Westpac Bank Tech Talk 2: Introduction to Streaming Data and Stream Processing with Apache Kafka®
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Watch the webcast here: https://videos.confluent.io/watch/P5up2YQX9QdVMhmYfsXy7Q Speaker: Brett Randall
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Event streaming allows companies to build more scalable and loosely coupled real-time applications supporting massive concurrency demands and simplifying the construction of services.
At the same time, API management provides capabilities to securely control the upstream services consumption, including the event processing infrastructure.
This session shows how Kong Konnect Enterprise can complement Kafka Event Streaming, exposing it to new and external consumers while applying specific and critical policies to control its consumption, including API key, OAuth/OIDC and others for authentication, rate limiting, caching, log processing, etc.
Maximize the Business Value of Machine Learning and Data Science with Kafka (...
Today, many companies that have lots of data are still struggling to derive value from machine learning (ML) and data science investments. Why? Accessing the data may be difficult. Or maybe it’s poorly labeled. Or vital context is missing. Or there are questions around data integrity. Or standing up an ML service can be cumbersome and complex.
At Nuuly, we offer an innovative clothing rental subscription model and are continually evolving our ML solutions to gain insight into the behaviors of our unique customer base as well as provide personalized services. In this session, I’ll share how we used event streaming with Apache Kafka® and Confluent Cloud to address many of the challenges that may be keeping your organization from maximizing the business value of machine learning and data science. First, you’ll see how we ensure that every customer interaction and its business context is collected. Next, I’ll explain how we can replay entire interaction histories using Kafka as a transport layer as well as a persistence layer and a business application processing layer. Order management, inventory management, logistics, subscription management – all of it integrates with Kafka as the common backbone. These data streams enable Nuuly to rapidly prototype and deploy dynamic ML models to support various domains, including pricing, recommendations, product similarity, and warehouse optimization. Join us and learn how Kafka can help improve machine learning and data science initiatives that may not be delivered to their full potential.
Matching the Scale at Tinder with Kafka
(Krunal Vora, Tinder) Kafka Summit San Francisco 2018
At Tinder, we have been using Kafka for streaming and processing events, data science processes and many other integral jobs. Forming the core of the pipeline at Tinder, Kafka has been accepted as the pragmatic solution to match the ever increasing scale of users, events and backend jobs. We, at Tinder, are investing time and effort to optimize the usage of Kafka solving the problems we face in the dating apps context. Kafka forms the backbone for the plans of the company to sustain performance through envisioned scale as the company starts to grow in unexplored markets. Come, learn about the implementation of Kafka at Tinder and how Kafka has helped solve the use cases for dating apps. Engage in the success story behind the business case of Kafka at Tinder.
Benefits of Stream Processing and Apache Kafka Use Cases
Watch this talk here: https://www.confluent.io/online-talks/benefits-of-stream-processing-and-apache-kafka-use-cases-on-demand
This talk explains how companies are using event-driven architecture to transform their business and how Apache Kafka serves as the foundation for streaming data applications.
Learn how major players in the market are using Kafka in a wide range of use cases such as microservices, IoT and edge computing, core banking and fraud detection, cyber data collection and dissemination, ESB replacement, data pipelining, ecommerce, mainframe offloading and more.
Also discussed in this talk are the differences between Apache Kafka and Confluent Platform.
This session is part 1 of 4 in our Fundamentals for Apache Kafka series.
Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...
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.
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...
In this talk, we'll discuss how VillageMD is able to use Kafka topic compaction for rapidly scaling our reprocessing pipelines to encompass hundreds of feeds. Within healthcare data ecosystems, privacy and data minimalism are key design priorities. Being able to handle data deletion in a reliable, timely manner within event-driven architectures is becoming more and more necessary with key governance frameworks like the GDPR and HIPAA.
We'll be giving an overview of the building and governance of dead-letter queues for streaming data processing.
We'll discuss:
1. How to architect a data sink for failed records.
2. How topic compaction can reduce duplicate data and enable idempotency.
3. Building a tombstoning system for removing successfully reprocessed records from the queues.
4. Considerations for monitoring a reprocessing system in production -- what metrics, dataops, and SLAs are useful?
Deep Dive Series #3: Schema Validation + Structured Audit Logs
Eine weitere neue sicherheitsrelevante Funktion in Confluent Platform 5.4 sind Structured Audit Logs. Jetzt ist natürlich alles in Kafka ein Log, aber Kafka protokolliert nicht, was Kafka mit Kafka macht - nur das, was in einen Topics geschrieben wird.
Im dritten Teil der Deep Dive Sessions besprechen wir neben den Structured Audit Logs außerdem die "Weiterentwicklung" der bereits bekannten Schema Registry: Die Schema Validation agiert auf dem Topic-Level und stellt sicher, dass jede einzelne Message, die zu einem bestimmten Topic erstellt wird in der Schema Registry überprüft wird. Mehr dazu erklären wir in unserem Deep Dive #3.
What is Apache Kafka®?
Viktor Gamov, Confluent, Developer Advocate
Apache Kafka is an open source distributed streaming platform that allows you to build applications and process events as they occur. Viktor Gamov (developer Advocate at Confluent) walks through how it works and important underlying concepts. As a real-time, scalable, and durable system, Kafka can be used for fault-tolerant storage as well as for other use cases, such as stream processing, centralized data management, metrics, log aggregation, event sourcing, and more.
This talk will explain what a 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.
https://www.meetup.com/Chennai-Kafka/events/269942117/
Developing custom transformation in the Kafka connect to minimize data redund...
Compacted topics grow over time and are often utilizing high performance, low latency and relatively expensive storage solutions. Reducing duplicated data plays a critical role in the size of compacted topics. with less data on the topics, the Kafka cluster consumes less disk space which in turn it leads to lower operation cost.
in this use case-driven talk, we are going to demonstrate how our team at UnitedHealth Group leveraged existing transformers to extract data from the message metadata in the topic as well as how we developed our customized transformers to minimize the amount of duplicated data in each message in the topic.
5 lessons learned for successful migration to Confluent cloud | Natan Silinit...
Confluent Cloud makes Devops engineers lives a lot more easier.
Yet moving 1500 microservices, 10K topics and 100K partitions to a multi-cluster Confluent cloud can be a challenge.
In this talk you will hear about 5 lessons that Wix has learned in order to successfully meet this challenge.
These lessons include:
1. Automation, Automation, Automation - all the process has to be completely automated at such scale
2. Prefer a gradual approach - E.g. migrate topics in small chunks and not all at once. Reduces risks if things go bad
3. Cleanup first - avoid migrating unused topics or topics with too many unnecessary partitions
SIEM Modernization: Build a Situationally Aware Organization with Apache Kafka®
Watch this talk here: https://www.confluent.io/online-talks/siem-modernization-build-a-situationally-aware-organization-with-apache-kafka
Of all security breaches, 85% are conducted with compromised credentials, often at the administration level or higher. A lot of IT groups think “security” means authentication, authorization and encryption (AAE), but these are often tick-boxes that rarely stop breaches. The internal threat surfaces of data streams or disk drives in a raidset in a data centre are not the threat surface of interest.
Cyber or Threat organizations must conduct internal investigations of IT, subcontractors and supply chains without implicating the innocent. Therefore, they are organizationally air-gapped from IT. Some surveys indicate up to 10% of IT is under investigation at any given time.
Deploying a signal processing platform, such as Confluent Platform, allows organizations to evaluate data as soon as it becomes available enabling them to assess and mitigate risk before it arises. In Cyber or Threat Intelligence, events can be considered signals, and when analysts are hunting for threat actors, these don't appear as a single needle in a haystack, but as a series of needles. In this paradigm, streams of signals aggregate into signatures. This session shows how various sub-systems in Apache Kafka can be used to aggregate, integrate and attribute these signals into signatures of interest.
In this talk you will learn:
-The current threat landscape
-The difference between Security and Threat Intelligence
-The value of Confluent platform as an ideal complement to hardware endpoint detection systems and batch-based SIEM warehouses
What is Apache Kafka and What is an Event Streaming Platform?
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.
Kafka in Context, Cloud, & Community (Simon Elliston Ball, Cloudera) Kafka Su...
The document discusses Kafka in the context of cloud platforms and open source communities. It describes several Apache projects that can be used with Kafka, such as Apache NiFi for data collection, Apache Flink for stream processing, and Apache Ranger for security. It also outlines features of Cloudera's platform for managing Kafka deployments, including unified governance tools, monitoring, and services to simplify operations. Finally, it discusses how Kafka can be deployed across cloud, on-premise, and hybrid environments with auto-scaling and other management capabilities.
Operational Analytics on Event Streams in Kafka
Speaker: Anirudh Ramanthan, Product Manager, Rockset
Tracking key events and analyzing these event streams are critical to many enterprises. We highlight how organizations are using Apache Kafka® as a fast, reliable event streaming platform alongside Rockset, a serverless search and analytics engine, to create stateful microservices to analyze their event streams.
In this talk, we will discuss a stateful microservices architecture, where events from multiple channels are collected and streamed into Kafka and continuously ingested into Rockset with no explicit schema or metadata specification required. Developers then use serverless compute frameworks, like AWS Lambda, in conjunction with serverless data management from Rockset to build microservices to derive insights on the data from Kafka. Organizations can leverage this pattern to support low-latency queries on event streams, providing immediate insight on their business.
Nordstrom's Event-Sourced Architecture and Kafka-as-a-Service | Adam Weyant a...
As a 120 year-old company, Nordstrom was facing numerous challenges as a result of an aging, service-oriented, architecture. Developers needing to implement reporting for analytics separately from core functionality resulted in questionable data quality for analytical purposes. Scaling dependent services in harmony to not overwhelm each other was a struggle faced by many, if not most, teams. Several years into a company-wide transition to an event-sourced architecture, Nordstrom has solved these and various other problems. By leveraging the capabilities of Apache Kafka and Confluent, combined with a deep organizational focus on well-defined business event schemas, a singular event can be used for analytical, functional, operational, and model building purposes. This session will describe this architecture and the lessons learned while building it, with a focus on the internally built, multi-tenant, multi-cluster, Kafka-as-a-Service platform that enables it.
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Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...
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Developing custom transformation in the Kafka connect to minimize data redund...
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5 lessons learned for successful migration to Confluent cloud | Natan Silinit...
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Similar to Westpac Bank Tech Talk 2: Introduction to Streaming Data and Stream Processing with Apache Kafka®
What's new in Confluent 3.2 and Apache Kafka 0.10.2
With the introduction of connect and streams API in 2016, Apache Kafka is becoming the defacto solution for anyone looking to build a streaming platform. The community continues to add additional capabilities to make it the complete solution for streaming data.
Join us as we review the latest additions in Apache Kafka 0.10.2. In addition, we’ll cover what’s new in Confluent Enterprise 3.2 that makes it possible for running Kafka at scale.
2018 07-11 - kafka integration patterns
During this meet up we will see different approach to integrate Apache Kafka in an enterprise architrecture
All Day DevOps - FLiP Stack for Cloud Data Lakes
https://www.alldaydevops.com/addo-speakers/timothy-spann
Timothy Spann
StreamNative
MODERN INFRASTRUCTURE
SHARE THIS SESSION
Session Name: FLiP Stack for Cloud Data Lakes
Utilizing an all Apache stack for Rapid Data Lake Population and querying utilizing Apache Flink, Apache Pulsar, and Apache NiFi. We can quickly stream data to and from any datalake, data lake house, lakehouse, database or any datamart regardless of cloud or size. FLiP allows for Java and Python developers to build scalable solutions that span messaging and streaming in cloud native fashion with full monitoring.
Speaker Bio:
Tim Spann is a Developer Advocate @ StreamNative where he works with Apache Pulsar, Apache Flink, Apache NiFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData, and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, Pulsar, NiFi, the blockchain, and Spark.
Netflix Keystone—Cloud scale event processing pipeline
http://www.oreilly.com/pub/e/3764
Keystone processes over 700 billion events per day (1 peta byte) with at-least-once processing semantics in the cloud. Monal Daxini details how they used Kafka, Samza, Docker, and Linux at scale to implement a multi-tenant pipeline in AWS cloud within a year. He'll also share plans on offering a Stream Processing as a Service for all of Netflix use.
[Demo session] 관리형 Kafka 서비스 - Oracle Event Hub Service
오라클 클라우드에서는 카프카를 관리형 서비스로 제공합니다. 밋업 세션에서는 관리형 카프카 서비스의 편의성을 소개하고 카프카 서비스의 데모를 진행합니다. 또한 MSA, 빅데이터 및 Blockchain의 인프라로 카프카가 핵심 위치를 갖는 것 뿐만 아니라 오라클 클라우드의 통합 핵심 컴포넌트로 카프카는 중요한 의미를 갖습니다.
오라클 클라우드의 통합 컴포넌트로 카프카의 역할과 주요 서비스의 구성을 소개합니다.
* 본 세션은 “입문자/초급자/중급자” 분들께 두루 적합한 세션입니다.
Apache kafka-a distributed streaming platform
Paolo Castagna is a Senior Sales Engineer at Confluent. His background is on 'big data' and he has, first hand, saw the shift happening in the industry from batch to stream processing and from big data to fast data. His talk will introduce Kafka Streams and explain why Apache Kafka is a great option and simplification for stream processing.
Apache Kafka - A Distributed Streaming Platform
A high level and gentle introduction to Apache Kafka and in particular Kafka Streams APIs (part of Apache Kafka).
Cloud lunch and learn real-time streaming in azure
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Apache pulsar is an open source, cloud-native distributed messaging and streaming platform.
ApacheCon 2021 Apache Deep Learning 302
ApacheCon 2021 Apache Deep Learning 302
Tuesday 18:00 UTC
Apache Deep Learning 302
Timothy Spann
This talk will discuss and show examples of using Apache Hadoop, Apache Kudu, Apache Flink, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications. This is the follow up to previous talks on Apache Deep Learning 101 and 201 and 301 at ApacheCon, Dataworks Summit, Strata and other events. As part of this talk, the presenter will walk through using Apache MXNet Pre-Built Models, integrating new open source Deep Learning libraries with Python and Java, as well as running real-time AI streams from edge devices to servers utilizing Apache NiFi and Apache NiFi - MiNiFi. This talk is geared towards Data Engineers interested in the basics of architecting Deep Learning pipelines with open source Apache tools in a Big Data environment. The presenter will also walk through source code examples available in github and run the code live on Apache NiFi and Apache Flink clusters.
Tim Spann is a Developer Advocate @ StreamNative where he works with Apache NiFi, Apache Pulsar, Apache Flink, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a senior solutions architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
* https://github.com/tspannhw/ApacheDeepLearning302/
* https://github.com/tspannhw/nifi-djl-processor
* https://github.com/tspannhw/nifi-djlsentimentanalysis-processor
* https://github.com/tspannhw/nifi-djlqa-processor
* https://www.linkedin.com/pulse/2021-schedule-tim-spann/
[Spark Summit EU 2017] Apache spark streaming + kafka 0.10 an integration story
Slides from the "Apache Spark Streaming + Kafka 0.10: an Integration Story" session at Spark Summit EU 2017
Apache Spark Streaming + Kafka 0.10 with Joan Viladrosariera
Spark Streaming has supported Kafka since it’s inception, but a lot has changed since those times, both in Spark and Kafka sides, to make this integration more fault-tolerant and reliable.Apache Kafka 0.10 (actually since 0.9) introduced the new Consumer API, built on top of a new group coordination protocol provided by Kafka itself. So a new Spark Streaming integration comes to the playground, with a similar design to the 0.8 Direct DStream approach. However, there are notable differences in usage, and many exciting new features. In this talk, we will cover what are the main differences between this new integration and the previous one (for Kafka 0.8), and why Direct DStreams have replaced Receivers for good. We will also see how to achieve different semantics (at least one, at most one, exactly once) with code examples. Finally, we will briefly introduce the usage of this integration in Billy Mobile to ingest and process the continuous stream of events from our AdNetwork.
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...
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Spark Streaming + Kafka 0.10: an integration story by Joan Viladrosa Riera at...
This document provides an overview of Apache Kafka and Spark Streaming and their integration. It discusses:
- What Apache Kafka is and how it works as a publish-subscribe messaging system with topics, partitions, producers, and consumers.
- What Apache Spark Streaming is and how it provides streaming data processing using micro-batching and leveraging Spark's APIs and engine.
- The evolution of the integration between Kafka and Spark Streaming, from using receivers to the direct approach without receivers in Spark 1.3+.
- Details on how to use the new direct Kafka integration in Spark 2.0+ including location strategies, consumer strategies, and committing offsets directly to Kafka.
- Considerations around at-least
OSS EU: Deep Dive into Building Streaming Applications with Apache Pulsar
OSS EU: Deep Dive into Building Streaming Applications with Apache Pulsar
In this session I will get you started with real-time cloud native streaming programming with Java, Golang, Python and Apache NiFi. If there’s a preferred language that the attendees pick, we will focus only on that one. I will start off with an introduction to Apache Pulsar and setting up your first easy standalone cluster in docker. We will then go into terms and architecture so you have an idea of what is going on with your events. I will then show you how to produce and consume messages to and from Pulsar topics. As well as using some of the command line and REST interfaces to monitor, manage and do CRUD on things like tenants, namespaces and topics. We will discuss Functions, Sinks, Sources, Pulsar SQL, Flink SQL and Spark SQL interfaces. We also discuss why you may want to add protocols such as MoP (MQTT), AoP (AMQP/RabbitMQ) or KoP (Kafka) to your cluster. We will also look at WebSockets as a producer and consumer. I will demonstrate a simple web page that sends and receives Pulsar messages with basic JavaScript. After this session you will be able to build simple real-time streaming and messaging applications with your chosen language or tool of your choice.
apache pulsar
Monitoring Apache Kafka with Confluent Control Center
Presentation by Nick Dearden, Direct, Product and Engineering, Confluent
It’s 3 am. Do you know how your Kafka cluster is doing?
With over 150 metrics to think about, operating a Kafka cluster can be daunting, particularly as a deployment grows. Confluent Control Center is the only complete monitoring and administration product for Apache Kafka and is designed specifically for making the Kafka operators life easier.
Join Confluent as we cover how Control Center is used to simplify deployment, operability, and ensure message delivery.
Watch the recording: https://www.confluent.io/online-talk/monitoring-and-alerting-apache-kafka-with-confluent-control-center/
Apache Kafka - Scalable Message-Processing and more !
ndependent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can me make sure that all these event are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
Trivadis TechEvent 2016 Apache Kafka - Scalable Massage Processing and more! ...
In this presentation Guido Schmutz talks about Apache Kafka, Kafka Core, Kafka Connect, Kafka Streams, Kafka and "Big Data"/"Fast Data Ecosystems, Confluent Data Platform and Kafka in Architecture.
Apache Kafka - Scalable Message Processing and more!
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
OSSNA Building Modern Data Streaming Apps
OSSNA
Building Modern Data Streaming Apps
https://ossna2023.sched.com/event/1Jt05/virtual-building-modern-data-streaming-apps-with-open-source-timothy-spann-streamnative
Timothy Spann
Cloudera
Principal Developer Advocate
Data in Motion
In my session, I will show you some best practices I have discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In my modern approach, we utilize several open-source frameworks to maximize all the best features. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there, we build streaming ETL with Apache Spark and enhance events with Pulsar Functions for ML and enrichment. We make continuous queries against our topics with Flink SQL. We will stream data into various open-source data stores, including Apache Iceberg, Apache Pinot, and others. We use the best streaming tools for the current applications with the open source stack - FLiPN. https://www.flipn.app/ Updates: This will be in-person with live coding based on feedback from the crowd. This will also include new data stores, new sources, and data relevant to and from the Vancouver area. This will also include updates to the platforms and inclusion of Apache Iceberg, Apache Pinot and some other new tech.
https://github.com/tspannhw/SpeakerProfile Tim Spann is a Principal Developer Advocate for Cloudera. He works with Apache Kafka, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a 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 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.
Timothy J Spann
Cloudera
Principal Developer Advocate
Hightstown, NJ
Websitehttps://datainmotion.dev/
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