Apache Kafka

Creating Data Pipeline with Spark streaming, Kafka and Cassandra

Reading Time: 3 minutes Hi Folks!! In this blog, we are going to learn how we can integrate Spark Structured Streaming with Kafka and Cassandra to build a simple data pipeline. Spark Structured Streaming is a component of Apache Spark framework that enables scalable, high throughput, fault tolerant processing of data streams.Apache Kafka is a scalable, high performance, low latency platform that allows reading and writing streams of data Continue Reading

Serialization in Kafka

Reading Time: 2 minutes Serialization is the process of converting an object into a stream of bytes that are used for transmission. Kafka stores and transmits these bytes of arrays in its queue. Deserialization, as the name suggests, does the opposite of serialization, in which we convert bytes of arrays into the desired data type. Apache Kafka stores as well as transmit these bytes of arrays in its queue. Continue Reading

Rebalancing: What the fuss is all about?

Reading Time: 4 minutes Apache Kafka is ruling in the world of Big Data. It is just not a messaging queue but a full-fledged event streaming platform. We have looked through the basic idea of Kafka and what makes it faster than any other messaging queue. You can read about it from my previous blog. Also, we looked through Partitions, Replicas, and ISR. We are now ready for our Continue Reading

Streaming from Kafka to PostgreSQL through Spark Structured Streaming

Reading Time: 3 minutes Hello everyone, in this blog we are going to learn how to do a structured streaming in spark with kafka and postgresql in our local system. We will be doing all this using scala so without any furthur pause, lets begin. Setting up the necessities first: Dependencies Set up the required dependencies for scala, spark, kafka and postgresql. 2. PostgreSQL setup Lets start fresh by Continue Reading

Apache Kafka: What & Why?

Reading Time: 6 minutes What is Apache Kafka? Apache Kafka is a well-known name in the world of Big Data. It is one of the most used distributed streaming platforms. Kafka is just not a messaging queue but a full-fledged event streaming platform. It is a framework for storing, reading and analyzing streaming data. It is a publish-subscribe based durable messaging system exchanging data between processes, applications, and servers. 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

Using Vertica with Spark-Kafka: Writing

Reading Time: 4 minutes In previous blog of this series, we took a glance over the basic definition of Spark and Vertica. We also did a code overview for reading data from Vertica using Spark as DataFrame and saving the data into Kafka. In this blog we will be doing the reverse flow i.e. working on reading the data from Kafka as a DataFrame and writing that DataFrame into Continue Reading

Using Vertica with Spark-Kafka: Reading

Reading Time: 4 minutes We live in a world of Big data where the size of data is so big even for small results. This is the result of an increase in data collection on a rapid scale in the modern world. This massiveness of data brings the requirements of such tools which can work upon such a big chunk of data. I am pretty sure that you guys 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

Hands-on: Apache Kafka with Scala

Reading Time: 4 minutes Apache Kafka is an open sourced distributed streaming platform used for building real-time data pipelines and streaming applications. It is horizontally scalable, fault-tolerant, wicked fast, and runs in production in thousands of companies. Before the introduction of Apache Kafka, data pipleines used to be very complex and time-consuming. A separate streaming pipeline was needed for every consumer. You can guess the complexity of it with Continue Reading

Exactly-Once Semantics with Apache Kafka

Reading Time: 4 minutes Kafka’s exactly once semantics was recently introduced with the version 0.11 which enabled the message being delivered exactly once to the end consumer even if the producer retries to send the messages. This major release raised many eyebrows in the community as people believed that this is not mathematically possible in distributed systems. Jay Kreps, Co-founder on Confluent, and Co-creator of Apache Kafka explained its Continue Reading