Top Big Data Technologies to learn in 2018

Ads
 

Top Big Data Technologies to learn in 2018

In 2018 there will be high demand of skilled Big Data professionals around the world. In this post we are discussing Top Big Data Technologies to learn in 2018.

In 2018 there will be high demand of skilled Big Data professionals around the world. In this post we are discussing Top Big Data Technologies to learn in 2018.

List of Top Big Data Technologies to learn in 2018 and beyond

Big Data technologies helped organizations in analyzing vast data set with the help of commodity computers running in parallel. Hadoop Big Data platform is most used system for handling and analyzing vast data sets. There are open source and commercial distribution of Hadoop Ecosystem to make things much easier for professionals.

Availability of Big Data system on the cloud further simplified and fueled the growth of Big Data. Amazon, Google and Microsoft provide easy to use cloud based Big Data system which can be provisioned quickly. Cloud based Big Data system comes with auto provisioning, scaling and monitoring modules which helps architects to quickly deploy Big Data solutions for businesses.

For the year 2018, Hadoop Based Big Data system including HDFS, Spark, Scala, R etc.. are important to learn. Here is the list of Big Data Technologies to learn in 2018 and beyond.

1. Apache Hadoop

Apache Hadoop is distributed file system and distributed processing framework for Big Data environment. Hadoop is most popular Big Data platform which used worldwide for handling pet bytes of data in business environment. Apache Hadoop is highly scalable and its power can be increase by installing new hardware and configuring it as a new node into the system. Data node is designed to both data storage and processing. Job submitted to Hadoop cluster is distributed among the nodes in the cluster and final output is collected from these processing nodes. Hadoop is using Hadoop Distributed File System (HDFS) to store data on the distributed nodes. It maintains 3 replicas (by default) of data on the distributed data nodes which makes it fault tolerant system.

Top Big Data Technologies to learn in 2018

2. HDFS

Hadoop Distributed File System or HDFS for short is the Java based distributed file system in Hadoop Environment used to store data in fault tolerant way. Developer should learn to manage Hadoop environment and perform administration activities for optimal performance. In Hadoop Ecosystem HDFS and YARN makes the data layer of the system. YARN is responsible for resource management and HDFS is responsible for storing the data in system.

3. NoSQL

NoSQL database is non-relational database and its designed to store non-structured data in it. In Big Data environment we have store both structured and non-structured data which may vary in content types. In such cases NoSQL databases are very useful. There are many NoSQL database and developer should learn few of them. Here are the lists of popular NoSQL databases:

  • MongoDB
  • Apache's CouchDB
  • HBase
  • Oracle NoSQL
  • Apache?s Cassandra DB

4. Hive

Hive is also very important technology to learn in Big Data industry. Hive is software project from Apache which runs on the Hadoop cluster and uses HDFS for storing the data. It is data ware house solution for Big Data to store data and access data using SQL-Like Query. It provides console, JDBC API, Thrift access to save, update and search data stored in Hive tables. You can use SQL-like queries to perform CRUD operations. Hive is internally uses HDFS for storing the data and appropriate for application where there is less requirement of updates. It should not be used in transaction processing applications.

5. Sqoop

Apache Sqoop is a tool for importing and exporting data between Hadoop HDFS and relational databases. Sqoop can be used for bulk transfer of data between Hadoop and relational databases. Sqoop script can also be configured for incremental transfer of data. Its another most used tool for transferring data in Big Data environment and external RDBMS. Developer should learn the Sqoop scripts and practice with large amount of data.

6. Apache Spark

Apache Spark is a framework for parallel, in-memory processing of data over the distributed clusters. Apache Spark engine can be installed on the commodity servers and use all these together for Machine Learning, Deep Learning, parallel processing tasks to achieve fast processing of data in Big Data environment. Apache Spark is also excellent machine learning library which can be used for various machine learning and deep learning tasks. It also provides support for distributed execution of R based program over Spark cluster. It is used for real-time and batch processing activities. The structured streaming is now production ready in Spark 2.2.0 which can be used to developed end-to-end stream processing solutions.

7. Scala

Scala is object oriented and functional programming language which is compiled into Java bytecode. Scala program can use all the existing Java libraries and code developed in Scala is finally compiled to Java bytecode, which runs on the JVM. Scala is used to quickly developed machine various applications quickly which runs on the Spark Cluster. Its easy to learn Scala and less coding is done for any task as compared to Java.

8. Java

Big Data application developer must learn Java programming language as there are many low level tasks which can't be done in Pig script. In such cases Java programming is useful, apart from this Java is required to develop GUI and many other application for Big Data. So, there is huge demand of Big Data developers with the Java and Enterprise Java application experience.

9. Python

Python is another demanded programming language for developing machine learning, deep learning and artificial intelligence applications. There are many machine learning APIs available in Python which can be used in Big Data analysis. Google TensorFlow application is also developed in the Python programming languages.

10. Kafka

Apache Kafka is software which is used as messaging and stream processing system in Big Data environment. Kafka cluster is used for ingesting, storing and processing of High volume of data in IoT environment. It can handle millions of events per second in properly configured system. Apache Spark can be used to process the streaming data from Kafka streams and used to develop high performance end-to-end streaming applications.

Developers should learn to install, configure and use Kafka for processing messages from Kafka cluster. Spark, Flume or custom consumers can be used to process Kafka data.

In this section we explained you the top 10 Big Data technologies that you must learn in 2018 and beyond.

Check more at Big Data tutorials, technologies, questions and answers.

Ads

Ads