The document discusses big data, its challenges, and the advantages of using Hadoop over traditional relational databases (RDBMS). It highlights the limitations of RDBMS for processing large datasets and the structure and components of Hadoop that make it suitable for handling big data. Additionally, it presents case studies demonstrating Hadoop's effectiveness in ETL processes and customer analytics.

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DWH & Big Data
Odessa
Vladimir Slobodianiuk
Date: 2014

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Agenda
1
2
Big Data – what is it
Hadoop vs RDBMS – pros and cons
3 Hadoop & Enterprise architecture
4 Hadoop as ETL engine
5 Case Studies

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Big Data
– what is it

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Current state
 Big data - is an all-encompassing term for any collection of data sets so large and
complex that it becomes difficult to process using traditional data processing
applications.

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Limitations & Problems
 Big data is difficult to work with using
most relational databases, requiring
instead massively parallel software
running on tens, hundreds, or even
thousands of servers
 eBay.com uses two data warehouses at 7.5 petabytes
 Walmart handles more than 1 million customer
transactions every hour
 Facebook handles 50 billion photos from its user base
 In 2012, the Obama administration announced the Big
Data Research and Development Initiative

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Hadoop vs RDBMS

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CORE HADOOP - MapReduce
In 2004, Google published a paper on a process called MapReduce
 DISTRIBUTED
COMPUTING
FRAMEWORK
 Process large jobs in
parallel across many
nodes and combine the
results

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Hadoop Structure
 HDFS is a distributed file system designed to run on commodity hardware
 HBase store data rows in labelled tables (sortable key and an arbitrary number of columns)
 Hive provide data summarization, query, and analysis (SQL-like interface)
 Pig is a platform for analyzing large data sets that consists of a high-level language

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Hadoop vs RDBMS
Hadoop RDBMS
 Performance for relational data
 Machine query optimization
 Mature workload management
 High concurrency interactive query
processing
How might this change in the future
 Query Optimization Improvements in Hive
– Statistics, better join ordering, more join types, etc
 Startup Time Improvements
– Simpler query plans to pass out
 Runtime Performance Improvements
 Schema-less Model
 Human query optimization
 Ability to create complex dataflow
with multiple inputs and outputs
 Parallelize many Analytic Functions

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Hadoop &
Enterprise architecture

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Classic architecture approach

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Hadoop & Enterprise architecture

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Case Study 1
Hadoop as ETL Data Quality tool
BENEFITS
 Reduced TCO (commodity hardware usage)
 Traceability of all the data quality issues
 Hadoop becomes clean data tool.
PROBLEM
Traditional tools show poor performance in exception
and data cleansing.
SOLUTION
Hadoop transforms the data into single format and
processes it using data cleansing workflows.

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Case Study 2
Know Your Customer PoC
Business Challenge
• Knowing the actual customer
reaction to products is essential
for business growth, but it’s
difficult to get valuable insights.
Social media is the place where
customer really share their
opinion
SOLUTION
Hadoop-based analysis tool that
provides the ability to:
• Find the events in the client
streams, identify needed
reaction
• Propose a product to a client,
based on his interests

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Case Study 3
Enterprise ETL & Hadoop Integration
Goals:
 MapReduce ETL jobs development
without coding
 Build, re-use, and check impact analysis
with enhanced metadata capabilities
 A windows-based graphical development
environment
 Comprehensive built-in transformations
 A library of Use Case Accelerators to
fast-track Hadoop productivity

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Big Data:

Cutting edge of DI technologies

State-of-the-art design approaches

A bit more than simple development, it's some of art, art
of data management
Summary

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THANK YOU