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Hadoop Training in San Francisco, Bay Area

  • Get Trained for Microsoft Big Data Certification - Learn More
  • Become a Hadoop Developer by getting project experience
  • Build a project portfolio to connect with recruiters
    - Check out Toly's Portfolio
  • Get hands-on experience with access to remote Hadoop cluster
  • Stay updated in your career with lifetime access to live classes

Upcoming Live Hadoop Training in California


24
Jun
Sat and Sun(4 weeks)
7:00 AM - 11:00 AM PST
$399

24
Jun
Sun to Thu(3 weeks)
6:30 PM - 8:30 PM PST
$399

08
Jul
Sun to Thu(3 weeks)
6:30 PM - 8:30 PM PST
$399

Want to work 1 on 1 with a mentor. Choose the project track

About Online Hadoop Training Course

Project Portfolio

Build an online project portfolio with your project code and video explaining your project. This is shared with recruiters.

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42 hrs live hands-on sessions with industry expert

The live interactive sessions will be delivered through online webinars. All sessions are recorded. All instructors are full-time industry Architects with 14+ years of experience.

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Remote Lab and Projects

You will get access to a remote Hadoop cluster for this purpose. Assignments include running MapReduce jobs/Pig & Hive queries. The final project will give you a complete understanding of the Hadoop Ecosystem.

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Lifetime Access & 24x7 Support

Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.

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Weekly 1-on-1 meetings

If you opt for the Microsoft Track, you will get 8 one-on-one meetings with an experienced Hadoop architect who will act as your mentor.

Big Data Hadoop Training in San Francisco, California

On any given day, there are at least 1500 hadoop job openings for professionals with hadoop skills, of which one third of them are in the Bay Area. One of the three IT jobs that require hadoop skills are based in San Francisco, Bay Area. There is more Hadoop related hiring in Bay Area when compares to other states in US like New York, Texas, Maryland, and Massachusetts. A broader band of big data hadoop job openings reveal that the demand for skills like MapReduce, Pig, MongoDB, R, Spark and other related technologies is up by 37%. For professionals in Bay Area or pros looking to relocate to California, 2017 is the best time to hone the Hadoop Skills through DeZyre’s Big Data and Hadoop Certification Training.

Hadoop Developer Salary in San Francisco, CA

  • Average Big Data Hadoop Developer Salary in San Francisco, CA is $139,000.
  • Average Java Hadoop Developer Salary in San Francisco, CA is $153,000.

Companies Hiring Hadoop Developers in San Francisco, Bay Area

 
  • AT & T
  • Collabera
  • Century Link
  • eBay
  • Experian
  • MapR
  • Oracle
  • SAP
  • Think Big Analytics
  • Teradata
  • Tesla Motors
  • Twitter
  • VISA
  • Workday Inc.

Hadoop Certification Cost in San Francisco, CA- $399

DeZyre's Hadoop Developer Certification Training in Bay Area, California costs around $399 featuring instructor-led online hadoop training and industry oriented hadoop projects. DeZyre provides hadoop certification to professionals on successful completion and evaluation of the hadoop project by industry experts.

Benefits of Hadoop Training online

How will this help me get jobs?

  • Display Project Experience in your interviews

    The most important interview question you will get asked is "What experience do you have?". Through the DeZyre live classes, you will build projects, that have been carefully designed in partnership with companies.

  • Connect with recruiters

    The same companies that contribute projects to DeZyre also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.

  • Stay updated in your Career

    Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.

What if I have any doubts?

For any doubt clearance, you can use:

  • Discussion Forum - Assistant faculty will respond within 24 hours
  • Phone call - Schedule a 30 minute phone call to clear your doubts
  • Skype - Schedule a face to face skype session to go over your doubts

Do you provide placements?

In the last module, DeZyre faculty will assist you with:

  • Resume writing tip to showcase skills you have learnt in the course.
  • Mock interview practice and frequently asked interview questions.
  • Career guidance regarding hiring companies and open positions.

Online Hadoop Training Course Curriculum

Module 1

Introduction to Big Data

  • Rise of Big Data
  • Compare Hadoop vs traditonal systems
  • Hadoop Master-Slave Architecture
  • Understanding HDFS Architecture
  • NameNode, DataNode, Secondary Node
  • Learn about JobTracker, TaskTracker
Module 2

HDFS and MapReduce Architecture

  • Core components of Hadoop
  • Understanding Hadoop Master-Slave Architecture
  • Learn about NameNode, DataNode, Secondary Node
  • Understanding HDFS Architecture
  • Anatomy of Read and Write data on HDFS
  • MapReduce Architecture Flow
  • JobTracker and TaskTracker
Module 3

Hadoop Configuration

  • Hadoop Modes
  • Hadoop Terminal Commands
  • Cluster Configuration
  • Web Ports
  • Hadoop Configuration Files
  • Reporting, Recovery
  • MapReduce in Action
Module 4

Understanding Hadoop MapReduce Framework

  • Overview of the MapReduce Framework
  • Use cases of MapReduce
  • MapReduce Architecture
  • Anatomy of MapReduce Program
  • Mapper/Reducer Class, Driver code
  • Understand Combiner and Partitioner
Module 5

Advance MapReduce - Part 1

  • Write your own Partitioner
  • Writing Map and Reduce in Python
  • Map side/Reduce side Join
  • Distributed Join
  • Distributed Cache
  • Counters
  • Joining Multiple datasets in MapReduce
Module 6

Advance MapReduce - Part 2

  • MapReduce internals
  • Understanding Input Format
  • Custom Input Format
  • Using Writable and Comparable
  • Understanding Output Format
  • Sequence Files
  • JUnit and MRUnit Testing Frameworks
Module 7

Apache Pig

  • PIG vs MapReduce
  • PIG Architecture & Data types
  • PIG Latin Relational Operators
  • PIG Latin Join and CoGroup
  • PIG Latin Group and Union
  • Describe, Explain, Illustrate
  • PIG Latin: File Loaders & UDF
Module 8

Apache Hive and HiveQL

  • What is Hive
  • Hive DDL - Create/Show Database
  • Hive DDL - Create/Show/Drop Tables
  • Hive DML - Load Files & Insert Data
  • Hive SQL - Select, Filter, Join, Group By
  • Hive Architecture & Components
  • Difference between Hive and RDBMS
Module 9

Advance HiveQL

  • Multi-Table Inserts
  • Joins
  • Grouping Sets, Cubes, Rollups
  • Custom Map and Reduce scripts
  • Hive SerDe
  • Hive UDF
  • Hive UDAF
Module 10

Apache Flume, Sqoop, Oozie

  • Sqoop - How Sqoop works
  • Sqoop Architecture
  • Flume - How it works
  • Flume Complex Flow - Multiplexing
  • Oozie - Simple/Complex Flow
  • Oozie Service/ Scheduler
  • Use Cases - Time and Data triggers
Module 11

NoSQL Databases

  • CAP theorem
  • RDBMS vs NoSQL
  • Key Value stores: Memcached, Riak
  • Key Value stores: Redis, Dynamo DB
  • Column Family: Cassandra, HBase
  • Graph Store: Neo4J
  • Document Store: MongoDB, CouchDB
Module 12

Apache HBase

  • When/Why to use HBase
  • HBase Architecture/Storage
  • HBase Data Model
  • HBase Families/ Column Families
  • HBase Master
  • HBase vs RDBMS
  • Access HBase Data
Module 13

Apache Zookeeper

  • What is Zookeeper
  • Zookeeper Data Model
  • ZNokde Types
  • Sequential ZNodes
  • Installing and Configuring
  • Running Zookeeper
  • Zookeeper use cases
Module 14

Hadoop 2.0, YARN, MRv2

  • Hadoop 1.0 Limitations
  • MapReduce Limitations
  • HDFS 2: Architecture
  • HDFS 2: High availability
  • HDFS 2: Federation
  • YARN Architecture
  • Classic vs YARN
  • YARN multitenancy
  • YARN Capacity Scheduler
Module 15

Project

  • Demo of 2 Sample projects.
  • Twitter Project : Which Twitter users get the most retweets? Who is influential in our industry? Using Flume & Hive analyze Twitter data.
  • Sports Statistics : Given a dataset of runs scored by players using Flume and PIG, process this data find runs scored and balls played by each player.
  • NYSE Project : Calculate total volume of each stock using Sqoop and MapReduce.
Module 1

Learn Hadoop on HDInsight (Linux)

  • What is Hadoop on HDInsight?
  • How is data stored in HDInsight?
  • Information about using HDInsight on Linux
  • Using SSH with Linux clusters from a Linux computer
  • SSH Tunneling to HDInsight Linux clusters
Module 2

Processing Big Data with Hadoop in Azure HDInsight

  • Provision an HDInsight cluster.
  • Connect to an HDInsight cluster, upload data, and run MapReduce jobs.
  • Use Hive to store and process data.
  • Process data using Pig.
  • Use custom Python user-defined functions from Hive and Pig.
  • Define and run workflows for data processing using Oozie.
  • Transfer data between HDInsight and databases using Sqoop.
Module 3

Implementing Real-Time Analytics with Hadoop in Azure HDInsight

  • Use HBase to implement low-latency NoSQL data stores.
  • Use Storm to implement real-time streaming analytics solutions.
  • Use Spark for high-performance interactive data analysis.
Module 4

Implementing Predictive Analytics with Spark in Azure HDInsight

  • Using Spark to explore data and prepare for modeling
  • Build supervised machine learning models
  • Evaluate and optimize models
  • Build recommenders and unsupervised machine learning models
Module 5

Project

  • Implement a Big Data Project under the guidance of a Hadoop Architect
  • Upload your project to DeZyre portfolio and display to recruiters

Upcoming Classes for Online Hadoop Training in San Francisco, Bay Area

June 24th

  • Duration: 4 weeks
  • Days: Sat and Sun
  • Time: 7:00 AM - 11:00 AM PST
  • 8 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

June 24th

  • Duration: 3 weeks
  • Days: Sun to Thu
  • Time: 6:30 PM - 8:30 PM PST
  • 8 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

July 8th

  • Duration: 3 weeks
  • Days: Sun to Thu
  • Time: 6:30 PM - 8:30 PM PST
  • 8 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

Online Hadoop Training Course Reviews

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Hadoop Developers in San Francisco, CA

Big Data and Hadoop Blogs

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Recap of Apache Spark News for January 2018


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Online Hadoop Training MeetUp

Using Erlang/Elixir to build Custom Web DSLs and Interactive Analytics on Data

Description: Cross post with the Erlang/Elixir Meetup here: https://www.meetup.com/ErlangElixirSF/events/251528664/ TITLE: Using Erlang/Elixir to build Custom Web DSLs and Interactive Analytics on Data DESCRIPTION: Learn about how Erlang/Elixir let AdStage implemented the next generation of their query service which allows users to express custom functions over multiple types and sources of data, using fam ...

Hosted By: San Francisco Bay Area Big Data and Scalable Systems
Event Time: 2018-06-20 20:00:00

Online Hadoop Training News

How Arcadia Data brings self-service BI to the data lake

Description:

Data has evolved over the years. Complex data structures, unstructured data, real-time processing, growing data volumes, and new varieties of data are all part of the evolution. Platforms have changed as well. “Schema-less,” real-time events, “schema-on-read,” and extract/load/discover/transform (ELDT) are now part of our vernacular.

Despite these changes, many businesses rely on the same data warehouse infrastructure that they’ve relied on for years. Many businesses have also turned to data lakes, through platforms such as Apache Hadoop, NoSQL databases, and Apache Kafka, or cloud storage technologies like Amazon S3, as a cost-effective way of managing large volumes of disparate data sets. Unfortunately, the success rates of these data lakes have been disappointing, as they have not been able to deliver quicker or better value to businesses.

To read this article in full, please click here

Date Posted: Wed, 30 May 2018 03:00:00 -0700

21 hot programming trends—and 21 going cold

Description:

Programmers love to sneer at the world of fashion where trends blow through like breezes. Skirt lengths rise and fall, pigments come and go, ties get fatter, then thinner. But in the world of technology, rigor, science, math, and precision rule over fad.

Hot: Renting
Not: Buying

To read this article in full, please click here

(Insider Story)
Date Posted: Mon, 16 Apr 2018 03:00:00 -0700

What’s new in Apache Spark? Low-latency streaming and Kubernetes

Description:

You’d be forgiven for passing by the announcement of Apache Spark 2.3. After all, it’s a point release, isn’t it? Sure, there will be some bug fixes, maybe an improvement or two to the MLLib framework, maybe an extra operator or something, but nothing all that major. That will be saved for Apache Spark 3.0, surely?

In fact, this is no mere point release. Apache Spark 2.3 ships with two major new features, one of which is perhaps the biggest (and often-requested) change to streaming operations since Spark Streaming was added to the project. The other is native integration with Kubernetes to execute Spark jobs in container clusters.

To read this article in full, please click here

Date Posted: Thu, 15 Mar 2018 03:00:00 -0700

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