Streaming

Back to Basics: Spark Streaming – DStreams

Reading Time: 4 minutes Despite Apache spark streaming being there for a longer time, I never got a chance to explore the fundamental abstraction of the Spark streaming. I’ve written few Streaming applications before but I always started with the “Structured streaming” API.  I recently started exploring DStreams which has been the core abstraction of Apache Spark Streaming API from the beginning. As part of this blog, we will Continue Reading

Tale of Apache Spark

Reading Time: 6 minutes Data is being produced extensively in today’s world and it is going to be generated more rapidly in future. 90% of total data that is produced in the world is produced in last two years only and it is estimated that in 2020 world’s total data would reach 45 ZB and data generated each day would be enough that if we try to store it Continue Reading

Streaming data from Cassandra using Alpakka

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

Defining your workflow: Why Not Airflow?

Reading Time: 4 minutes What is Apache Airflow? Airflow is a platform to programmatically author, schedule & monitor workflows or data pipelines. These functions achieved with Directed Acyclic Graphs (DAG) of the tasks. It is an open-source and still in the incubator stage. It was initialized in 2014 under the umbrella of Airbnb since then it got an excellent reputation with approximately 800 contributors on GitHub and 13000 stars. Continue Reading

Reactive Java: Understanding Reactive streams

Reading Time: 3 minutes With a lot of buzz in the programming world about “reactive Programming”, a new concept following the same path has been introduced. This is “Reactive streams” backed up by the idea of backpressure. In this blog, we try to understand, what does it mean exactly? What are Reactive Streams? We are here talking about handling streams of data that needs to be handled in an Continue Reading

Reactive Spring: Define a REST endpoint as a continuous stream

Reading Time: 2 minutes In the REST APIs, all Http requests are stateless. We fire the request and get the response, That’s it. It does not keep any state for any HTTP request. The connection between client and server is lost once the transaction ends, so 1 response for 1 request. But sometimes, we get the requirement to have a continuous response for a single request. This continuous response Continue Reading

Using Vertica with Spark-Kafka: Write using Structured Streaming

Reading Time: 3 minutes In two previous blogs, we explored about Vertica and how it can be connected to Apache Spark. The first blog in this mini series was about reading data from Vertica using Spark and saving that data into Kafka. The next blog explained the reverse flow i.e. reading data from Kafka and writing data to Vertica but in a batch mode. i.e reading data from Kafka Continue Reading

Flinkathon: Guide to setting up a Local Flink Custer

Reading Time: 3 minutes In our previous blog post, Flinkathon: First Step towards Flink’s DataStream API, we created our first streaming application using Apache Flink. It was easy, clean, and concise. However, the real power of Apache Flink is seen on a cluster, where data is processed in a distributed manner, with the advantage of multi-core/multi-memory systems. So, in this blog post, we will see how to set up Continue Reading

Determine Kafka broker health using Kafka stream application’s JMX metrics and setup Grafana alert

Reading Time: 3 minutes As we all know, Kafka exposes the JMX metrics whether it is Kafka broker, connectors or Kafka applications. A few days ago, I got the scenario where I needed to determine Kafka broker health with the help of Kafka stream application’s JMX metrics. It looks bit awkward, right? I should use the broker’s JMX metrics to do this, why am I looking to application JMX Continue Reading

Knolx: Alpakka-Connecting Kafka & ElasticSearch to Akka Streams

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

Flinkathon: First Step towards Flink’s DataStream API

Reading Time: 3 minutes In our previous blog posts: Flinkathon: Why Flink is better for Stateful Streaming applications? Flinkathon: What makes Flink better than Kafka Streams? We saw why Apache Flink is a better choice for streaming applications. In this blog post, we will explore how easy it is to express a streaming application using Apache Flink’s DataStream API. DataStream API DataStream API is used to develop regular programs Continue Reading

Flinkathon: What makes Flink better than Kafka Streams?

Reading Time: 2 minutes Initially, I would like you all to focus on a few questions before comparing the frameworks:1. Is there any comparison or similarity between Flink and the Kafka?2. What could be better in Flink over the Kafka?3. Is it the problem or system requirement to use one over the other? Before talking about the Flink betterment and use cases over the Kafka, let’s first understand their Continue Reading

Kafka: Consumer – Push vs Pull approach

Reading Time: 2 minutes Have you ever thought about the Push vs Pull approach for the system, which one suits or solves which problem? Another Question why did Kafka choose Pull over Push design for Consumers? Before talking about the Kafka approach, whether the Broker should push the data to consumer or consumer should pull from Kafka? Let’s first understand both of the approaches, as each one has its Continue Reading

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