Data Integration with Apache Kafka: What, Why, How
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Presented at Orange County Advanced Analytics and Big Data Meetup, June 21 2019. Apache Kafka has fast become the dominant messaging technology for the enterprise; if you're a data scientist or data engineer and you have not yet worked with Kafka, that situation will likely change soon! In this session, Pat Patterson, director of evangelism at StreamSets, explains what Kafka is, why it has disrupted the previous generation of messaging products, and how you can use open source products to build dataflow pipelines with Kafka, without writing code.
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Timothy J Spann
Cloudera
Principal Developer Advocate
Hightstown, NJ
Websitehttps://datainmotion.dev/
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Enabling Insight to Support World-Class Supercomputing (Stefan Ceballos, Oak ...
The Oak Ridge Leadership Facility (OLCF) in the National Center for Computational Sciences (NCCS) division at Oak Ridge National Laboratory (ORNL) houses world-class high-performance computing (HPC) resources and has a history of operating top-ranked supercomputers on the TOP500 list, including the world's current fastest, Summit, an IBM AC922 machine with a peak of 200 petaFLOPS. With the exascale era rapidly approaching, the need for a robust and scalable big data platform for operations data is more important than ever. In the past when a new HPC resource was added to the facility, pipelines from data sources spanned multiple data sinks which oftentimes resulted in data silos, slow operational data onboarding, and non-scalable data pipelines for batch processing. Using Apache Kafka as the message bus of the division's new big data platform has allowed for easier decoupling of scalable data pipelines, faster data onboarding, and stream processing with the goal to continuously improve insight into the HPC resources and their supporting systems. This talk will focus on the NCCS division's transition to Apache Kafka over the past few years to enhance the OLCF's current capabilities and prepare for Frontier, OLCF's future exascale system; including the development and deployment of a full big data platform in a Kubernetes environment from both a technical and cultural shift perspective. This talk will also cover the mission of the OLCF, the operational data insights related to high-performance computing that the organization strives for, and several use-cases that exist in production today.
Kafka as your Data Lake - is it Feasible?
For a long time we discuss how much data we can keep in Kafka. Can we store data forever or do we remove data after a while and maybe having the history in a data lake on Object Storage or HDFS? With the advent of Tiered Storage in Confluent Enterprise Platform, storing data much longer in Kafka is much very feasible. So can we replace a traditional data lake with just Kafka? Maybe at least for the raw data? But what about accessing the data, for example using SQL?
KSQL allows for processing data in a streaming fashion using an SQL like dialect. But what about reading all data of a topic? You can reset the offset and still use KSQL. But there is another family of products, so-called query engines for Big Data. They originate from the idea of reading Big Data sources such as HDFS, object storage or HBase, using the SQL language. Presto, Apache Drill and Dremio are the most popular solutions in that space. Lately these query engines also added support for Kafka topics as a source of data. With that you can read a topic as a table and join it with information available in other data sources. The idea of course is not real-time streaming analytics but batch analytics directly on the Kafka topic, without having to store it in a big data storage.
This talk answers, how well these tools support Kafka as a data source. What serialization formats do they support? Is there some form of predicate push-down supported or do we have to always read the complete topic? How performant is a query against a topic, compared to a query against the same data sitting in HDFS or an object store? And finally, will this allow us to replace our data lake or at least part of it by Apache Kafka?
Evolving from Messaging to Event Streaming
Speakers:
- Perry Krol, Manager Solutions Engineer
- Mic Hussey, Staff Solutions Engineer
dotScale 2017 Keynote: The Rise of Real Time by Neha Narkhede
Slides from Neha Narkhede's keynote at the dotScale conference in Paris on April 24th, 2017.
There is a tectonic shift happening in how data powers the core of a company's business. This shift is about the rise of real-time. Apache Kafka was built with the vision to help companies navigate this change and become the central nervous system that makes data available in real-time to all the applications that need to use it.
This talk is about how you can put Apache Kafka to practice to help your company make this shift to real-time. And how the Connect and Streams API in Apache Kafka capture the entire scope of what it means to put streams into practice.
Developing a custom Kafka connector? Make it shine! | Igor Buzatović, Porsche...
Some people see their cars just as a means to get them from point A to point B without breaking down halfway, but most of us want it also to be comfortable, performant, easy to drive, and of course - to look good.
We can think of Kafka Connect connectors in a similar way. While the main focus is on getting data from or writing data to the external target system, it’s also relevant how easy it is to configure, does it scale well, does it provide the best possible data consistency, is it resilient to both the external system and Kafka cluster failures, and so on. This talk focuses on aspects of connector plugin development important for achieving these goals. More specifically - we‘ll cover configuration definition and validation, external source partitions and offsets handling, achieving desired delivery semantics, and more."
Kafka as your Data Lake - is it Feasible? (Guido Schmutz, Trivadis) Kafka Sum...
For a long time we discuss how much data we can keep in Kafka. Can we store data forever or do we remove data after a while and maybe having the history in a data lake on Object Storage or HDFS? With the advent of Tiered Storage in Confluent Enterprise Platform, storing data much longer in Kafka is much very feasible. So can we replace a traditional data lake with just Kafka? Maybe at least for the raw data? But what about accessing the data, for example using SQL?
KSQL allows for processing data in a streaming fashion using an SQL like dialect. But what about reading all data of a topic? You can reset the offset and still use KSQL. But there is another family of products, so-called query engines for Big Data. They originate from the idea of reading Big Data sources such as HDFS, object storage or HBase, using the SQL language. Presto, Apache Drill and Dremio are the most popular solutions in that space. Lately these query engines also added support for Kafka topics as a source of data. With that you can read a topic as a table and join it with information available in other data sources. The idea of course is not real-time streaming analytics but batch analytics directly on the Kafka topic, without having to store it in a big data storage.
This talk answers, how well these tools support Kafka as a data source. What serialization formats do they support? Is there some form of predicate push-down supported or do we have to always read the complete topic? How performant is a query against a topic, compared to a query against the same data sitting in HDFS or an object store? And finally, will this allow us to replace our data lake or at least part of it by Apache Kafka?
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.
Introducing Events and Stream Processing into Nationwide Building Society (Ro...
Facing Open Banking regulation, rapidly increasing transaction volumes and increasing customer expectations, Nationwide took the decision to take load off their back-end systems through real-time streaming of data changes into Kafka. Hear about how Nationwide started their journey with Kafka, from their initial use case of creating a real-time data cache using Change Data Capture, Kafka and Microservices to how Kafka allowed them to build a stream processing backbone used to reengineer the entire banking experience including online banking, payment processing and mortgage applications. See a working demo of the system and what happens to the system when the underlying infrastructure breaks. Technologies covered include: Change Data Capture, Kafka (Avro, partitioning and replication) and using KSQL and Kafka Streams Framework to join topics and process data.
How to Build an Apache Kafka® Connector
Apache Kafka® is the technology behind event streaming which is fast becoming the central nervous system of flexible, scalable, modern data architectures. Customers want to connect their databases, data warehouses, applications, microservices and more, to power the event streaming platform. To connect to Apache Kafka, you need a connector!
This online talk dives into the new Verified Integrations Program and the integration requirements, the Connect API and sources and sinks that use Kafka Connect. We cover the verification steps and provide code samples created by popular application and database companies. We will discuss the resources available to support you through the connector development process.
This is Part 2 of 2 in Building Kafka Connectors - The Why and How
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Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms.
Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.
Removing performance bottlenecks with Kafka Monitoring and topic configuration
Apache Kafka is a distributed messaging system used to build real-time data pipelines & streaming applications. Since applications rely heavily on efficient data transfer, message passing platforms like Kafka cannot afford a breakdown or poor performance.
But how do we ensure that Kafka is running well and successfully streaming messages at low latency? This is where Kafka monitoring steps in.
Here’s the agenda of the webinar -
> Why Kafka monitoring?
> Top 10 Kafka metrics to focus on
> How to change Kafka topic configuration at runtime?
Streaming Visualization
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One was is to first persist the data into a data store and then use a traditional data visualisation solution to present the data.
If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and highlights some of the products available to implement these blueprints.
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.
SingleStore & Kafka: Better Together to Power Modern Real-Time Data Architect...
To remain competitive, organizations need to democratize access to fast analytics, not only to gain real-time insights on their business but also to power smart apps that need to react in the moment. In this session, you will learn how Kafka and SingleStore enable modern, yet simple data architecture to analyze both fast paced incoming data as well as large historical datasets. In particular, you will understand why SingleStore is well suited process data streams coming from Kafka.
PCAP Graphs for Cybersecurity and System Tuning
Cybersecurity is a broad topic and many commercial products are related to it. We demonstrate a fundamental concept in network analysis: re-construction and visualization of temporal networks. Furthermore, we apply the method to describe operational conditions of a Hadoop cluster. Our experiments provide first results and allow a classification of the cluster state related to current workloads. The temporal networks show significant differences for different operation modes. In reallity we would expect mixed workloads. If such workload parameters are known, we are able to handle a-typical events accordingly - which means, we are able to create alerts based on context information, rather than only the package content. We show an end-to-end example: (1) Data collection is done via python, using the sniffer script; (2) using Apache Hive and Apache Spark we analyze the network traffic data and create the temporary network. Finally, we are able to visualize the results using Gephi in step (3). In a next step, we plan to contribute to the Apache Spot project.
Confluent REST Proxy and Schema Registry (Concepts, Architecture, Features)
High level introduction to Confluent REST Proxy and Schema Registry (leveraging Apache Avro under the hood), two components of the Apache Kafka open source ecosystem. See the concepts, architecture and features.
Mind the App: How to Monitor Your Kafka Streams Applications | Bruno Cadonna,...
You cannot operate what you cannot measure. In this talk, I am going to present the built-in metrics framework of Kafka Streams that supports monitoring Kafka Streams applications. You will learn how to setup monitoring of metrics for your Kafka Streams applications and you will hear about the following recent improvements to the metrics framework that aim to extend and simplify monitoring. KIP-444 aims to simplify and extend the built-in metrics framework. The RocksDB metrics introduced in KIP-471 and KIP-607 allow you to look directly into the built-in persistent state stores of your Kafka Streams applications. Finally, KIP-613 specifies metrics that measure end-to-end latencies in your applications. This talk will help you collect intel about the behavior of your Kafka Streams applications, and will allow you to reason about the deployment. In the end, you will be able to better understand your applications and run them in a more robust manner.
ETL as a Platform: Pandora Plays Nicely Everywhere with Real-Time Data Pipelines
ETL can be painful with dirty data and outdated batch processes slowing you down; there has to be a better way. In this talk we’ll discuss the benefits of introducing a streaming platform to your architecture including how it can greatly simplify complexity, speed up performance, and help your team deliver the features they need with real-time data integration.
Pandora’s Lawrence Weikum will discuss what they’ve done to bring real-time data integration to the team. We’ll review their Kafka-powered data pipelines and how they make the most of Kafka’s Connect API to make it surprisingly system to keep systems in sync.
Presented by:
Lawrence Weikum, Senior Software Engineer, Pandora
Gehrig Kunz, Technical Product Marketing Manager, Confluent
Writing Blazing Fast, and Production-Ready Kafka Streams apps in less than 30...
If you have already worked on various Kafka Streams applications before, then you have probably found yourself in the situation of rewriting the same piece of code again and again.
Whether it's to manage processing failures or bad records, to use interactive queries, to organize your code, to deploy or to monitor your Kafka Streams app, build some in-house libraries to standardize common patterns across your projects seems to be unavoidable.
And, if you're new to Kafka Streams you might be interested to know what are those patterns to use for your next streaming project.
In this talk, I propose to introduce you to Azkarra, an open-source lightweight Java framework that was designed to provide most of that stuffs off-the-shelf by leveraging the best-of-breed ideas and proven practices from the Apache Kafka community.
Can Apache Kafka Replace a Database?
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit and when it is not.
The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage and ksqlDB as event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Key takeaways:
- Kafka can store data forever in a durable and high available manner
- Kafka has different options to query historical data
- Kafka-native add-ons like ksqlDB or Tiered Storage make Kafka more powerful than ever before to store and process data
- Kafka does not provide transactions, but exactly-once semantics
- Kafka is not a replacement for existing databases like MySQL, MongoDB or Elasticsearch
- Kafka and other databases complement each other; the right solution has to be selected for a problem
- Different options are available for bi-directional pull and push-based integration between Kafka and databases to complement each other
Video Recording:
https://youtu.be/7KEkWbwefqQ
Blog post:
https://www.kai-waehner.de/blog/2020/03/12/can-apache-kafka-replace-database-acid-storage-transactions-sql-nosql-data-lake/
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Enabling Insight to Support World-Class Supercomputing (Stefan Ceballos, Oak ...
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dotScale 2017 Keynote: The Rise of Real Time by Neha Narkhede
dotScale 2017 Keynote: The Rise of Real Time by Neha Narkhede
Developing a custom Kafka connector? Make it shine! | Igor Buzatović, Porsche...
Developing a custom Kafka connector? Make it shine! | Igor Buzatović, Porsche...
Kafka as your Data Lake - is it Feasible? (Guido Schmutz, Trivadis) Kafka Sum...
Kafka as your Data Lake - is it Feasible? (Guido Schmutz, Trivadis) Kafka Sum...
Maximize the Business Value of Machine Learning and Data Science with Kafka (...
Maximize the Business Value of Machine Learning and Data Science with Kafka (...
Introducing Events and Stream Processing into Nationwide Building Society (Ro...
Introducing Events and Stream Processing into Nationwide Building Society (Ro...
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
Removing performance bottlenecks with Kafka Monitoring and topic configuration
Removing performance bottlenecks with Kafka Monitoring and topic configuration
What is Apache Kafka and What is an Event Streaming Platform?
What is Apache Kafka and What is an Event Streaming Platform?
SingleStore & Kafka: Better Together to Power Modern Real-Time Data Architect...
SingleStore & Kafka: Better Together to Power Modern Real-Time Data Architect...
Confluent REST Proxy and Schema Registry (Concepts, Architecture, Features)
Confluent REST Proxy and Schema Registry (Concepts, Architecture, Features)
Mind the App: How to Monitor Your Kafka Streams Applications | Bruno Cadonna,...
Mind the App: How to Monitor Your Kafka Streams Applications | Bruno Cadonna,...
ETL as a Platform: Pandora Plays Nicely Everywhere with Real-Time Data Pipelines
ETL as a Platform: Pandora Plays Nicely Everywhere with Real-Time Data Pipelines
Writing Blazing Fast, and Production-Ready Kafka Streams apps in less than 30...
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Apache kafka
Data Analytics is often described as one of the biggest challenges associated with big data, but even before that step can happen, data must be ingested and made available to enterprise users. That’s where Apache Kafka comes in.
OSSNA Building Modern Data Streaming Apps
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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/
Apache Pulsar: Why Unified Messaging and Streaming Is the Future - Pulsar Sum...
Data insights and data-driven strategies create the competitive differentiators companies thrive off today. The need for unified messaging and streaming has never been more apparent.
Pulsar started with the goal of building a global, geo-replicated infrastructure to serve Yahoo!’s messaging needs. With the increased need to process both business events (such as payment request, billing request) and operational events (such as log data, click events, etc), the team at Yahoo! set out to build a true unified infrastructure platform to handle all in-motion data. That technology became Apache Pulsar.
In this talk, Matteo Merli and Sijie Guo will dive into the landscape of unified messaging and streaming, how Pulsar helps companies achieve this vision, and what the future of Pulsar will look like.
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https://www.learntek.org/blog/apache-kafka/
https://www.learntek.org/
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https://www.learntek.org/
https://www.learntek.org/blog/apache-kafka/
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ITPC Building Modern Data Streaming Apps
ITPC Building Modern Data Streaming Apps
https://princetonacm.acm.org/tcfpro/
17th Annual IEEE IT Professional Conference (ITPC)
Armstrong Hall at The College of New Jersey
Friday, March 17th, 2023 at 8:30 AM to 5:00 PM
TCF Photo
In continuous operation since 1976, the Trenton Computer Festival (TCF) is the nation's longest running personal computer. For the seventeenth year, the TCF is extending its program to provide Information Technology and computer professionals with an additional day of conference. It is intended, in an economical way, to provide attendees with insight and information pertinent to their jobs, and to keep them informed of emerging technologies that could impact their work.
The IT Professional Conference is co-sponsored by the Institute of Electrical and Electronics Engineers (IEEE) Computer Society Chapter of Princeton / Central Jersey.
11:00am Building Modern Data Streaming Apps
presented by
Timothy Spann
Building Modern Data Streaming Apps
In this 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 Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there we build streaming ETL with Spark, enhance events with Pulsar Functions for ML and enrichment. We build continuous queries against our topics with Flink SQL.
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.
Logging infrastructure for Microservices using StreamSets Data Collector
Logging infrastructure for Microservices using StreamSets Data Collector
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and K...
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Cloudera
Data in Motion
https://budapestdata.hu/2023/en/speakers/timothy-spann/
Timothy Spann
Principal Developer Advocate
Cloudera (US)
LinkedIn · GitHub · datainmotion.dev
June 8 · Online · English talk
Building Modern Data Streaming Apps with NiFi, Flink and Kafka
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source 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 SQL. We will stream data into Apache Iceberg.
We use the best streaming tools for the current applications with FLaNK. flankstack.dev
BIO
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.
Event Detection Pipelines with Apache Kafka
Event Detection Pipelines with Apache Kafka
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JConWorld_ Continuous SQL with Kafka and Flink
JConWorld: Continuous SQL with Kafka and Flink
In this talk, I will walk through how someone can setup and run continous SQL queries against Kafka topics utilizing Apache Flink. We will walk through creating Kafka topics, schemas and publishing data.
We will then cover consuming Kafka data, joining Kafka topics and inserting new events into Kafka topics as they arrive. This basic over view will show hands-on techniques, tips and examples of how to do this.
Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, 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 Developer Advocate at StreamNative, 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://www.datainmotion.dev/p/about-me.html https://dzone.com/users/297029/bunkertor.html
https://www.youtube.com/channel/UCDIDMDfje6jAvNE8DGkJ3_w?view_as=subscriber
Leverage Kafka to build a stream processing platform
Leverage Kafka to build a stream processing platform, Staff Software Engineer - Frontend, Meroxa
Meetup link: https://www.meetup.com/boston-apache-kafka-meetup/events/291189903/
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AI&BigData Lab 2016. Сарапин Виктор: Размер имеет значение: анализ по требова...
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Как устроить анализ данных 40 млн. человек за 5 лет так, чтобы это выглядело почти в реальном времени.
Meetup: Streaming Data Pipeline Development
Meetup: Streaming Data Pipeline Development
In this interactive session, Tim will lead participants through how to best build streaming data pipelines. He will cover how to build applications from some common use cases and highlight tips, tricks, best practices and patterns.
He will show how to build the easy way and then dive deep into the underlying open source technologies including Apache NiFi, Apache Flink, Apache Kafka and Apache Iceberg.
If you wish to follow along, please download open source projects beforehand. You can also download this helpful streaming platform: https://docs.cloudera.com/csp-ce/latest/installation/topics/csp-ce-installing-ce.html
All source code and slides will be shared for those interested in building their own FLaNK Apps. https://www.flankstack.dev/
You can join the meeting virtually here:
https://cloudera.zoom.us/j/91603330726
Speaker - Tim 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.
Using FLiP with InfluxDB for EdgeAI IoT at Scale 2022
Using FLiP with InfluxDB for EdgeAI IoT at Scale 2022
https://adtmag.com/webcasts/2021/12/influxdata-february-10.aspx?tc=page0
Using FLiP with InfluxDB for EdgeAI IoT at Scale
Date: Thursday, February 10th at 11am PT / 2pm ET
Join this webcast as Timothy from StreamNative takes you on a hands-on deep-dive using Pulsar, Apache NiFi + Edge Flow Manager + MiniFi Agents with Apache MXNet, OpenVino, TensorFlow Lite, and other Deep Learning Libraries on the actual edge devices including Raspberry Pi with Movidius 2, Google Coral TPU and NVidia Jetson Nano.
The team runs deep learning models on the edge devices, sends images, and captures real-time GPS and sensor data. Their low-coding IoT applications provide easy edge routing, transformation, data acquisition and alerting before they decide what data to stream in real-time to their data space. These edge applications classify images and sensor readings in real-time at the edge and then send Deep Learning results to Flink SQL and Apache NiFi for transformation, parsing, enrichment, querying, filtering and merging data to InfluxDB.
In this session you will learn how to:
Build an end-to-end streaming edge app
Pull messages from Pulsar topics and persists the messages to InfluxDB
Build a data stream for IoT with NiFi and InfluxDB
Use Apache Flink + Apache Pulsar
Timothy Spann, Developer Advocate, StreamNative
Tim Spann is a Developer Advocate at StreamNative where he works with Apache NiFi, MiniFi, Kafka, 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 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.
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More from Pat Patterson
DevOps from the Provider Perspective
Keynote at Developer Week Global: Cloud on Sep 15, 2021.
There are many, many resources for DevOps engineers: learning paths, guides and tutorials for using tools such as Terraform, Packer and Ansible to save time in provisioning and configuring reliable, predictable systems. This session looks at the other side of the equation: creating the plugins, modules and providers that abstract away upstream APIs for use by DevOps tools.
Director of Developer Evangelism Pat Patterson will explain how Citrix implemented DevOps tooling for its App Delivery & Security products, and how the company is working with its community to create tooling for its Virtual Apps & Desktops Service. Pat will explain the different approaches to creating tooling, trade-offs between them, and the lessons that Citrix has learned along the way. This session will NOT be death-by-PowerPoint! Come prepared for semi-colons, curly braces and monospaced text!
How Imprivata Combines External Data Sources for Business Insights
Imprivata is a healthcare-focused IT security company. Using data integration tools from StreamSets, they built a pipeline to consolidate data from NetSuite, Salesforce, and Oracle in a cloud-based PostgreSQL data warehouse. Hear how their analytics team combined transaction, product, and support data in Einstein Analytics to support decision-making across their business functions: sales, marketing operations, product owners, and CxOs.
Dealing with Drift: Building an Enterprise Data Lake
Presented at San Diego Predictive Analytics Meetup, Dec 5, 2017
Cox Automotive comprises more than 25 companies dealing with different aspects of the car ownership lifecycle, with data as the common language they all share. The challenge for Cox was to create an efficient engine for the timely and trustworthy ingest of data capability for an unknown but large number of data assets from practically any source. Discover how their big data engineering team overcame data drift and are now populating a data lake, allowing analysts easy access to data from their subsidiary companies and producing new data assets unique to the industry.
Integrating with Einstein Analytics
Einstein Analytics (previously known as Wave Analytics) allows developers to not only create analytics applications, but also to create application templates that allow end-users to create their own analytics applications based on your master app. You, the developer, can define parameters and rules as part of the template, allowing the end-user to customize the app to their requirements. This Dreamforce 2017 session explains how to use Analytics Templates and the Analytics External Data API to automate the ingest of data from outside the platform, manipulating datasets and dataflows to provide a seamless experience for the user.
Efficient Schemas in Motion with Kafka and Schema Registry
Apache Avro allows data to be self-describing, but carries an overhead when used with message queues such as Apache Kafka. Confluent’s open source Schema Registry integrates with Kafka to allow Avro schemas to be passed ‘by reference’, minimizing overhead, and can be used with any application that uses Avro. Learn about Schema Registry, using it with Kafka, and leveraging it in your application.
Dealing With Drift - Building an Enterprise Data Lake
Data drift, the gradual morphing of data structure and semantics, is a fact of life in enterprise IT. New requirements force schema changes, the meaning of database columns changes over time, and infrastructure upgrades add new fields to log files. Left unchecked, drift in data sources can cause applications and dataflows to fail, with costly downtime and, in the worst case, corruption in downstream data stores.
Cox Automotive comprises more than 25 companies dealing with different aspects of the car ownership lifecycle, with data as the common language they all share. The challenge for Cox was to create an efficient engine for the timely and trustworthy ingest of data capability for an unknown but large number of data assets from practically any source. Discover how their big data engineering team overcame data drift and are now populating a data lake, allowing analysts easy access to data from their subsidiary companies and producing new data assets unique to the industry.
Building Data Pipelines with Spark and StreamSets
Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Metadata in upstream sources can ‘drift’ due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an Apache 2.0 licensed open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we’ll look at how SDC’s “intent-driven” approach keeps the data flowing, with a particular focus on clustered deployment with Spark and other exciting Spark integrations in the works.
Adaptive Data Cleansing with StreamSets and Cassandra
Presented at Cassandra Summit 2016.
Cassandra is a perfect fit for consuming high volumes of time-series data directly from users, devices, and sensors. Sometimes, though, when we consume data from the real world, systematic and random errors creep in. In this session, we'll see how to use open source tools like RabbitMQ and StreamSets Data Collector with Cassandra features such as User Defined Aggregates to collect, cleanse and ingest variable quality data at scale. Discover how to combine the power of Cassandra with the flexibility of StreamSets to implement adaptive data cleansing.
Building Custom Big Data Integrations
Big data ingest frameworks ship with an array of connectors for common data origins and destinations, such as flat files, S3, HDFS, Kafka etc, but sometimes, you need to send data to, or receive data from a system that's not on the list. StreamSets includes template code for building your own connectors and processors; we'll walk through the process of building a simple destination that sends data to a REST web service, and show how it can be extended to target more sophisticated systems such as Salesforce Wave Analytics.
Ingest and Stream Processing - What will you choose?
Pat Patterson and Ted Malaska talk about current and emerging technologies. They evaluate each and understand how they are useful in solving problems related to large scale data processing, joining and combining streams. They also talk about the various ways of achieving "at least once" and "exactly once" processing and how we can make sure that data is processed in a timely fashion.
Open Source Big Data Ingestion - Without the Heartburn!
Big Data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Upstream data sources can 'drift' due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail, inducing heartburn in even the most resilient data scientist. This session will survey the big data ingestion landscape, focusing on how open source tools such as Sqoop, Flume, Nifi and StreamSets can keep the data pipeline flowing.
Ingest and Stream Processing - What will you choose?
Presented at Hadoop Summit Dublin, April 13 2016.
All Aboard the Boxcar! Going Beyond the Basics of REST
One of the basic tenets of the REST paradigm is that resources are individually identified by URLs. REST APIs provide access to resources by HTTP operations such as GET and POST on those URLs. While this approach makes for a clean abstraction, it can be inefficient - an application that makes an HTTP request for each resource it wishes to act on may deliver a poor user experience, especially in a high latency environment such as mobile.
'Boxcarring' allows us to bundle multiple operations into a single HTTP call. For example, an application might POST a whole set of resource representations for creation. Resources carried in the boxcar can be independent, or form a tree of linked resources. This session will explore the boxcar approach, show an implementation in action, and discuss whether this can still be considered a RESTful approach.
Presented 9/30/15 at Integrate 2015.
Provisioning IDaaS - Using SCIM to Enable Cloud Identity
Presented at Silicon Valley IAM User Group, August 20, 2015
OData: Universal Data Solvent or Clunky Enterprise Goo? (GlueCon 2015)
Why would anyone but the most pedestrian enterprise developer be interested in a data access protocol originally designed by Microsoft, implemented in XML and handed to OASIS for standardization? The Open Data Protocol, or OData for short, has evolved into a clean, RESTful interface for CRUD operations against data services. Alongside the usual enterprise suspects such as Microsoft, Salesforce and IBM, OData has been adopted by government and non-profit agencies to open up their data and make it accessible to the public. For developers wanting to consume data, or create their own OData services, there's no shortage of open source options, from Apache Olingo in Java to node-odata and ODataCpp. Whether you're accessing customer orders in SAP or the Whitehouse visitor book, you're going to need some OData smarts.
Enterprise IoT: Data in Context
A look at IoT and Salesforce. Presented at Fog Conference, San Jose, November 20 2014.
OData: A Standard API for Data Access
The Open Data Protocol, or OData for short, provides a RESTful interface for CRUD operations against data services. OData services, such as Microsoft Azure, SAP, and WebSphere expose data and metadata as typed name/value pairs in JSON or XML, allowing 'off-the-shelf' data consumers to integrate with services without custom code. This session gives an overview of OData, and explains why salesforce.com selected it as a protocol to integrate with external data services.
API-Driven Relationships: Building The Trans-Internet Express of the Future
Presented at IRM Summit, June 4 2014. Abstract:
Move over Thomas the Tank Engine! If developers are the train conductors of the railway, then APIs
are the bullet trains of the future. In this session, Pat Patterson will explain how identity allows API
ecosystems to flourish, enabling developers to build ever more elaborate integrations. Whether it be
ExactTarget, Minecraft, or other great services, APIs are the basis for empowering developers to build
boundary-less railways across the web. ALLLLLL ABOOOOOOOOOAAAARD!
Using Salesforce to Manage Your Developer Community
Presented at Evans Data Developer Relations Bootcamp, 3/23/14
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How Imprivata Combines External Data Sources for Business Insights
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Dealing with Drift: Building an Enterprise Data Lake
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Efficient Schemas in Motion with Kafka and Schema Registry
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Dealing With Drift - Building an Enterprise Data Lake
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Adaptive Data Cleansing with StreamSets and Cassandra
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Ingest and Stream Processing - What will you choose?
Open Source Big Data Ingestion - Without the Heartburn!
Open Source Big Data Ingestion - Without the Heartburn!
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Ingest and Stream Processing - What will you choose?
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All Aboard the Boxcar! Going Beyond the Basics of REST
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OData: Universal Data Solvent or Clunky Enterprise Goo? (GlueCon 2015)
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Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Beyond Event Sourcing - Embracing CRUD for Wix Platform - Java.IL
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Enterprise Resource Planning System in Telangana
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Quarkus Hidden and Forbidden Extensions
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
GraphSummit Paris - The art of the possible with Graph Technology
Sudhir Hasbe, Chief Product Officer, Neo4j
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Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
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AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
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Prosigns: Transforming Business with Tailored Technology Solutions
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Understanding Globus Data Transfers with NetSage
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Launch Your Streaming Platforms in Minutes
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
How to Position Your Globus Data Portal for Success Ten Good Practices
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
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Data Integration with Apache Kafka: What, Why, How
- 1. 1© StreamSets, Inc. All rights reserved. Data Integration with Apache Kafka: What, Why, How Orange County Advanced Analytics and Big Data Meetup Pat Patterson pat@streamsets.com | @metadaddy
- 2. 2© StreamSets, Inc. All rights reserved. Who Am I? Pat Patterson / pat@streamsets.com / @metadaddy Past: Sun Microsystems, Salesforce Present: Director of Evangelism, StreamSets I run far ☺
- 3. 3© StreamSets, Inc. All rights reserved. Agenda What is Kafka? Why use Kafka? How do I use Kafka?
- 4. 4© StreamSets, Inc. All rights reserved. Open-source stream-processing software platform Developed at LinkedIn Donated to the Apache Software Foundation Commercialized as part of Confluent Platform In use by > 40% of F5001 across all industries. (1) Apache Kafka Refcard - https://dzone.com/refcardz/apache-kafka What is Apache Kafka?
- 5. 5© StreamSets, Inc. All rights reserved. Core - publish/subscribe messaging Streams - library for real-time apps and microservices Connect - integration framework Kafka Components
- 6. 6© StreamSets, Inc. All rights reserved. Topic - logical stream of records Partition - disjoint, ordered set of records within a topic Offset - unique sequential record index, per partition Record - key, value, timestamp, headers Broker - stores records in partitions Cluster - comprises multiple brokers Kafka Terminology
- 7. 7© StreamSets, Inc. All rights reserved. From https://kafka.apache.org/intro.html Kafka Partition
- 8. 8© StreamSets, Inc. All rights reserved. Kafka Topic From https://kafka.apache.org/intro.html
- 9. 9© StreamSets, Inc. All rights reserved. Putting It All Together Kafka Cluster Topic Partition Partition Partition Topic Partition Partition Partition Topic Partition Partition Partition Broker Broker Broker
- 10. 10© StreamSets, Inc. All rights reserved. Kafka Consumer Group From https://kafka.apache.org/documentation/
- 11. 11© StreamSets, Inc. All rights reserved. Contrast with Traditional Message Queues Message Queue Kafka ~30-40K messages/sec per node ~600K-800K messages/sec per node Any payload size Optimized for small payload: 1KB FIFO queue Commit log - ‘topic’ Messages stored until consumer ack Messages stored according to policy Messages in a queue are consumed once, typically by a single application Messages in a topic may be consumed by multiple applications; consumers may replay messages from any point in time See also https://jack-vanlightly.com/blog/2017/12/3/rabbitmq-vs-kafka-series-introduction
- 12. 12© StreamSets, Inc. All rights reserved. Throughput - a single server can handle 100s of MB/sec Availability - data can be stored redundantly across multiple servers in a cluster No single point of failure Scalability - cluster can scale out by adding more servers Integration - client libraries for Java, Python, C/C++, Go, .Net and many more Benefits of Kafka
- 13. 13© StreamSets, Inc. All rights reserved. Impedance - decouple producers from consumers, apply back pressure Buffering - handle spiky workloads Aggregation - store record stream, replay records from a given instant When to Use Kafka
- 14. 14© StreamSets, Inc. All rights reserved. kafka.apache.org/quickstart Getting Started with Kafka
- 15. 15© StreamSets, Inc. All rights reserved. Kafka Basics Demo
- 16. 16© StreamSets, Inc. All rights reserved. Example Log Ingest Use Case UDP Many producers One record per message Asynchronous message flow Unreliable transport Single cloud service Thousands of messages/object Synchronous object creation Reliable storage
- 17. 17© StreamSets, Inc. All rights reserved. UDP to S3 Demo
- 18. 18© StreamSets, Inc. All rights reserved. Consuming records from flat files Low velocity/volume/variety of messages High throughput AND global ordering is required When NOT to Use Kafka
- 19. 19© StreamSets, Inc. All rights reserved. Kafka Is Not the Only Game in Town Apache Pulsar Second mover advantage Has queue semantics as well as streaming More flexible message retention
- 20. 20© StreamSets, Inc. All rights reserved. Conclusion Apache Kafka is the de facto stream-processing platform for enterprises Kafka can handle Internet-scale load but may be overkill for simple use cases StreamSets Data Collector is a great, open source, tool to work with Kafka
- 21. 21© StreamSets, Inc. All rights reserved. September 3-5, 2019 Tue, Sep 3 - Training & Tutorials Wed-Thu, Sep 4-5, Keynote & Breakouts Hilton Financial District (Tue|Wed|Thur)
- 22. 22© StreamSets, Inc. All rights reserved. QUESTIONS?