Big Data Hadoop & Spark Developer/Architect
About this Course: If an act of something gets bigger and bigger, wider and wider with the number of people …
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About this Course:
If an act of something gets bigger and bigger, wider and wider with the number of people who use products or services, then the need for supported languages like SQL, Python, R, Scala; and data-processing engines like Spark and Impala is strongly felt. With increased workloads, comes the need for more strong and healthy useful things with valuable supply management being increased; and services like YARN comes out and becomes visible. As Hadoop matured and adoption increased, so did the need for higher-level constructs, like descriptive information, like picture date; GPS location, management and data question / management languages. Hadoop changed and got better at Yahoo as a solution for low-cost scale-out storage joined or connected with parallelizable tasks. HCatalog, Pig, and Hive became parts of the community. The result was HDFS and MapReduce.
Why should I go for Spark Online Training and Big Data Classroom Training by Bay Area:
It is a solid basic structure on which bigger things can be built for doing or completing general data like information-giving numbers on distributed group of computers like Hadoop. It provides in memory computations for increase speed and data process over MapReduce. It runs on top of existing Hadoop group and accesses Hadoop Data Store (HDFS); and can also processes structured data in Hive and Streaming data from HDFS, Flume, Kafka, and Twitter.
Spark Online Training | Big Data Classroom training in Bay area is important?
The Spark Online Training wave has come and gone and was just a hype cycle. Hadoop and related big-data technology and services will be a $50 billion industry.
Job Opportunities for the Developers
India needs a minimum of one lakh data scientists during the next couple of years. USA alone would face a shortage of one lakh ninety two thousand data scientists as against its needs of four lakh ninety thousand data scientists by 2018.
Eligibility for Spark Online Training
Data analysts / Data scientists who wish to learn and exploit their abilities to perform well and efficiently on Big Data.
Schedule: 5 Weeks Program
Every Sunday: 10:30 AM – 4:30 PM PST
Module 1 – Big Data Overview
- Lab 1 Cloudera Install
- Cloudera VM Walkthrough
Module 2 – Basic of Unix & Java
- Lab 2a Basic Unix Command
- Lab 2b Basic Java for Hadoop
Module 3 – HDFS Deep Dive
- Lab 3 – Interacting with HDFS
Module 4 – MapReduce in Action
- Lab 4a – Running First MapReduce Job
- Lab 4b – Library Indexing MapReduce Job
Module 5 Hive
- Lab 5a Practice HiveQL
- Lab 5b Working with Hive
Module 6 – Pig
- Lab 6 Practice Pig Latin
Module 7 – YARN ( MapReduce v2)
- Lab 7 – YARN Cloudera VM Walkthrough
Module 8 – Scoop
- Lab 8 – Scoop RDMS Data to Hadoop
Module 9 – Flume
- Lab 9 – Loading Logs Data to Hadoop
Module 10 – Hbase
- Lab 10 – Hbase Lab
Module 11 – Oozie
- Lab 11– Oozie Lab
Module 12 – Zookeeper
Module 13 – Apache Spark
- Lab 13– Spark Lab
Module 14 – Best Practices
Module 15 – Project 1
Module 15 – Project 2
Module 16 – Interview Prep