Streaming Solutions

Apache Spark

Deep Dive into Apache Spark Transformations and Action

Reading Time: 4 minutes In our previous blog of Apache Spark, we discussed a little about what Transformations & Actions are? Now we will get deeper into the topic and will understand what actually they are & how they play a vital role to work with Apache Spark? What is Spark RDD? Spark introduces the concept of an RDD (Resilient Distributed Dataset), an immutable fault-tolerant, distributed collection of objects Continue Reading

Diving deeper into Delta Lake

Reading Time: 6 minutes Delta Lake is an open-source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing.

Delta Lake To the Rescue

Reading Time: 4 minutes Welcome Back. In our previous blogs, we tried to get some insights about Spark RDDs and also tried to explore some new things in Spark 2.4. You can go through those blogs here: RDDs – The backbone of Apache Spark Spark 2.4: Adding a little more Spark to your code In this blog, we will be discussing something called a Delta Lake. But first, let’s Continue Reading

Spark – Actions and Transformations

Reading Time: 4 minutes Hey guys, welcome to series of spark blogs, this blog being the first blog in this series we would try to keep things as crisp as possible, so let’s get started. So I recently get to start learning spark about believe me and now it has made me inquisitive about it, for a brief introduction of spark, I would say that it is a pretty 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

Big Data Evolution: Migrating on-premise database to Hadoop

Reading Time: 4 minutes We are now generating massive volumes of data at an accelerated rate. To meet business needs, address changing market dynamics as well as improve decision-making, sophisticated analysis of this data from disparate sources is required. The challenge is how to capture, store and model these massive pools of data effectively in relational databases. Big data is not a fad. We are just at the beginning 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

Monitoring Kafka with Prometheus and Grafana

Reading Time: 3 minutes Kafka monitoring is an operation which is used for the optimization of the Kafka deployment. This process is easy and efficient, by applying one of the existing monitoring solutions instead of building your own. Let’s say, we use Apache Kafka for message transfer and processing and we want to monitor it.But, before learning the steps for monitoring, let’s first understand the prerequisites. Kafka It is 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

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