s Mastering Kafka Streams and ksqlDB and the author and illustrator of Gently Down the Stream: A Gentle Introduction to Apache Kafka. EPISODE LINKSThe Exciting Frontier of Custom ksql Functions (Mitch Seymour, Mailchimp) Kafka Summit LondonApache Kafka 101: Kafka Streams CourseksqlDB UDFs and UDADs Made EasyUsing Apache Kafka as a Scalable, Event-Driven Backbone for Service ArchitecturesThe Haiku Approach to Writing SoftwareWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

Minimizing Software Speciation with ksqlDB and Kafka Streams ft. Mitch Seymour

The best-informed business insights that support better decision-making begin with data collection, ahead of data processing and analytics. Enterprises nowadays are engulfed by data floods, with data sources ranging from cloud services, applications, to thousands of internal servers. The massive volume of data that organizations must process presents data ingestion challenges for many large companies. In this episode, data security engineer, Vitalli Rudenskyi, discusses the decision to replace a vendor security information and event management (SIEM) system by developing a custom solution with Apache Kafka® and Kafka Connect for a better data collection strategy.Having a data collection infrastructure layer is mission critical for Vitalii and the team in helping enterprises protect data and detect security events. Building on the base of Kafka, their custom SIEM infrastructure is configurable and designed to be able to ingest and analyze huge amounts of data, including personally identifiable information (PII) and healthcare data. When it comes to collecting data, there are two fundamental choices: push or pull. But how about both? Vitalii shares that Kafka Connect API extensions are integral to data ingestion in Kafka. The three key components to allow their SIEM system to collect and record daily by pushing and pulling: NettySource Connector: A connector developed to receive data from different network devices to Apache Kafka. It helps receive data using both the TCP and UDP transport protocols and can be adopted to receive any data from Syslog to SNMP and NetFlow.PollableAPI Connector: A connector made to receive data from remote systems, pulling data from different remote APIs and services.Transformations Library: These are useful extensions to the existing out-of-the-box transformations. Approach with “tag and apply” transformations that transform data into the right place in the right format after collecting data.Listen to learn more as Vitalii shares the importance of data collection and the building of a custom solution to address multi-source data management requirements. EPISODE LINKSFeed Your SIEM Smart with Kafka ConnectTo Pull or to Push Your Data with Kafka Connect? That Is the Question.Free Kafka Connect 101 CourseSyslog Source Connector for Confluent PlatformJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Collecting Data with a Custom SIEM System Built on Apache Kafka and Kafka Connect ft. Vitalii Rudenskyi

Stream processing has become an important part of the big data landscape as a new programming paradigm to implement real-time data-driven applications. One of the biggest challenges for streaming systems is to provide correctness guarantees for data processing in a distributed environment. Guozhang Wang (Distributed Systems Engineer, Confluent) contributed to a leadership paper, along with other leaders in the Apache Kafka® community, on consistency and completeness in streaming processing in Apache Kafka in order to shed light on what a reimagined, modern infrastructure looks like. In his white paper titled Consistency and Completeness: Rethinking Distributed Stream Processing in Apache Kafka, Guozhang covers the following topics in his paper: Streaming correctness challengesStream processing with KafkaExactly-once in Kafka StreamsFor context, accurate, real-time data stream processing is more friendly to modern organizations that are composed of vertically separated engineering teams. Unlike in the past, stream processing was considered as an auxiliary system to normal batch processing oriented systems, often bearing issues around consistency and completeness. While modern streaming engines, such as ksqlDB and Kafka Streams are designed to be authoritative, as the source of truth, and are no longer treated as an approximation, by providing strong correctness guarantees. There are two major umbrellas of the correctness of guarantees: Consistency: Ensuring unique and extant recordsCompleteness: Ensuring the correct order of records, also referred to as exactly-once semantics. Guozhang also answers the question of why he wrote this academic paper, as he believes in the importance of knowledge sharing across the community and bringing industry experience back to academia (the paper is also published in SIGMOD 2021, one of the most important conference proceedings in the data management research area). This will help foster the next generation of industry innovation and push one step forward in the data streaming and data management industry. In Guozhang&apos;s own words, "Academic papers provide you this proof of concept design, which gets groomed into a big system."EPISODE LINKSWhite Paper: Rethinking Distributed Stream Processing in Apache KafkaBlog: Rethinking Distributed Stream Processing in Apache KafkaEnabling Exactly-Once in Kafka StreamsWhy Kafka Streams Does Not Use Watermarks ft. Matthias SaxStreams and Tables: Two Sides of the Same CoinWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get $60 of free Confluent Cloud usage (details)

Consistent, Complete Distributed Stream Processing ft. Guozhang Wang

Streaming Audio is a podcast from Confluent, the team that built Apache Kafka®️. Host Tim Berglund (Senior Director of Developer Experience, Confluent) and guests unpack a variety of topics surrounding Apache Kafka, event stream processing and real-time data. The show also features the segment "Ask Confluent," in which Gwen Shapira (Engineering Manager, Confluent) and guests respond to a handful of questions and comments about the Confluent and Kafka ecosystems—from Kafka connectors, to distributed systems, data integration, Kafka deployment, and managed Apache Kafka as a service—on Twitter, YouTube, and elsewhere. Apache®️, Apache Kafka, Kafka, and the Kafka logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by The Apache Software Foundation is implied by the use of these marks.

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Streaming Audio: a Confluent podcast about Apache Kafka

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