Introduction to Big Data and Data Science
In Today’s context information is the most valuable asset and huge volumes of information is rapidly getting generated from sources like Facebook, Twitter and Instagram etc. The information is in the form of structured and unstructured data. Big data is term used to define this large volumes of data that traditional data processing software cannot store or analyse. Analytics of big data provides deeper insights of customer’s feedback and helps provide improved value to customers.
This course is for multiple stakeholders – Business analysts and data analysts, database administrators who want to enhance their BI and data analytics skills, IT professionals who want to acquire big data analytics skills and those looking for data science as their career. The courses are as below
Big Data Hadoop Developer
Hadoop is not big data. It is an open source Java based framework for storing and processing large data sets. It’s a clustered system, and data storage is done using the Hadoop distributed file system. Using this Hadoop helps store data larger in size and number that you cannot store on the traditional node or server. Second component of Hadoop is called MapReduce, that helps processing data in parallel on multiple node in a unique way and brings the answer set (called reduce) back.
Hadoop is not a plug and play architecture and there is heavy programming required, hence its necessary we bring you up to speed on the required Java programming skills before you start as Hadoop developer. To become a Hadoop developer you need to learn how to use the Hadoop file system and the processing like MapReduce/Spark.
It is possible to become a Hadoop developer in 3-4 months. More and more companies are going for implementation of Hadoop and it is worth investing time to become a Hadoop Developer and will be growing for next 5 years.
Other Components of Hadoop – Apache Spark, Scala , Pig, Hive, Hbase, ZooKeeper, Oozie, Fume and Sqoop
Similar to MapReduce , there are other processing tools available on Hadoop like Spark which is a very fast processing method because of in memory data processing. Data is analysed in memory rather than doing on the disk. Hadoop has many other projects associated with it like Pig, Hive , Drill etc that add additional functions to Hadoop in the form of queries.
All tools can be run on the yet another resource negotiator called Yarn.
Other Key components of Hadoop are Hbase (a NoSQL database), ZooKeeper and Ambari (to manage Clusters and coordination), Oozie (workflow scheduler for Job Scheduling), Flume and Sqoop (for Data Ingesting Services into HDFS) where Flume is for unstructured data and Sqoop is for structured data, Solr and Lucene (Searching and indexing).
Apache Cassandra and Kafka and Storm
Kafka and Storm are for streaming data, used for bringing data into the Hadoop distributed file system.
Big Data Hadoop Administrator
Big Data Using Cloudera
Big Data on Azure
Big Data on AWS
Data Science with R
Big Data Analytics with Tableau
- AWS Big Data Certification
Microsoft- Data Management and Analytics