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BI 2.0 – Hadoop Everywhere. What New
Skills are Required.
Dmitry Tolpeko, EPAM Systems - December 2014
What is Big Data?
• Not just large data volume
• Approach to perform data
intensive computation in a
scalable way
Data Volume Problem
• Machine Generated Data
• I/O Limitations
What was Before
• Expensive Commercial MPP
Systems (Teradata, Oracle,
Netezza etc.)
• Open source products (MySQL,
PostgreSQL etc.)
• NoSQL products
• Cloud
Scalable Applications
• Threads and Concurrency
• Distributed processing
• Scheduling
• Sharding
• Fault-Tolerance
Infrastructure code takes almost
all time
Hadoop
• Distributed File System – Any data,
any format
• Generic Framework for Scalable
Applications (not only SQL):
• SQL-on-Hadoop
• Map Reduce, Spark, Tez
• Graph
• Machine Learning, Neural Networks etc.
• Ideal for Cloud – Scalable and Elastic
Must Have
• Universal Data Platform for Enterprise
– Landing Zone (Active HDD)
• Oracle Big Data Appliance
• IBM BigInsight
• Microsoft HDInsight
• Teradata
• HP
Forrester: “Hadoop is no longer
optional”
Developers
• Cool frameworks
• Distributed systems
• Real-time, Machine Learning etc.
New Concepts
• Data Lake
• Lambda Architecture
• Active Archive
BI Trends
• Moving workload to Hadoop
• Analyzing unstructured data
• Streaming and real time –
Proactive and Predictive
analytics
Problems
• Data Quality
• Data Consistency
• Security
Two Worlds
• Business Intelligence and Data
Warehousing
• Software Development
Java and Scala
• Products and tools
• Custom Frameworks
• Real time
• Advanced processing
New Skills for Software Developers
Distributed processing, machine
learning
Traditional Data warehousing and
BI concepts:
• Kimball, Inmon
• Facts and Dimensions (slowly
changing dimensions i.e.)
Data Science
Gradual Transformation from
traditional Data Analysis to Machine
Learning
• Analysts
• BI/Database Developers
• QA
Like Architect in Software
Development
Python
Rich libraries:
• Pandas
• NumPy
• SciPy
SQL-on-Hadoop
Dozen of SQL-on-Hadoop tools:
• Schemaless SQL – Apache Drill
i.e.
• SQL on top of NoSQL
• SQL for Graphs, Machine
Learning etc.
Everywhere
It is Ecosystem not a set of tools
Yesterday: Map Reduce
Today: YARN, Spark etc.
Tomorrow: ?
Must have skill set
Like Linux
Thank you!
Dmitry_Tolpeko@epam.com
www.dmtolpeko.com
Join Belarus Hadoop User Group at LinkedIn

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