Tag : MapReduce


Big Data processing using Apache Spark – Introduction

What is Spark

Apache spark is an open source general purpose distributed cluster computing framework. It is an unified computing engine for big data processing. Spark is designed for lightning fast cluster computing especially for fast computation. An application can run up to 100 times faster than Hadoop MapReduce using Spark in-memory cluster computing. Also, Spark can run up to 10 times faster than Hadoop MapReduce when running on disk.

Why Spark

We can use Spark for any kind of big data processing ranging from SQL to streaming and machine learning running from a single machine to thousands of servers. It supports widely used programming languages like Python, Java, Scala, and R by exposing a set of high level API libraries. Spark can run on clusters managed by Hadoop YARN, Apache Mesos, or it can run standalone also. It provides many features like fast computational speed, multiple language support, … More


Understanding Map join in Hive

Apache Hive is a big data query language which is used to read, transform and write large datasets in a distributed environment. It has a SQL like syntax which gets translated into a MapReduce job in order to execute on Hadoop clusters. In Hadoop ecosystem, we use Hive for batch processing to extract, transform and load the data into a data warehouse system or in a file system which can be HDFS, Amazon S3, Azure Blob or Azure DataLake. However, Hive is not meant for OLTP tasks as it has high latency. In this post, we are going to learn Map Join which can be used to improve the performance of a hive query. We will also discuss the parameters required in order to enable the Map join along with its limitations.

What is Map join in Hive

Join clause in hive is used to combine records from two tables … More