Addressing Big Data Challenges - The Hadoop Way

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Are you having doubts and questions about how to use Big Data in your organizations? The presentation here would clear some of your doubts. Feel free to comment if you have more queries or write to us at: bigdata@xoriant.com

Données & analyses

Addressing Big Data Challenges: The Hadoop Way
Presented by: Atul Dambalkar

Agenda Big Data Challenges Big Data Analytics Industry Trends Hadoop as a Solution Real Life Solution Studies
•Case Study I - Retail Industry
•Case Study II - Online Advertising Industry How Xoriant can help? Q & A

Big Data Challenges
THE FOUR V’s OF BIG DATA
Source: IBM

Traditional Approach & Its Limitations
Data Warehousing Vendors (ETL)
Costs - High Initial Setup,
Maintenance, Subscription
or Licensing Fees
No support for
unstructured data
Multiple copies of data in different formats
Data latency and
bottlenecks
No support for ad-hoc query
Note: The Logos are proprietary of the individual companies

Big Data and Analytics - Trends
Enterprise Data Hub or Data Lake (Hadoop with HDFS)
Commodity Hardware
No multiple data copies
Fault-Tolerant storage for Raw data As-Is
Current Limitations - Write/Append only, No Delete or Update
Unified Data Access
Multiple Data Processing Paradigms
In-memory processing
In-memory,
Real-time Stream processing
Analytics based on Distributed SQL Processing
Falling hardware prices
Batch mode processing for data size more than Hundreds of TBs
In-memory processing for data size less than hundreds of TBs
ETL Trends
Data
Processing
Trends
Architecture
Trends
Open Source Software

Hadoop Proposition
Open Source Ecosystem
No data loss through replicated storage (HDFS)
Runs on commodity hardware
•Map-Reduce
•Script based (Pig Latin)
•SQL like - HiveQL, Apache Drill, Presto (Facebook)
•Impala (Cloudera), HAWQ (Pivotal)
•In-memory processing (Apache Spark)
Multiple data analysis/processing paradigms

Case Study - 1
Benefits
Retail Industry
Problem Scenario
Personalize marketing campaigns, coupons, offers, marking down inventories
Improving customer loyalty – leads to sales and profitability
Competition from other retailers
ETL based analysis tasks - taking lot of time – up to 6 weeks
Software systems (Oracle, Greenplum, SAS, Teradata)
Mainframe based expensive hardware systems
Hadoop based Solution
Data stored into HDFS with replication
300 Hadoop nodes with 2PB data
Data processing time down to 1 week and even daily
Mainframe cost savings
No software licensing costs
Limitless data storage with HDFS
No multiple data copies
Low cost

Case Study - 2
Benefits
Attribution computation time down to 45 minutes
Capable of processing up to 300GB data for each computation
Manageable data storage with HDFS
Low cost
Online Advertising Industry (Attribution Computation)
Problem Scenario
Growing Ads Impression and conversion events
Longer attribution computation time (6 to 8 hours for each computation run). Advertisers needed quick results
Unable to process more than 150GB data within each computation
IBM Netezza based solution along with Oracle
Expensive hardware and software costs
Hadoop based Solution
Data stored into HDFS with replication
Initially used HiveQL then moved to Cloudera Impala (MPP architecture based Distributed SQL Engine)

Xoriant Big Data Practice - Overview Understands technological needs and organizational challenges faced with respect to Big Data Understands rapidly evolving Big Data technology space Can help bridge the gaps with Big Data capabilities Brings Big Data and NoSQL technology expertise

Xoriant – Big Data Center of Excellence
Email: bigdata@xoriant.com
For FREE consultation, please contact us on the above mentioned email address.
Do you have any Questions?

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Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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