When Kafka Meets the Scaling and Reliability needs of World's Largest Retailer : A WALMART Story (Suman Pattnaik & Prasanna Subburaj, Walmart) Kafka Summit SF 2019

Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka. No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck. A detailed article about this topic: https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/

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Apache Spark at Airbnb

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Airbnb primarily leverages Spark to power mission critical data applications. In this talk, we would like to share our major production use cases including both Streaming applications and Batch processing applications. In addition, we would like share our optimizations on how to improve the throughput of Spark Kafka connector by 10X. Furthermore, we plan to share our journey and lessons learned during the process of upgrading Spark 1+ applications to Spark 2+. The key takeaways includes best practices learned from building and scaling production Spark applications as well as tips and benefits of migrating to Spark 2.x. We hope to share our experiences of making Spark successful at Airbnb with a broader audience of Spark users.

CDC patterns in Apache Kafka®

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Ketan Gote

Kafka Streams: What it is, and how to use it?

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Full recorded presentation at https://www.youtube.com/watch?v=2UfAgCSKPZo for Tetrate Tech Talks on 2022/05/13. Envoy's support for Kafka protocol, in form of broker-filter and mesh-filter. Contents: - overview of Kafka (usecases, partitioning, producer/consumer, protocol); - proxying Kafka (non-Envoy specific); - proxying Kafka with Envoy; - handling Kafka protocol in Envoy; - Kafka-broker-filter for per-connection proxying; - Kafka-mesh-filter to provide front proxy for multiple Kafka clusters. References: - https://adam-kotwasinski.medium.com/deploying-envoy-and-kafka-8aa7513ec0a0 - https://adam-kotwasinski.medium.com/kafka-mesh-filter-in-envoy-a70b3aefcdef

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Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.

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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.

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Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.

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This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems. Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/) Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.

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Kafka is a high-throughput, fault-tolerant, scalable platform for building high-volume near-real-time data pipelines. This presentation is about tuning Kafka pipelines for high-performance. Select configuration parameters and deployment topologies essential to achieve higher throughput and low latency across the pipeline are discussed. Lessons learned in troubleshooting and optimizing a truly global data pipeline that replicates 100GB data under 25 minutes is discussed.

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Aviation and travel are notoriously vulnerable to social, economic, and political events, as well as the ever-changing expectations of consumers. Coronavirus is just a piece of the challenge. This presentation explores use cases, architectures, and references for Apache Kafka as event streaming technology in the aviation industry, including airline, airports, global distribution systems (GDS), aircraft manufacturers, and more. Examples include Lufthansa, Singapore Airlines, Air France Hop, Amadeus, and more. Technologies include Kafka, Kafka Connect, Kafka Streams, ksqlDB, Machine Learning, Cloud, and more.

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This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.

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(Gunnar Morling, RedHat) Kafka Summit SF 2018 Debezium (noun | de·be·zi·um | /dɪ:ˈbɪ:ziːəm/): secret sauce for change data capture (CDC) streaming changes from your datastore that enables you to solve multiple challenges: synchronizing data between microservices, gradually extracting microservices from existing monoliths, maintaining different read models in CQRS-style architectures, updating caches and full-text indexes and feeding operational data to your analytics tools Join this session to learn what CDC is about, how it can be implemented using Debezium, an open source CDC solution based on Apache Kafka and how it can be utilized for your microservices. Find out how Debezium captures all the changes from datastores such as MySQL, PostgreSQL and MongoDB, how to react to the change events in near real time and how Debezium is designed to not compromise on data correctness and completeness also if things go wrong. In a live demo we’ll show how to set up a change data stream out of your application’s database without any code changes needed. You’ll see how to sink the change events into other databases and how to push data changes to your clients using WebSockets.

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

If there were a buzzword of the hour, it would certainly be "data mesh"! This new architectural paradigm unlocks analytic data at scale and enables rapid access to an ever-growing number of distributed domain datasets for various usage scenarios. As such, the data mesh addresses the most common weaknesses of the traditional centralized data lake or data platform architecture. And the heart of a data mesh infrastructure must be real-time, decoupled, reliable, and scalable. This presentation explores how Apache Kafka, as an open and scalable decentralized real-time platform, can be the basis of a data mesh infrastructure and - complemented by many other data platforms like a data warehouse, data lake, and lakehouse - solve real business problems. There is no silver bullet or single technology/product/cloud service for implementing a data mesh. The key outcome of a data mesh architecture is the ability to build data products; with the right tool for the job. A good data mesh combines data streaming technology like Apache Kafka or Confluent Cloud with cloud-native data warehouse and data lake architectures from Snowflake, Databricks, Google BigQuery, et al.

Introduction to Apache Kafka

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This document provides an introduction to Apache Kafka. It describes Kafka as a distributed messaging system with features like durability, scalability, publish-subscribe capabilities, and ordering. It discusses key Kafka concepts like producers, consumers, topics, partitions and brokers. It also summarizes use cases for Kafka and how to implement producers and consumers in code. Finally, it briefly outlines related tools like Kafka Connect and Kafka Streams that build upon the Kafka platform.

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Flink Forward San Francisco 2022. In normal situations, the default Kafka consumer and producer configuration options work well. But we all know life is not all roses and rainbows and in this session we’ll explore a few knobs that can save the day in atypical scenarios. First, we'll take a detailed look at the parameters available when reading from Kafka. We’ll inspect the params helping us to spot quickly an application lock or crash, the ones that can significantly improve the performance and the ones to touch with gloves since they could cause more harm than benefit. Moreover we’ll explore the partitioning options and discuss when diverging from the default strategy is needed. Next, we’ll discuss the Kafka Sink. After browsing the available options we'll then dive deep into understanding how to approach use cases like sinking enormous records, managing spikes, and handling small but frequent updates.. If you want to understand how to make your application survive when the sky is dark, this session is for you! by Olena Babenko

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The document discusses how AWS is helping companies in the travel industry innovate through leveraging data, machine learning, and personalization. It highlights trends in travel like connected customer experiences and operational efficiency. Examples are given of airlines like Qantas using AWS to gain customer insights and Ryanair rebuilding applications on AWS to personalize travel experiences. The conclusion encourages travel companies to focus on differentiating through customers and having an ambitious innovation plan using AWS's 13+ years of experience.

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This document contains slides from a presentation given by Francisco Ruiz on creating successful pitches and avoiding common mistakes. The presentation discusses key elements to include in a pitch deck such as clearly presenting the problem, solution, business model, growth and traction, and team. It also identifies some common pitches sins to avoid like burying the main point, having an unclear thesis, including too much information, pretending there is no competition, and unrealistic assumptions about the market. The presentation emphasizes iteratively refining the pitch deck but notes the presenter's role is not as a "PowerPoint master."

AWS Startup Day Guadalajara - Pitch essentials

This document contains slides from a presentation given by Francisco Ruiz on creating successful pitches and avoiding common mistakes. The presentation discusses key elements to include in a pitch deck such as the problem, solution, business model, growth and traction, and team. It also identifies some "deadly sins" to avoid like burying the main point, lacking focus, including irrelevant information, pretending the company has no competition, and unrealistic projections. The presentation emphasizes iteratively refining the pitch deck but notes the presenter's role is not as a "PowerPoint master."

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The document discusses Amazon's AI and machine learning journey, highlighting six key lessons learned: (1) maintain a sense of urgency and customer obsession as if it is still day one, (2) act with agility to stay ahead of or keep up with changes, (3) modernize applications using microservices and automation, (4) recognize that general artificial intelligence has not been achieved and specific models are needed, (5) determine when to build custom models versus using pre-trained ones, and (6) apply a continuous machine learning flywheel of data collection, model training, evaluation and improvement. The document emphasizes Amazon's focus on customers, innovation, and using AI/ML to enhance operations.

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The document discusses a presentation about mastering digital channels through APIs. It begins with an agenda that covers the digital world of CMOs/CDOs, companies that are doing it well using APIs, what to do next, and Q&A. It then provides details on the evolution of the digital world from the first generation web to today's SMAC stack challenges. It also discusses how Amazon has mastered digital channels through vision, focus on data and APIs, agility, and persistence in broadening their offerings.

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The document discusses strategies for efficiently raising a seed round of funding. It begins by establishing ground rules for fundraising, such as dedicating one person to fundraising full-time and fundraising in parallel rather than sequentially. It also discusses crafting an effective fundraising narrative in 20 seconds or less that communicates what the company does, its most compelling feature, progress to date and future milestones. The document then covers prospecting an initial funnel of potential investors by working backwards from an ideal cap table structure.

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This document discusses challenges faced by retailers with their ecommerce systems and strategies for modernizing systems on AWS. It provides an overview of typical ecommerce architectures and principles for lifting and shifting applications to AWS. Case studies are presented of retailers like Sainsbury's and The White Company that modernized their Oracle Commerce systems on AWS and achieved benefits like reduced costs, increased scalability and availability, and faster delivery of changes. The document also provides recommendations for what components of an Oracle ecommerce system can be initially lifted and shifted to AWS.

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On the road to digitalization, manufacturing companies must increase their operations efficiency and accelerate their innovation rhythms. AWS provides tools and best practices for incorporating digital capabilities to achieve manufacturing business objectives without compromising security and governance and while reducing cost structures. It’s time to drive change and emerge better prepared to face current and future business environments. A strategy based on digital processes, tools, and data-driven decisions that enhances the value for customers is making a difference in the success of manufacturing companies. Learn about best practices, customer cases, and examples of companies that embarked on the digitalization journey and emerged as market leaders.

Initiate Edinburgh 2019 - Moving to DevOps the Amazon Way

DevOps is currently one of the most sought after engineering models. One reason is that it helps enterprise transformations. The Amazon transformation to DevOps was born out of the desire to be even more customer obsessed, more agile, and more innovative. Come and learn from our journey as we share the playbook that helped us successfully implement and adopt DevOps as well as the lessons we learned the hard way.

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The document discusses different database models and their common use cases. It describes relational, key-value, document, in-memory, graph, and time-series database models. For each model it provides examples of common data structures, features, and example use cases such as ERP/CRM systems, shopping carts, content management, fraud detection, and IoT applications. The goal is to help readers understand which database model to select based on their specific data and requirements.

AWS Summit Singapore 2019 | Driving Business Outcomes with Data Lake on AWS

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The document discusses how companies can drive business outcomes through building a data lake on AWS. It provides examples of how media and banking companies have used data lakes on AWS to improve customer experiences, gain operational efficiencies, and derive business value. The speaker then discusses HDFC Life's data journey and architecture, and provides an overview of BlazeClan Technologies, the consulting company he represents.

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The document discusses agile transformation in the cloud. It notes that traditional IT organizations face challenges like slow time-to-market, high technical debt, and inflexible platforms. It then outlines eight new principles for organizations looking to adopt cloud-native agile practices, such as acting like a startup, embracing cloud computing, and cultivating DevOps. It also shares the experience of Copa Airlines in transforming its IT organization using AWS cloud services to improve agility, collaboration, and customer experience.

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