Talend Open Studio (TOS) is a wonderful open source Data Integration (DI) tool used to build end-to-end ETL solutions. This course will not only help the beginners to understand the art of data integration but also equip them with Big Data skills in the smart way. This course also aims to educate you about Big Data through Talend's powerful product "Talend for Big Data" (the first Hadoop-based data integration platform). The topics covered in the presentation are: 1. Why ETL is still essential and arrival of Big Data is not the doom of ETL era 2.How and why ETL is using Talend 3.Talend complementing Hadoop Ecosystem? Adopting to ETL-Big Data industry 4.Learn Big Data not in months but in Minutes! Sounds too good?

1. www.edureka.co/talend-for-big-data
Talend For Big Data
View Talend For Big Data course details at www.edureka.co/talend-for-big-data
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 Understand ETL which is complementing Hadoop Ecosystem
 Learn adopting to ETL-Big Data industry
 Understand why Talend is used with Big Data
 Learn Big Data not in months but in Minutes
 Use Case – Banking Industry
 Implement a Talend job with Hadoop
At the end of this session, you will be able to:
Objectives

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A Graphical Abstraction Layer on top of Hadoop Applications – this makes life so much easy in the Big Data buzz
world
The surprising stuff about the current buzz and questions heralding the end of ETL and even data warehousing
is the lack of pushback and analysis of some of the outlandish comments made
ETL with Big Data
» What no one seems to question in response to these sorts of comments is
the naive assumptions these statements are based on !!
» Is it realistic for most companies to move all of their data into Hadoop?
The typical assertion is that "Hadoop eliminates the need for ETL”…. Seriously ?

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Modern data integration tools and platforms ensure timely, trusted, relevant, secure and authoritative data
Modern integration technologies use optimizers to process information in both scale-up and scale-out
architectures, push processing into database management systems, and push processing -- not just data -- into
Hadoop
They broker and publish a data layer that abstracts processing such that multiple applications can consume and
benefit from secure and curated datasets
ETL no doubt needs to continue to evolve and adapt to developer preferences and the performance, scale and
latency needs of modern applications
Hadoop is just another engine upon which ETL and its associated technologies (like data quality and data
profiling) can run
Renaming what is commonly referred to as ETL, or worse, ignorantly dismissing data challenges and enterprise-
wide data needs, is just irresponsible
ETL with Big Data

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Is writing ETL scripts in
MapReduce code still ETL?
Is ETL running faster (in
few cases & slower in
others) on Hadoop
eliminating ETL?
Is introduction of Hadoop
changing when, where
and how ETL happens?
Yes No Yes
The question isn't really that are we eliminating ETL, but where does ETL take place & how are we changing its definition
ETL with Big Data (Contd.)

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Defining ETL
E
• represents the ability to consistently and reliably extract data with
high performance and minimal impact to the source system
T
• represents the ability to transform one or more data sets in batch or
real-time into a consumable format
L • stands for loading data into a persistent or virtual data store

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How learning ETL (along Big Data) is addressing major business problems ?
Why ETL + Hadoop?
BIG DATA
DATA
INTEGRATION
DATA QUALITY MDM ESB BPM
TALEND UNIFIED PLATFORM

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One Stop Solution!!
Improves efficiency of big data job design with graphic interface
Abstract and generates code
Run transforms inside Hadoop
Native support for HDFS, Sqoop, HBase, Mahout, Pig, Hive &
MapReduce code generate
Apache License 2.0
Embedded in Hortonworks Data Platform
Certified with Cloudera, MapR and Grenplum
An open source ecosystem

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Talend
Q. Why Talend?
Ans . Because the more connected the world becomes, the more quickly a business must adapt

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Talend is the only Graphical User Interface tool which is capable enough to “translate” an ETL job to a
MapReduce job. Thus, Talend ETL job gets executed as a MapReduce job on Hadoop and get the big data work
done in minutes
This is a key innovation which helps to reduce entry barriers in Big Data technology and allows ETL job
developers (beginners and advanced) to carry out Data Warehouse offloading to greater extent
With its Eclipse-based graphical workspace, Talend Open Studio for Big Data enables the developer and data
scientist to leverage Hadoop loading and processing technologies like HDFS, HBase, Hive, and Pig without
having to write Hadoop application code
Hadoop Applications, Seamlessly Integrated within minutes using Talend
Why Talend?

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By simply selecting graphical components from a palette, arranging and configuring them, you can create Hadoop jobs
For example:
1. Load data into HDFS (Hadoop Distributed File System)
2. Use Hadoop Pig to transform data in HDFS
3. Load data into a Hadoop Hive based data warehouse
4. Perform ELT (extract, load, transform) aggregations in Hive
5. Leverage Sqoop to integrate relational databases and Hadoop
Why Talend? (Contd.)

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Talend Hadoop Integration

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 For Hadoop applications to be truly accessible to your organization, they need to be smoothly integrated into your
overall data flows
 Talend Open Studio for Big Data is the ideal tool for integrating Hadoop applications into your broader data
architecture
 Talend provides more built-in connector components than any other data integration solution available, with more
than 800+ connectors that make it easy to read from or write to any major file format, database, or packaged
enterprise application
 For example, in Talend Open Studio for Big Data, you can use drag 'n drop configurable components to create
data integration flows that move data from delimited log files into Hadoop Hive, perform operations in Hive, and
extract data from Hive into a MySQL database (or Oracle, Sybase, SQL Server, and so on)
Talend Hadoop Integration (Contd.)

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 More and more enterprise wanted to scale up in Hadoop/Big Data technologies with use of existing pool of
talent and reduce overspending on map-reduce programmer (which is pretty new and expensive)
 High rise of job trend in Data Scientist/Data Analysis (Talend also comes along with basic BI transformations
which reduces your dependency on simple excel dash board/ BI tools)
 Gartner is featuring Talend as the best technology in market for Data Integration and Big Data
 3 major players in Big Data industry, Hortonworks, Cloudera, MapR have already tied up with Talend for big data
solutions
 And mostly any level person in industry can quickly get started on this without much pre-requisites
 Myth : I don’t know Java programming , how would this course help me learn and excel in Big Data ? The
biggest advantage you get with Talend for Big Data is “there is no prerequisite” to learn this concept. Whether
you come with prior knowledge of Hadoop or not , this course has some or other best things to offer
Talend Hadoop Integration (Contd.)

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Learn Big Data not in months but in Minutes!! Sounds too good ? But true
Big Data in 10 minutes
HADOOP
HORTONWORKSMAPR
CLOUDERA Go from zero to big data in under 10 minutes
Get big data without coding. The Talend Big Data
Sandbox is a ready-to-run virtual environment that
includes Talend Platform for Big Data, popular
Hadoop distributions and data examples

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Who can use “Talend for Big Data”!!

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Let us all see quickly, what Talend
can do in minutes, reducing the
man-hours in doing MapReduce
programming in Hadoop, shall we?
We are just about to see the Bigger Picture

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A Banking industry use case :
“Addressing the challenges in growing the business with use of Big Data“ . We will use customer filled web-log data
(collected by bank) and with the help of Pig-ETL job will answer the question “where should bank hold marketing
campaigns for new product launch to get more business” , in ETL-Big Data Analytics style
In this section, you will be able to sense the true power of Talend+Big Data
Real time Use Case : ETL + Big Data

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Our use case setup is using the below :
» Hortonworks Sandbox 1.3
» Talend Open Studio for Big Data 5.5
» Windows 7 (64 Bit OS)
» Machine : 4GB RAM , i3 processor
Environment Setup

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Use-case demonstration has been divided into steps such as :
» Step 1:
Generate huge web-log data (we are generating our own source sample data to simulate real time data)
» Step 2:
Load the data from local file system to HDFS (Hadoop) in seconds
» Step 3:
Read from HDFS, Process via Pig Scripts and achieve results
Use case Design

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Use-case Snapshot

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References
 https://www.talend.com/resource/hadoop-applications.html
 http://www.edureka.co/blog/big-data-and-etl-are-family/

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Course Topics
 Module 1
» Role of Open Source ETL Technologies in
Big Data
 Module 2
» Talend: A Revolution in Big Data
 Module 3
» Talend: Read & Write Various Types of
Source/Target Systems
 Module 4
» Talend: How to Transform your Business:
Basic
 Module 5
» Talend: How to Transform your Business:
Advanced 1
 Module 6
» Talend: How to Transform your Business:
Advanced 2
 Module 7
» Big Data Concepts: Required for Talend
for Big Data
 Module 8
» Introduction to Talend for Big Data
 Module 9
» Hive in Talend for Big Data
 Module 10
» Pig in Talend for Big Data and Project

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LIVE Online Class
Class Recording in LMS
24/7 Post Class Support
Module Wise Quiz
Project Work
Verifiable Certificate
www.edureka.co/talend-for-big-data
How it Works?

25. Questions
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26. Slide 26 Course Url