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Integrated Data Warehouse with Hadoop and Oracle Database
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Gwen (Chen) Shapira
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Use cases and advice on integrating Hadoop with the enterprise data warehouse
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Integrated Data Warehouse with Hadoop and Oracle Database
1.
Building the Integrated
Data Warehouse With Oracle Database and Hadoop Gwen Shapira, Senior Consultant
2.
Why Pythian • Recognized
Leader: • Global industry leader in data infrastructure managed services and consulting with expertise in Oracle, Oracle Applications, Microsoft SQL Server, MySQL, big data and systems administration • Work with over 200 multinational companies such as Forbes.com, Fox Sports, Nordion and Western Union to help manage their complex IT deployments • Expertise: • One of the world’s largest concentrations of dedicated, full-time DBA expertise. Employ 8 Oracle ACEs/ACE Directors • Hold 7 Specializations under Oracle Platinum Partner program, including Oracle Exadata, Oracle GoldenGate & Oracle RAC • Global Reach & Scalability: • 24/7/365 global remote support for DBA and consulting, systems administration, special projects or emergency response © 2012 – Pythian
3.
About Gwen Shapira
• Oracle ACE Director • 13 Years with pager • 7 as Oracle DBA • Senior Consultant: • Has MacBook, will travel. • @gwenshap • http://www.pythian.com/news/author/ shapira/ © 2012 – Pythian
4.
Agenda • What is
Big Data? • Why do we care about Big Data? • Why your DWH needs Hadoop? • Examples of Hadoop in the DWH • How to integrate Hadoop into your DWH • Avoiding major pitfalls © 2012 – Pythian
5.
What is Big
Data?
6.
MORE DATA THAN
YOU CAN HANDLE © 2012 – Pythian
7.
MORE DATA THAN RELATIONAL
DATABASES CAN HANDLE © 2012 – Pythian
8.
MORE DATA THAN RELATIONAL
DATABASES CAN HANDLE CHEAPLY © 2012 – Pythian
9.
Data Arriving at
fast Rates Typically unstructured Stored without aggregation Analyzed in Real Time For Reasonable Cost © 2012 – Pythian
10.
Where does Big
Data come from? • Social media • Enterprise transactional data • Consumer behaviour • Multimedia • Sensors and embedded devices • Network devices © 2012 – Pythian
11.
Why all the
Excitement? © 2012 – Pythian
12.
Complex Data Architecture
© 2012 – Pythian
13.
Your DWH needs
Hadoop
14.
Big Problems with
Big Data • It is: • Unstructured • Unprocessed • Un-aggregated • Un-filtered • Repetitive • And generally messy. Oh, and there is a lot of it. © 2012 – Pythian
15.
Technical Challenges • Storage
capacity • Storage throughput Scalable storage • Pipeline throughput • Processing power • Parallel processing Massive Parallel Processing • System Integration • Data Analysis Ready to use tools © 2012 – Pythian
16.
Hadoop Principles Bring Code
to Data Share Nothing © 2012 – Pythian
17.
Hadoop in a
Nutshell HDFS: Map-Reduce: Replicated Distributed Big-Data File Framework for writing massively parallel jobs System © 2012 – Pythian
18.
Hadoop Benefits • Reliable
solution based on unreliable hardware • Designed for large files • Load data first, structure later • Designed to maximize throughput of large scans • Designed to maximize parallelism • Designed to scale • Flexible development platform • Solution Ecosystem © 2012 – Pythian
19.
Hadoop Limitations • Hadoop
is scalable but not fast • Batteries not included • Instrumentation not included either • Well-known reliability limitations © 2012 – Pythian
20.
Hadoop in the
Data Warehouse Use Cases and Customer Stories
21.
ETL for Unstructured
Data Logs Flume Hadoop Web servers, Cleanup, app server, aggregation clickstreams Longterm storage DWH BI, batch reports © 2012 – Pythian
22.
ETL for Structured
Data Sqoop, Hadoop OLTP Oracle, Perl Transformation MySQL, aggregation Informix… Longterm storage DWH BI, batch reports © 2012 – Pythian
23.
Bring the World
into Your Datacenter © 2012 – Pythian
24.
Rare Historical Report
© 2012 – Pythian
25.
Find Needle in
Haystack © 2012 – Pythian
26.
We are not
doing SQL anymore © 2012 – Pythian
27.
Connecting the (big)
Dots
28.
Sqoop
Queries © 2012 – Pythian
29.
Sqoop is Flexible
(for import) • Select columns from table where condition • Or write your own query • Split column • Parallel • Incremental • File formats © 2012 – Pythian
30.
Sqoop Import Examples •
Sqoop import -‐-‐connect jdbc:oracle:thin:@//dbserver: 1521/masterdb -‐-‐username hr -‐-‐table emp -‐-‐where “start_date ’01-‐01-‐2012’” • Sqoop import jdbc:oracle:thin:@//dbserver:1521/masterdb -‐-‐username myuser -‐-‐table shops -‐-‐split-‐by shop_id Must be indexed or -‐-‐num-‐mappers 16 partitioned to avoid 16 full table scans © 2012 – Pythian
31.
Less Flexible Export
• 100row batch inserts • Commit every 100 batches • Parallel export • Update mode Example: sqoop export -‐-‐connect jdbc:oracle:thin:@//dbserver:1521/masterdb -‐-‐table bar -‐-‐export-‐dir /results/bar_data © 2012 – Pythian
32.
Fuse-DFS • Mount HDFS
on Oracle server: • sudo yum install hadoop-0.20-fuse • hadoop-fuse-dfs dfs:// name_node_hostname:namenode_port mount_point • Use external tables to load data into Oracle • File Formats may vary • All ETL best practices apply © 2012 – Pythian
33.
Oracle Loader for
Hadoop • Load data from Hadoop into Oracle • Map-Reduce job inside Hadoop • Converts data types. • Partitions and sorts • Direct path loads • Reduces CPU utilization on database © 2012 – Pythian
34.
Oracle Direct Connector
to HDFS • Create external tables of files in HDFS • PREPROCESSOR HDFS_BIN_PATH:hdfs_stream • All the features of External Tables • Tested (by Oracle) as 5 times faster (GB/s) than FUSE-DFS © 2012 – Pythian
35.
Big Data Appliance
and Exadata © 2012 – Pythian
36.
How not to
Fail
37.
Data that belongs
in RDBMS © 2012 – Pythian
38.
Prepare for Migration
© 2012 – Pythian
39.
Use Hadoop Efficiently •
Understand your bottlenecks: • CPU, storage or network? • Reduce use of temporary data: • All data is over the network • Written to disk in triplicate. • Eliminate unbalanced workloads • Offload work to RDBMS • Fine-tune optimization with Map-Reduce © 2012 – Pythian
40.
Your Data
is NOT as BIG as you think © 2012 – Pythian
41.
Getting Started • Pick a
Business Problem • Acquire Data • Use right tool for the job • Hadoop can start on the cheap • Integrate the systems • Analyze data • Get operational © 2012 – Pythian
42.
Thank you and
QA To contact us… sales@pythian.com 1-877-PYTHIAN To follow us… http://www.pythian.com/news/ http://www.facebook.com/pages/The-Pythian-Group/163902527671 @pythian @pythianjobs http://www.linkedin.com/company/pythian © 2012 – Pythian
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