Introduction to Stream Processing with Apache Flink⁠—Marta Paes Moreira

Stream processing has deeply changed the way we build data pipelines. Over the years, it outgrew its original space of real-time applications into a “grand unifying” paradigm for distributed data processing. Apache Flink is at the forefront of this development, pushing the boundaries and redefining what is possible with streams.
With flexible APIs and a powerful execution model, Flink has been hardened at (huge) scale by companies like Alibaba, Netflix, Uber and Yelp. In this talk, we’ll explore the building blocks that make it the most resilient and versatile option for stateful stream processing and beyond. In particular, we’ll review basic concepts of streaming with Flink, like state and time, and walk through the main features and abstractions that give it a competitive edge over similar frameworks.

Connect to WeAreDevelopers Live, the platform where you can see featured developers working and showcasing live. Keep yourself informed of the latest trends and issues and improve your skills and knowledge. So, if you’re in the mood for learning something new then keep scrolling to find out when the next live sessions are!

#apache

What is GEEK

Buddha Community

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics

An Introduction to Stream Processing with Apache Flink

In many application domains, massive streaming data is generated from different sources, for example, user activities on the web, measurements from the Internet of Things (IoT) devices, transactions from financial services, and location-tracking feeds. These data streams (unbounded) that traditionally used to be stored as datasets (bounded), and processed later by batch processing jobs. Although this is not an efficient way in some scenarios due to the time value of the data, where the real-time processing is desirable by businesses to enable them to get insights from data and proactively respond to changes as close as the data is being produced (in motion).

Toward that, the applications have to be updated to be more stream-based using real-time stream processors. That is where Apache Flink comes in; Flink is an open-source framework for stateful, large-scale, distributed, and fault-tolerant stream processing.

This blog post presents an overview of Apache Flink and its key features for streaming applications. It focuses on Flink’s DataStream API and explores some of the underlying architectural design concepts.

Most of the details of this post are based on my hands-on experience in Flink during my involvement in the datAcron EU research project as summarised in this paper.

Distributed Online Learning System Archircture using Apache Flink. Photo by the author | Photo from A Distributed Online Learning Approach for Pattern Prediction over Movement Event Streams.

Apache Flink is gaining more popularity and it is being used in production to build large-scale data analytics and processing components over massive streaming data, where it powers some of the world’s most demanding stream processing applications, for example, it is a crucial component of Alibaba’s search engine.

#apache-flink #event-driven-architecture #big-data #stream-processing #apache

Introduction to Stream Processing with Apache Flink⁠—Marta Paes Moreira

Stream processing has deeply changed the way we build data pipelines. Over the years, it outgrew its original space of real-time applications into a “grand unifying” paradigm for distributed data processing. Apache Flink is at the forefront of this development, pushing the boundaries and redefining what is possible with streams.
With flexible APIs and a powerful execution model, Flink has been hardened at (huge) scale by companies like Alibaba, Netflix, Uber and Yelp. In this talk, we’ll explore the building blocks that make it the most resilient and versatile option for stateful stream processing and beyond. In particular, we’ll review basic concepts of streaming with Flink, like state and time, and walk through the main features and abstractions that give it a competitive edge over similar frameworks.

Connect to WeAreDevelopers Live, the platform where you can see featured developers working and showcasing live. Keep yourself informed of the latest trends and issues and improve your skills and knowledge. So, if you’re in the mood for learning something new then keep scrolling to find out when the next live sessions are!

#apache

Spark Structured Streaming – Stateful Streaming

Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Internals of Structured Streaming“. And this blog pertains to Stateful Streaming in Spark Structured Streaming. So let’s get started.

Let’s start from the very basic understanding of what is Stateful Stream Processing. But to understand that, let’s first understand what Stateless Stream Processing is.

In my previous blogs of this series, I’ve discussed Stateless Stream Processing.

You can check them before moving ahead – Introduction to Structured Streaming and Internals of Structured Streaming

#analytics #apache spark #big data and fast data #ml #ai and data engineering #scala #spark #streaming #streaming solutions #tech blogs #stateful streaming #structured streaming

Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

Corona Virus Pandemic has brought the world to a standstill.

Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.

Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!

And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!

If you are still not aware of it, don’t worry as Georgia Byng has well said,

“No time is better than the present”

– Georgia Byng, a British children’s writer, illustrator, actress and film producer.

No matter if you are a developer (be it front-end or back-end) or a data scientist, tester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.

From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.

How to upgrade your skills?

Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.

These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.

Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).

xlr8rs make your development real fast by just adding your core business logic to the template.

If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂

Confused with which technology to start with?

To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.

Since we believe:

“There’s always a scope of improvement“

If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.

#ai #akka #akka-http #akka-streams #amazon ec2 #angular 6 #angular 9 #angular material #apache flink #apache kafka #apache spark #api testing #artificial intelligence #aws #aws services #big data and fast data #blockchain #css #daml #devops #elasticsearch #flink #functional programming #future #grpc #html #hybrid application development #ionic framework #java #java11 #kubernetes #lagom #microservices #ml # ai and data engineering #mlflow #mlops #mobile development #mongodb #non-blocking #nosql #play #play 2.4.x #play framework #python #react #reactive application #reactive architecture #reactive programming #rust #scala #scalatest #slick #software #spark #spring boot #sql #streaming #tech blogs #testing #user interface (ui) #web #web application #web designing #angular #coronavirus #daml #development #devops #elasticsearch #golang #ionic #java #kafka #knoldus #lagom #learn #machine learning #ml #pandemic #play framework #scala #skills #snowflake #spark streaming #techhub #technology #test automation #time management #upgrade