Technologies to learn for Big Data

In this section we will discuss the Technologies to learn for Big Data.

Technologies to learn for Big Data

Technologies to learn for Big Data - Top Technologies you must learn in Big Data

In this section we will discuss all the important technologies that you must learn for Big Data. This article will give you in-depth details about all the technologies that you must learn to start your career in Big Data and Hadoop field.

As a beginner you should learn and practice these topics to gain enough knowledge to work on the projects. Then you should prepare for the interviews with the help of sample questions asked during interviews. These topics will help beginners in getting enough experience to work on project from day first. There are many job opportunities for beginners in Big Data and you can apply for these after learning at least 75% of topics discussed here. You goal should be to get job in Big Data as junior developer or admin. So, let's see the topics you should master to be able to work for Big Data.

Here is the list of Technologies to learn for Big Data:

1. Linux

Linux is most import topic and it is must for everyone venturing into Big Data field. Big Data Platform such as Hadoop installs on the Linux operating systems such as Centos, RedHat and Ubuntu. So, it's mandatory to learn how to work with Linux Operating system. You can check our tutorial at The Beginners Linux Guide.

2. Java

The Java programming language is one of the most important programming languages to learn for Big Data. There are many API's for Big Data that works with Java and these APIs are used for developing applications for Big Data. You can write program for batch and  real-time data processing job. Java can be used to write Spark and MapReduce Jobs. It is also used to connect to various components such HDFS, Hive, HBase, Thrift and many others from custom Java programs. Java is one of the most important technologies for learn for Big Data and Hadoop programming. You can learn Java programming language on our website at Java Example Codes and Tutorials.

3. SQL

The SQL stands for Standard Query Language and this is used for working with the relational databases such as Oracle, MySQL and MSSQL server. Learning SQL is important because there are many companies using relational databases for their many applications and its not easy to migrate all these applications to Big Data. So, the easiest method is to ingest the data from these relational databases into Hadoop Big Data system and use it for analysis. So, Big Data professionals should have better understanding of SQL and relational databases. You can check our tutorials SQL Tutorials, SQL Tutorials for Beginner and MySQL Tutorials.

4. Hadoop

Apache Hadoop is Big Data platform which supports distributed storage and distributed computing over the nodes in the cluster. Apache Hadoop is top project at Apache Software foundation and this software is packaged by other software vendors and distributed as complete Big Data platform. You can see more about Big Data platform at What is Big Data Platform? Apache Hadoop is robust Big Data platform for storing enterprise data on distributed cluster. Companies are using Hadoop as enterprise data store and running machine learning/analytics jobs to analyze data. Companies are using machine learning and deep learning framework to analyze data to solve many business problem such as product recommendation, fraud detection, object detection, natural language processing and sentiment analysis. You can learn Apache Hadoop on our website by visiting the tutorial page at Apache Hadoop Tutorials.


The Hadoop Distributed File System (HDFS) is distributed and replicated file system in Hadoop. HDFS provides fault-tolerance and fast access to files stored on the distributed cluster. The HDFS runs on the commodity hardware and any number of nodes can be added in the cluster to meet storage requirement.

In HDFS there are two types of nodes the NameNode and DataNode. The NameNode stores all the meta-data of files stored on Hadoop cluster. If NameNode is down then files are not accessible and NameNode is single point of failure. To mitigate this issue Secondary NameNode is used which is an exact replica of NameNode and if NameNode fails it can take the responsibility of NameNode. So, in case NameNode failure Secondary NameNodes takes over the work of Primary NameNode and there is very less downtime. This switch is automatic and cluster becomes available in few minutes.

Big Data professional should learn HDFS architecture, its commands and administration. In our tutorial we will give you many tutorials and examples of HDFS.

6. Hive

Apache Hive is a system for creating data ware house solution based on the Hadoop HDFS. Hive uses HDFS to store the data on HDFS and provides SQL like interface for querying the data stored. The Hive is being used successfully for data warehouse tasks such as ETL, reporting, and data analysis.

7. Pig

Apache Pig is a tool which allows the developers to write MapReduce Jobs is Pig Latin scripting language which is translated by Pig and executed over Hadoop system as MapReduce job. You can use Java or Scala to write MapReduce job but it requires programming skills and also takes time. To, enable non-programmers to write MapReduce jobs the Pig Latin scripting was developed. The Pig Latin script is executed with the help of Apache Pig framework.

8. Spark

Apache Spark is one of the leading in-memory framework for Big Data environment that comes with API for general data processing over distributed cluster. Apache Spark is 100 times faster than MapReduce job. Apache Spark is used for real-time, batch and stream processing. There are many API's available in Spark for developing today's Machine Learning and Artificial Intelligence applications. If you want to make your career in Apache Spark then learn Java, Scala and Python. After learning these technologies you can learn Core and then Advanced Apache Spark API.

9. HBase

Apache HBase is open source, non-relational, NoSQL database for Hadoop Big Data platform. It runs over HDFS and provides big table like capabilities to the Hadoop Big data cluster. HBase provides real-time read/write access to the large data sets stored on the Hadoop Cluster.

10. Drill

Apache Drill is open source project for querying large data sets in Big Data environment. It is one of the top projects of Apache Software foundation. This project is is open source version of  Google's Dremel system. Apache Drill is powerful system which can be installed on a cluster of upto 10,000 or more severs to query peta-bytes of data in fraction of seconds. This tool is used by many visualization tools such as Tableau, Excel, Qlikview and many other custom tools. Apache Drill supports HDFS data, NoSQL, SQL, Hive, text files and many other data formats.

Apache Drill is one of most used tools in Big Data for query large and analyzing large data sets in Big Data environment. As a Big Data professional you must learn Apache Drill.

11. Apache ZooKeeper

Apache ZooKeeper is top level project at Apache Software foundation and this software is distributed hierarchical key-value store which is used by Hadoop Ecosystem components. The Apache ZooKeeper provide distributed shared, synchronized naming registry for large distributed systems such as Big Data system.

ZooKeeper works as centralized repository in the distributed cluster where various applications put their configuration/application data and get from it.

12. Oozie

Apache Oozie is another project of Hadoop ecosystem and this software provides a software system for job workflow management and execution. Apache Oozie is used to configure and schedule the Hadoop jobs (as well as workflows) over Hadoop cluster. Within workflow you can define any task such a running MapReduce Job, accessing database, running Pig jobs, using SSH and interacting with email. Apache Oozie is used extensively for development and exaction of data processing Jobs over Hadoop cluster. Ooze is well designed to handle the execution of work flow and supports job retry from the point where it failed (in case of job failure).

13. Zeppelin

Apache Zeppelin is new project from Apache which provides web based notebook for interactive data analysis. This aims to provide web-based notebook for data ingestion, data querying, visualization of data and data collaboration. The Zeppelin web based notebook works with Spark and Hive out of the box. It can be used as "Modern Data Science Studio" for developing and testing of machine learning applications.

14. Hue

The term Hue stands for Hadoop User Experience, Hue is an open source tool for Hadoop ecosystem which is used for browsing, querying and visualization data. Hue is web based editor tool for almost all the Hadoop ecosystem components such as Hive, Impala, Pig, MapReduce, Spark, HBase and other SQL servers like MySQL, Oracle, SparkSQL, Solr SQL, Phoenix etc.. You can use Hue to schedule Hue Jobs and monitors the jobs running on Hadoop cluster.

15. MongoDB

MongoDB is document oriented NoSQL database for handling large volume of data over distributed MongoDB cluster. MongoDB is high performance, highly scalable, open-source NoSQL database server for storing and retrieving JSON (document) data.

16. Flume

Apache Flume is software from Apache Foundation for Big Data environment which is used for moving the large amount of log data into Hadoop cluster. This software is used for efficient and reliable collection of large amount of log data into HDFS.

17. Cassendra

Apache Cassendra is another NoSQL database which is used for storing large amount of data on to distributed commodity severs. AApache Cassendra open source, highly scalable, wide-column NoSQL database server and it comes with no single point of failure feature. Many companies around the world is using Cassendra to handle large amount of data for their business needs. Developer should learn Apache Cassendra NoSQL database.

18. Kafka

Kafka is full-fledge stream messaging system which can handle trillions of events every day. Kafka is very fast, scalable, fault-tolerant and distributed messaging system which is used for handling many types of small size data in Big Data environment. Kafka is used for Stream Processing, Website Activity Tracking, Metrics Collection and Monitoring and Log Aggregation in Big Data environment.

19. Flink

Apache Flink is open source distributed streaming processing framework used with the Big Data for running streaming, ingestion, data processing jobs over the Hadoop cluster.

20. Storm

Apache Storm is real-time computing framework which is used for processing real-time streams of data. Apache Storm works at very high speed and processes the data at unprecedented speed.

In this section we have learned about the important big data technologies that every Big Data professionals must learn.

Check more tutorials at: