Couchbase Cloud No Equal (Rick Jacobs, Couchbase) Kafka Summit 2020

In this session we share our experience of building a real-time data pipelines at Tencent PCG - one that handles 20 trillion daily messages with 700 clusters and 100Gb/s bursting traffic from a single app. We discuss our roadmap of enhancing Kafka to break its limits in terms of scalability, robustness and cost of operation. We first built a proxy layer that aggregates physical clusters in a way agnostic to the clients. While this architecture solves many operational problems, it requires significant development to stay future-proof. With retrospection with our customer and careful study of the ongoing work from the community, we then designed a region federation solution in the broker layer, which allows us to deploy clusters at a much larger scale than previously possible, while at the same time providing better failure recovery and operability. We discuss how we make this development compatible with KIP-500 and KIP-405, and the two KIP (693, 694) that we submitted for discussion.

Real-time connectivity of databases and systems is critical in enterprises adopting digital transformation to support super-fast decisioning to drive applications like fraud detection, digital payments, recommendation engines. This talk will focus on the many functions that database streaming serves with Kafka, Spark and Aerospike. We will explore how to eliminate the wall between transaction processing and analytics by synthesizing streaming data with system of record data, to gain key insights in real-time.

The document summarizes a presentation about using Kafka, Streamliner, MemSQL and ZoomData for real-time analytics visualization. It shows an initial setup with one producer and queue feeding into Kafka, then adding a sink to an in-memory SQL database and real-time visualization consumer. It asks questions about ensuring the system is resilient, handles bad data and schema evolution, maintains consistency across visualization layers, and ability to scale throughput, concurrency and size.

If you want to build an ecosystem of streaming data to your Kafka platform, you will need a much easier way for your developer to quickly move what’s on the source to your cluster. Better yet, making the connector serverless so it would NOT waste any resources for being idle, and having a trusted partner manage your Kafka infrastructure for you. In this session, we will show you how easy we have made streaming data with great user experience. Flexible resource management with our new secret weapon in the Apache Camel project -- Kamelet. We’ll also demonstrate how Red Hat OpenShift Streams for Apache Kafka simplifies the provisioning of Kafka deployments in a public cloud, managing the cluster,topics, and configuring secure access to the Kafka cluster for your developers.

For many industries the need to group together related events based on a period of activity or inactivity is key. Advertising businesses, content producers are just a few examples of where session windows can be used to better understand user behavior. While such sessionization has been possible in Apache Kafka up to this point, implementing it has been rather complex and required leveraging low-level APIs. In the most recent release of Kafka, however, new capabilities have been added making session windows much easier to implement. In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.

Presenters: Rachel Pedreschi, Senior Director, Solutions Engineering, Imply.io + Josh Treichel, Partner Solutions Architect, Confluent Analytic pipelines running purely on batch processing systems can suffer from hours of data lag, resulting in accuracy issues with analysis and overall decision-making. Join us for a demo to learn how easy it is to integrate your Apache Kafka® streams in Apache Druid (incubating) to provide real-time insights into the data. In this online talk, you’ll hear about ingesting your Kafka streams into Imply’s scalable analytic engine and gaining real-time insights via a modern user interface. Register now to learn about: -The benefits of combining a real-time streaming platform with a comprehensive analytics stack -Building an analytics pipeline by integrating Confluent Platform and Imply -How KSQL, streaming SQL for Kafka, can easily transform and filter streams of data in real time -Querying and visualizing streaming data in Imply -Practical ways to implement Confluent Platform and Imply to address common use cases such as analyzing network flows, collecting and monitoring IoT data and visualizing clickstream data Confluent Platform, developed by the creators of Kafka, enables the ingest and processing of massive amounts of real-time event data. Imply, the complete analytics stack built on Druid, can ingest, store, query and visualize streaming data from Confluent Platform, enabling end-to-end real-time analytics. Together, Confluent and Imply can provide low latency data delivery, data transform, and data querying capabilities to power a range of use cases.

Transaction Banking from Goldman Sachs is a high volume, latency sensitive digital banking platform offering. We have chosen an event driven architecture to build highly decoupled and independent microservices in a cloud native manner and are designed to meet the objectives of Security, Availability Latency and Scalability. Kafka was a natural choice – to decouple producers and consumers and to scale easily for high volume processing. However, there are certain aspects that require careful consideration – handling errors and partial failures, managing downtime of consumers, secure communication between brokers and producers / consumers. In this session, we will present the patterns and best practices that helped us build robust event driven applications. We will also present our solution approach that has been reused across multiple application domains. We hope that by sharing our experience, we can establish a reference implementation that application developers can benefit from.

The document provides an overview of leveraging mainframe data for modern analytics using Attunity Replicate and Confluent streaming platform powered by Apache Kafka. It discusses the history of mainframes and data migration, how Attunity enables real-time data migration from mainframes, the Confluent streaming platform for building applications using data streams, and how Attunity and Confluent can be combined to modernize analytics using mainframe data streams. Use cases discussed include query offloading and cross-system customer data integration.

Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. In the TigerGraph database, Kafka Connect framework was used to build the native S3 data loader. In TigerGraph Cloud, we will be building native integration with many data sources such as Azure Blob Storage and Google Cloud Storage using Kafka as an integrated component for the Cloud Portal. In this session, we will be discussing both architectures: 1. built-in Kafka Connect framework within TigerGraph database; 2. using Kafka cluster for cloud native integration with other popular data sources. Demo will be provided for both data streaming processes.

The Ohio Department of Transportation has adopted Confluent as the event driven enabler of DriveOhio, a modern Intelligent Transportation System. DriveOhio digitally links sensors, cameras, speed monitoring equipment, and smart highway assets in real time, to dynamically adjust the surface road network to maximize the safety and efficiency for travelers. Over the past 24 months the team has increased the number and types of devices within the DriveOhio environment, while also working to see their vendors adopt Kafka to better participate in data sharing.

The Apache Kafka ecosystem is very rich with components and pieces that make for designing and implementing secure, efficient, fault-tolerant and scalable event stream processing (ESP) systems. Using real-world examples, this talk covers why Apache Kafka is an excellent choice for cloud-native and hybrid architectures, how to go about designing, implementing and maintaining ESP systems, best practices and patterns for migrating to the cloud or hybrid configurations, when to go with PaaS or IaaS, what options are available for running Kafka in cloud or hybrid environments and what you need to build and maintain successful ESP systems that are secure, performant, reliable, highly-available and scalable.

Apache Kafka users who want to leverage Google Cloud Platform's (GCPs) data analytics platform and open source hosting capabilities can bridge their existing Kafka infrastructure on-premise or in other clouds to GCP using Confluent's replicator tool and managed Kafka service on GCP. Using actual customer examples and a reference architecture, we'll showcase how existing Kafka users can stream data to GCP and use it in popular tools like Apache Beam on Dataflow, BigQuery, Google Cloud Storage (GCS), Spark on Dataproc, and Tensorflow for data warehousing, data processing, data storage, and advanced analytics using AI and ML.

The Apache Kafka ecosystem is very rich with components and pieces that make for designing and implementing secure, efficient, fault-tolerant and scalable event stream processing (ESP) systems. Using real-world examples, this talk covers why Apache Kafka is an excellent choice for cloud-native and hybrid architectures, how to go about designing, implementing and maintaining ESP systems, best practices and patterns for migrating to the cloud or hybrid configurations, when to go with PaaS or IaaS, what options are available for running Kafka in cloud or hybrid environments and what you need to build and maintain successful ESP systems that are secure, performant, reliable, highly-available and scalable.

One of the great things about running applications in the cloud is that you only pay for the resources that you use. But that also makes it more important than ever for our applications to be resource-efficient. This becomes even more critical when we use serverless functions. Micronaut is an application framework that provides dependency injection, developer productivity features, and excellent support for Apache Kafka. By performing dependency injection, AOP, and other productivity-enhancing magic at compile time, Micronaut allows us to build smaller, more efficient microservices and serverless functions. In this session, we'll explore the ways that Apache Kafka and Micronaut work together to enable us to build fast, efficient, event-driven applications. Then we'll see it in action, using the AWS Lambda Sink Connector for Confluent Cloud.

This document discusses scaling the backend of a financial platform for big data and blockchain. It describes challenges integrating big data using Apache Spark and Cassandra for tasks like predictive modeling, recommendations, and credit scoring. It also covers using a microservices architecture with Spring Cloud, Docker, and Kubernetes for deployment. Blockchain integration involves a private Ethereum network on Kubernetes for tokenization and a connection to the public Ethereum mainnet using Infura for payments and transfers.

While Kafka has guarantees around the number of server failures a cluster can tolerate, to avoid service interruptions, or even data loss, it is prudent to have infrastructure in place for when an environment becomes unavailable during a planned or unplanned outage. This talk describes the architectures available to you when planning for an outage. We will examine configurations including active/passive and active/active as well as availability zones and debate the benefits and limitations of each. We will also cover how to set up each configuration using the tools in Kafka. Whether downtime while you fail over clients to a backup is acceptable or you require your Kafka clusters to be highly available, this talk will give you an understanding of the options available to mitigate the impact of the loss of an environment.

Speaker: Gabriel Schenker, Lead Curriculum Developer, Confluent Streaming platforms have emerged as a popular, new trend, but what exactly is a streaming platform? Part messaging system, part Hadoop made fast, part fast ETL and scalable data integration. With Apache Kafka® at the core, event streaming platforms offer an entirely new perspective on managing the flow of data. This talk will explain what an event streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use—including several examples of where it is solving real business problems. New developments in this area such as KSQL will also be discussed.

Thousands of companies, from Uber and Netflix to Goldman Sachs and Cisco, use Apache Kafka to transform and reshape their data architectures. Kafka is frequently used as the bridge between legacy RDBMS and new NoSQL database systems, effectively transforming SQL table data into JSON documents and vice versa. Many companies also use Kafka for business-critical applications that drive real-time stream processing and analytics, intersystem messaging, high-volume data ingestion, and operational metrics collection. Couchbase and Kafka can be used together to address high throughput, distributed data management, and transformation challenges. In this webinar we’ll explore: Where Kafka fits into the big data ecosystem How companies are using Kafka for both real-time processing and as a bus for data exchange An example of how Kafka can bridge legacy RDBMS and new NoSQL database systems Several real-world use case architectures

Date: 14th November 2018 Location: Customer Experience Theatre Time: 11:50 - 12:20 Speaker: Perry Krug Organisation: Couchbase About: Who wants to see an ad today for the shoes they bought last week? Everyone knows that customer experience is driven by data: don't waste an opportunity to get them the right data at the right time. Real-time results are critical, but raw speed isn't everything: you need power and flexibility to react to changes on the fly. Come learn how market-leading enterprises are using Couchbase as their speed layer for ingestion, incremental view and presentation layers alongside Kafka, Spark and Hadoop to liberate their data lakes.

(Bruno Simic, Solutions Engineer, Couchbase) Breakout during Confluent’s streaming event in Munich. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.

GSJUG: Mastering Data Streaming Pipelines 09May2023 https://www.meetup.com/futureofdata-princeton/events/293233881/ This is a repost from the Garden State Java Users Group Event. Join me at https://www.meetup.com/garden-state-java-user-group/events/293229660/ See: https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.1787151623.1682868226-741104479.1678110925 Please note that registration via EventBrite is required to attend either in-person or online. We are happy to announce that Tim Spann will be our special guest for the May 9, 2023 meeting! Abstract: In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors and sinks. We hope to see you on May 9! Speaker Timothy Spann Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science. In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there, we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors, and sinks. https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.178