Insights and Perspectives to keep you updated
Reading Time: 5 minutes Distributed stream processing engines have been on the rise in the last few years, first Hadoop became popular as a batch processing engine, then focus shifted towards stream processing engines. Stream processing engines can make the job of processing data that comes in via a stream easier than ever before and by using clustering can enable processing data in larger sets in a timely manner. Continue Reading
Reading Time: 4 minutes In my previous blogs I discussed about akka-streams , materialization and graphs in akka-stream . It was so easy to create sources, sinks and flows and connect them using methods and create runnable graphs. Now consider a situation where I’m getting data from Kafka and want to push it in Cassandra or Elasticsearch ? How to create such source or flow or sink that can Continue Reading
Reading Time: 3 minutes In this big data world, we have lots of data, and generally, that data comes in CSV files. So as the data is huge, so the CSV files will also be huge. It will not be feasible to load that CSV file into memory in one go. So here the challenge comes, that how should we read those big CSVs. I got the same use Continue Reading
Reading Time: 5 minutes Lagom is an opensource framework for building reactive applications using Java or Scala and it is built on Akka and Play, which are well-known technologies performing in production in some of the most performance-centric and scalable application systems. The Lagom has been continuously proving itself as a user-friendly and convenient framework to design and develop scalable microservices. However, the microservices can either based on orchestration, Continue Reading
Reading Time: 3 minutes Akka gRPC provides support for building streaming gRPC servers and clients on top of Akka Streams and Akka Http. Features of Akka-gRPC A generator, that starts from a protobuf service definitions, for: Model classes The service API as a Scala trait using Akka Stream Sources On the server side code to create an Akka HTTP route based on your implementation of the service On the client side, a client for the Continue Reading
Reading Time: 4 minutes In my previous blogs, I discussed about the basics of akka-streams and materialization. Now let’s dig deeper into Graphs in Akka-Streams. Graphs Till now we know how to create a linear pipeline/linear graph. But in real life scenario we generally don’t have linear graphs to implement. The graphs can be complex. In Akka Streams computation graphs are written in a more graph-resembling DSL. It aims Continue Reading
Reading Time: 4 minutes In my previous blog, I discussed about the basics of akka-stream. Basically, it was an introduction to akka-stream to get you started. There I mentioned about materialization which is a really important concept in Akka-Streams. And in this blog I’m going to discuss about it and some related topics in detail. So, let’s begin ! Akka-Streams Basics Let’s revisit the basics first. So, the data Continue Reading
Reading Time: 4 minutes As the world is growing, so is the data. And analysis of this data has become important. But how will you do it? How will you work with the data whose size is unknown to you? A solution to this scenario is Akka-Streams and here I’m going to discuss it. In this blog you will get to know the basics of Akka-Streams just to get Continue Reading
Reading Time: 7 minutes Alpakka project is an open-source initiative to implement stream aware and reactive pipelines using Java and Scala which is built on top of Akka streams and specially designed to provide a DSL for reactive and stream-oriented programming with built-in support for backpressure to avoid the flood of data. As a reference, Akka streams supports reactive streams and JDK 9+ compliant implementation and therefore fully interoperable Continue Reading
Reading Time: < 1 minute Hi all, Knoldus has organized a 30 min session on 1st March 2019 at 3:30 PM. The topic was Alpakka – Connecting Kafka and ElasticSearch to Akka Streams. Many people have joined and enjoyed the session. I am going to share the slides here. Please let me know if you have any question related to linked slides or video. The slides of the KnolX are here: And Continue Reading
Reading Time: 5 minutes As you all might have known by now that one of the hot topics for quite some time has been streaming of big data. Day after day, we see tons of streaming technologies out there competing with one another. The obvious reason for that, processing big volumes of data is not enough. We need real-time processing of data, especially when we need to handle continuously increasing Continue Reading
Reading Time: 2 minutes In this blog, we will discuss what are “map” and “mapAsync” when used in the Akka stream and how to use them. The difference is highlighted in their signatures:- Flow.map takes in a function that returns a type T, while Flow.mapAsync takes in a function that returns a type Future[T]. Let’s take one practical example to understand both:- Problem – Suppose we have a user with a userId and Continue Reading
Reading Time: 3 minutes In the modern digital world, a system which cannot talk to other systems or does not allow other systems to talk to itself is bound to fail. A lot of organizations that we work with be it large banks, healthcare institutions, travel booking and predictions, industrial analysers, would like to expose their ecosystem through an API so that it is available as a platform for other companies and products to consume and innovate on.
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Knoldus is the world's largest pure-play Scala and Spark company. We modernize enterprise through cutting-edge digital engineering by leveraging Scala, Functional Java and Spark ecosystem. Our mission is to provide reactive and streaming fast data solutions that are message-driven, elastic, resilient, and responsive.
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