Data Engineer Training Online 2017-08-07T02:26:30+00:00

Data Engineering Training Course

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Data Engineer Training 
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Data Engineer TrainingAbout: The Data Engineer training program is designed to teach Data engineers how to build and operate frameworks to handle the exploding amount of data being collected in today’s top firms.

Data Engineer training The curriculum is structured based on emerging trends from industry-leading companies to make the candidates relevant and marketable in the evolving world of data engineering.

The potential Data Engineer candidate will be honed and equipped to become the all-purpose guy of big data analytics operation, working between downstream analysis on the one hand, and upstream data scientists on the other.

Deliverable: Market-primed Data Engineer Skills-set

At the end of this course, the candidate will be able to launch data analytics applications for companies.

He/she will be instrumental in getting data from a variety of different sources. These data will be parsed in the right formats, assuring that they adhere to data quality standards, and assuring that downstream users can get that data quickly so they can perform whatever downstream tasks they have such as reporting or exploratory analytics with the end-view of a recommended algorithm.

TRAINING METHODOLOGY

In Class: $2,499
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 14th Aug 2017

Online: $2,499
Next Session: On Demand

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DATA ENGINEER TRAINING COURSE 
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Instructor: John Doe, Lamar George

DESCRIPTION

Dat engineer training | Data Engineer Tutorials

Dat engineer training | Data Engineer Tutorials

Data Engineer Training Course Proper

TheData Engineer Training  training modules and live in-class teaching will equip the data engineer candidate to have intuitive knowledge of databases and market-tested engineering practices.

These include monitoring the system, handling, and logging errors, building human-fault-tolerant pipelines, understanding what is necessary to scale up, addressing continuous integration, knowledge of database administration, and maintaining data cleaning. Platforms and tools to be used include Hadoop, Apache Hive, Apache Pig, Apache Spark and NoSQL.

Data Engineer Training Is this right fit for me?

Data Engineer Training Candidates typically come from an extensive background in math, statistics, and engineering. They have completed their Ph.D. in a related field or are currently doing Post-Doctoral research. The rigorous curriculum is designed for fellows that already have a significant experience with large data sets, CS programming, and big picture problem-solving.

This comprehensively intense program will let you solve critical company problems such as predictive data sets and related algorithms.

Candidates may not necessarily hold a Ph.D. for as long as they have several years of software engineering experience, the drive to learn quickly and are passionate about Big Data and software engineering. learning problems using Google’s Tensor Flow library.

It will not only help you discover what Tensor Flow is and how to use it but will also show you the unbelievable things that can be done in machine learning with the help of examples/real-world use cases.

We start off with the basic installation of Tensor flow, moving on to covering the unique features of the library such as Data Flow Graphs, training, and visualization of performance with Tensor Board—all within an example-rich context using problems from the multiple sources.

The focus is on introducing new concepts through problems that are coded and solved over the course of each section.

CURRICULUM

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WEEK 1- MASTERING HADOOP

Lecture1.1 Introduction to Big Data
Lecture1.2 The Big Data Pipeline
Lecture1.3 Core Elements of Apache Hadoop
Lecture1.4 The Apache Hadoop Ecosystem
Lecture1.5 Solving Big Data Problems with Apache Hadoop
Lecture1.6 Use Cases
WEEK 2- DEVELOPING HADOOP APPLICATIONS

Lecture2.1 Introduction to Developing Hadoop Application
Lecture2.2 Job Execution Framework MapReduce v1 & v2
Lecture2.3 Write a MapReduce Program
Lecture2.4 Use the MapReduce API
Lecture2.5 Managing, monitoring, and testing MapReduce jobs
Lecture2.6 Characterizing and improving MapReduce job performance
Lecture2.7 Working with different data sources in MapReduce
Lecture2.8 Managing multiple MapReduce jobs
Lecture2.9 Using MapReduce streaming
WEEK 3- HBASE DATA MODEL AND ARCHITECTURE

Lecture3.1 Introduction to HBase
Lecture3.2 HBase Data Model
Lecture3.3 HBase Architecture
Lecture3.4 HBase Schema Design
Lecture3.5 Basic Schema Design
Lecture3.6 Design Schemas for Complex Data Structures
Lecture3.7 Use Hive to Query HBase
WEEK 4- APACHE HIVE

Lecture4.1 Hive in the Hadoop Ecosystem
Lecture4.2 Use cases of Hive
Lecture4.3 Steps in the data pipeline
Lecture4.4 Create and Load Data
Lecture4.5 Create databases, internal tables, external tables, and partitioned tables
Lecture4.6 Learn about data types and casting in Hive
Lecture4.7 Load data into tables and databases
Lecture4.8 Query and Manipulate Data
Lecture4.9 Query, sort, and filter data
Lecture4.10 Manipulate data with user-defined functions
WEEK 5- APACHE PIG

Lecture5.1 Pig in the Hadoop Ecosystem
Lecture5.2 Use cases of Pig
Lecture5.3 Steps in the data pipeline
Lecture5.4 Extract, Transform, and Load Data
Lecture5.5 Load data into relations
Lecture5.6 Debug Pig scripts
Lecture5.7 Perform simple manipulations
Lecture5.8 Save relations as files
Lecture5.9 Manipulate Data
Lecture5.10 Subset relations
Lecture5.11 Combine relations
Lecture5.12 Use UDFs on relations
WEEK 6- APACHE SPARK

Lecture6.1 Introduction to Apache Spark
Lecture6.2 Load and Inspect Data in Apache Spark
Lecture6.3 Build a Simple Apache Spark Application
Lecture6.4 Work with PairRDD
Lecture6.5 Work with DataFrames
Lecture6.6 Monitor Apache Spark Applications
Lecture6.7 Apache Spark Data Pipelines
Lecture6.8 Create an Apache Spark Streaming Application
Lecture6.9 Use Apache Spark GraphX
Lecture6.10 Use Apache Spark MLlib

 

WEEK 7- NOSQL DATABASES
Data Engineer training
Data Engineer Training
Lecture7.1 Introducing NoSQL
Lecture7.2 Hadoop & NoSQL
Lecture7.3 MongoDB Introduction
Lecture7.4 Introduction to Cassandra
Lecture7.5 Cloud NoSQL Databases
Lecture7.6 Use Cases

Online: $2,499
Next Batch: starts from – On demand basis

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In Class: $4,999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 14th Aug 2017

Course Duration: Fully Immersive 8 Weeks 8 AM to 4 PM EST

INSTRUCTORS

COURSE HIGHLIGHTS

Course Duration : 8 WEEKS
Location: NYC|D.C|Bay Area|Toronto|Online
Certificate: Yes
Assessments: Daily
Prerequisites: Basic Python programming

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FAQ'S

What do I need to know before taking this Course?

A basic understanding of Python and modeling.
Familiarity with matrices and linear algebra.

Does Tensor Flow work with Python 3?

As of the 0.6.0 release timeframe (Early December 2015), it does support Python 3.3+.

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Summary
Training | Workshops | Paid Consulting | Bootcamps
User Rating
5 based on 1 votes
Service Type
Training | Workshops | Paid Consulting | Bootcamps
Provider Name
BigDataGuys ,
1250 Connecticut Ave, NW,Washington,D.C-20036,
Telephone No.202-897-1944
Area
NYC | D.C | Toronto | Bay Area | Online
Description
The Data Engineer training program is designed to teach Data engineers how to build and operate frameworks to handle the exploding amount of data being collected in today’s top firms.