What is bigdata? difference between Hadoop and Spark, Future of bigdata common question. I think this post will give a clear blueprint to understand bigdata.
Before understanding bigdata, firest important points you must aware of it. Last 2 decades, most of the software companies facing two problems, such as storage problems and processing problems. Let eg: Oracle, or Java or MySQL process a small amount of data like college, office data. To process Indian population data, election data, such traditional technologies takes a lot of time to process. This problem called processing problems. Similarly to store a small amount of data, one server it’s not possible to store Terabytes or Petabyte data in Single Server, this problem called the Storage problem.
To represent these problems such as storage and processing problems, a buzz word came into a picture called bigdata. Means bigdata is a recognized buzzword not any programming language or database technology.
Let eg: Agriculture, what is this its a recognization name related to agriculture/cultivation field. In this Agriculture different crops available like mango, Tomato, Apple, Rice, based on the scenario you are using different crops. Here Agriculture is not any crop name, it’s just a recognization buzz word to recognize all crops. Similarly,
Big Data is a buzzword to recognize it’s related to bigdata problem, that’s it. To solve these problems, bigdata using different technologies such as Hadoop, Spark, Kafka, Hive, Sqoop, Pig, Flink, and other big data technologies. In the above vegetables example, based on your situation ur using apple or rice, similarly based on the business use case you are using Hadoop, Spark, Flink, and hive.
Hadoop: To store data without data loss, but processing too slow. It processes only batch data (historical data)
Spark: To process batch data, Streaming, Machine learning, Graphical data at a time use Spark. Its too fast compare with Hadoop. nowadays most of the members taking spark training and most of the companies also looking for spark developers.
Sqoop: To import data from RDBMS/NoSql databases using Sqoop. It supports export as well, means store data in oracle.
Hive: By default Hadoop programming friendly, to run SQL queries on top of Hadoop, use Hive. Hive and Sqoop most frequently used in data warehouse projects.
Flink: Its competitor to Spark, but most frequently used to process Live data, support batch data as well. It’s highly recommended to implement IOT applications.
Ignite: It’s a next-generation platform to solve all problems. Unified storage and processing, cloud and other technologies. Especially to implement AI and IOT applications Ignite highly recommended.
In future Spark 3.0 , Flink and Ignite create wonders in BigData.
I think now U got a blueprint about bigdata. If you have any doubts please comment below.