Reading Time: 5 minutes In our current scenario, we have 4 Node cluster where one is master node (HDFS Name node and YARN resource manager) and other three are slave nodes (HDFS data node and YARN Node manager) In this cluster, we have implemented Kerberos, which makes this cluster more secure. Kerberos services are already running in the different server which would be treated as KDC server. In all Continue Reading
Reading Time: 3 minutes Nowadays Delta lake is a buzz word in the Big Data world, especially among the spark developers because it relegates lots of issues found in the Big Data domain. Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It is evolving day by day and adds cool features in its every release. Continue Reading
Reading Time: 4 minutes 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 !! What is Stateful Streaming? Continue Reading
Reading Time: 5 minutes This blog focuses on some of the problems faced while working with the Spark SQL
Reading Time: 4 minutes A Spark app that takes data from HDFS and persists in Vertica.
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: 6 minutes Dynamic Partition Pruning in Spark 3.0 With the release of Spark 3.0, big improvements were implemented to enable Spark to execute faster and there came many new features along with it. Among them, dynamic partition pruning is one. Before diving into the features which are new in Dynamic Partition Pruning let us understand what is Partition Pruning. Partition Pruning in Spark In standard database pruning Continue Reading
Reading Time: 3 minutes Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. In this blog, we are going to see how we can collect logs from Azure to ALA .Before going further we need to look how to setup spark cluster in azure Create a Spark cluster in Databricks In the Azure portal, go to the Databricks workspace that you created, Continue Reading
Reading Time: 3 minutes In today’s Big Data world, we process large amounts of data continuously and store the resulting data into data lake. This keeps changing the state of the data lake. But, sometimes we would like to access a historical version of our data. This requires versioning of data. Such kinds of data management simplifies our data pipeline by making it easy for professionals or organizations to Continue Reading
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
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
Reading Time: 5 minutes In the world of cloud computing, big data and IoT, system and application requirements have changed by leaps and bounds in recent years. Even the challenges being faced by developers and enterprises today are way different from the ones that they faced, say, a decade or two earlier. Find out why should modern enterprises opt for reactive systems today?
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
