When we talk about spark on top of Hadoop its generally Hadoop core with Spark compute engine instead of MapReduce, i.e (HDFS, Spark, YARN) Spark follows a master-slave architecture where the master is called a Driver in spark and is responsible for ...
Sometimes in a spark application, we need to share small data across all the machines for processing. For example, if you want to filter some set of words from a large dataset residing in a datalake. Or if we simply just want to know how many blank l...
In simple terms, Apache spark is an in-memory unified parallel compute engine. In Memory,Most of the operations in apache spark happen in memory and there is very less disk IO operation giving rise to faster data transformation and computation, unlik...
We cannot use an analytical storage system for transactional requirements and vice versa. But have you ever wondered why is that so? Transactional vs Analytical storage system Transactional storage (ex - MySQL, Postgres, etc.) is used to work with da...
Data ingestion is one of the crucial steps in the data lifecycle and when the source is a relational database, Sqoop can be a very easy and simple tool for this. What is Sqoop? Sqoop is a general tool for transferring bulk data from relational databa...
Although MapReduce is not much used in solving Big Data problems nowadays because of its poor performance compared to spark. But it's still a very good approach to understanding how distributed computing works in Big Data. What is Hadoop MapReduce? H...
