Spark Streaming: Adding Spark to Streaming

In today’s world we have a lot of data. And this data will only grow more and more in future. According to a study, in 2020, the data produced is abound 44 zettabytes each day. And by 2025, approximately 463 exabytes would be created every 24 hours worldwide. Do you ever imagine how one can store or process this much data ?A solution to this is Apache Spark and in this blog I’m going to discuss about Spark Streaming here.

What is Spark Streaming?

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It was added to Apache Spark in 2013. We can get data from many sources such as Kafka, Flume etc. and process it using functions such as map, reduce etc. After processing we can push data to filesystem, databases and even to live dashboards.

In Spark Streaming we work on near real time data. It divides the received input stream into batches. The Spark Engine processes the batches and generate final output in batches.

Spark Streaming

Spark DStream

DStream (also known as discretized stream) is an abstraction of Spark Streaming. It represents a continuous stream of data. You can create DStreams in two ways :

  • By taking an input stream from sources such as Kafka, Flume etc.
  • By applying functions on input DStream that will produce another DStream.

Internally, a DStream is a sequence of RDD and so we can also say that it is a continuous stream of RDD . RDD (Resilient Distributed Dataset) is the fundamental data structure of Apache Spark which are an immutable collection of objects which computes on the different node of the cluster. Every RDD in DStream contains data from the certain interval. Also if you will apply any operation on a DStream, it applies to all the underlying RDDs.

Spark Streaming Sources and Receivers

Spark streaming provides 2 categories of Spark Streaming Sources. You can create an input DStream using these sources.

The categories are following :

  • Basic sources: Sources directly available in the StreamingContext API. For example: file systems, and socket connections.
  • Advanced sources: Sources like Kafka, Flume, Kinesis, etc. are available through extra utility classes. These require linking by adding extra dependencies.

Also, every input stream(except file stream) has a receiver object. The work of receiver object is to receive the data from input stream and store it in Spark’s memory.

There are two kinds of receiver(based on some sources allow acknowledgement and some not):

  • **Reliable Receiver **: A reliable receiver correctly sends acknowledgment to a reliable source when the data has been received and stored in Spark with replication.
  • Unreliable Receiver: An unreliable receiver does not send acknowledgment to a source.

You can create multiple input DStreams to receive multiple streams of data in parallel. This will create multiple receivers according to the number of streams.

#scala #spark #spark streaming #spark dstream

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Spark Streaming: Adding Spark to Streaming
Teresa  Jerde

Teresa Jerde

1597452410

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

Spark Streaming: Adding Spark to Streaming

In today’s world we have a lot of data. And this data will only grow more and more in future. According to a study, in 2020, the data produced is abound 44 zettabytes each day. And by 2025, approximately 463 exabytes would be created every 24 hours worldwide. Do you ever imagine how one can store or process this much data ?A solution to this is Apache Spark and in this blog I’m going to discuss about Spark Streaming here.

What is Spark Streaming?

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It was added to Apache Spark in 2013. We can get data from many sources such as Kafka, Flume etc. and process it using functions such as map, reduce etc. After processing we can push data to filesystem, databases and even to live dashboards.

In Spark Streaming we work on near real time data. It divides the received input stream into batches. The Spark Engine processes the batches and generate final output in batches.

Spark Streaming

Spark DStream

DStream (also known as discretized stream) is an abstraction of Spark Streaming. It represents a continuous stream of data. You can create DStreams in two ways :

  • By taking an input stream from sources such as Kafka, Flume etc.
  • By applying functions on input DStream that will produce another DStream.

Internally, a DStream is a sequence of RDD and so we can also say that it is a continuous stream of RDD . RDD (Resilient Distributed Dataset) is the fundamental data structure of Apache Spark which are an immutable collection of objects which computes on the different node of the cluster. Every RDD in DStream contains data from the certain interval. Also if you will apply any operation on a DStream, it applies to all the underlying RDDs.

Spark Streaming Sources and Receivers

Spark streaming provides 2 categories of Spark Streaming Sources. You can create an input DStream using these sources.

The categories are following :

  • Basic sources: Sources directly available in the StreamingContext API. For example: file systems, and socket connections.
  • Advanced sources: Sources like Kafka, Flume, Kinesis, etc. are available through extra utility classes. These require linking by adding extra dependencies.

Also, every input stream(except file stream) has a receiver object. The work of receiver object is to receive the data from input stream and store it in Spark’s memory.

There are two kinds of receiver(based on some sources allow acknowledgement and some not):

  • **Reliable Receiver **: A reliable receiver correctly sends acknowledgment to a reliable source when the data has been received and stored in Spark with replication.
  • Unreliable Receiver: An unreliable receiver does not send acknowledgment to a source.

You can create multiple input DStreams to receive multiple streams of data in parallel. This will create multiple receivers according to the number of streams.

#scala #spark #spark streaming #spark dstream

Roberta  Ward

Roberta Ward

1595344320

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 scientisttester, 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

Top Spark Development Companies | Best Spark Developers - TopDevelopers.co

An extensively researched list of top Apache spark developers with ratings & reviews to help find the best spark development Companies around the world.

Our thorough research on the ace qualities of the best Big Data Spark consulting and development service providers bring this list of companies. To predict and analyze businesses and in the scenarios where prompt and fast data processing is required, Spark application will greatly be effective for various industry-specific management needs. The companies listed here have been skillfully boosting businesses through effective Spark consulting and customized Big Data solutions.

Check out this list of Best Spark Development Companies with Best Spark Developers.

#spark development service providers #top spark development companies #best big data spark development #spark consulting #spark developers #spark application

Art  Lind

Art Lind

1601496000

Stateful Streaming in Spark

Apache Spark is a fast and general-purpose cluster computing system. In Spark, we can do the batch processing and stream processing as well. It does near real-time processing. It means that it processes the data in micro-batches. I have discussed more Spark Streaming in my previous blog. Now in this blog, I’ll discuss Stateful Streaming in Spark. So let’s start !!

#big data #scala #spark #spark streaming #streaming api