Cloudera - Amr Awadallah - Hadoop World 2010

Business Analyst Tools for Hadoop
Amr Awadallah
CTO, Cloudera, Inc.
Hadoop World
October 12th, 2010
Copyright 2010 Couldera Inc. All Rights Reserved.
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The Spectrum of Hadoop Users
Copyright 2010 Cloudera Inc. All rights reserved
2
BI, Analytics
IDEs
Enterprise Reporting
Analysts
Operators
Business Users
Customers
Engineers
Cloudera Enterprise
Enterprise
Data
Warehouse
Low-Latency Serving Systems
Web Application
Logs
Relational Databases
Web Data
Files

Evolution of Hadoop Query/Programming Languages
Java MapReduce: Gives the most flexibility and performance, but potentially long development cycle (the “assembly language” of Hadoop).
Streaming MapReduce (also Pipes): Allows you to develop in any programming language of your choice, but slightly lower performance and less flexibility.
Cascading: Cascading is a thin Java library that sits on top of MapReduce, it lets developers assemble complex processes.
Pig: A high-level language out of Yahoo, suitable for batch data flow workloads.
Hive: A SQL interpreter out of Facebook, also includes a meta-store mapping files to their schemas and associated SerDe.
Oozie: A PDL XML workflow server engine that enables creating a workflow of jobs composed of any of the above.
3

Hive vs Pig Example (count distinct values > 0)
Hive syntax:
SELECT COUNT(DISTINCT col1)
FROM mytable
WHERE col1 > 0;
Pig syntax:
mytable = LOAD ‘myfile’ AS (col1, col2, col3);
mytable = FOREACH mytable GENERATE col1;
mytable = FILTER mytable BY col1 > 0;
mytable = DISTINCT col1;
mytable = GROUP mytable BY col1;
mytable = FOREACH mytable GENERATE COUNT(mytable);
DUMP mytable;
4

Hive Features
A subset of SQL covering the most common statements
Agile data types: Array, Map, Struct, and JSON objects
User Defined Functions and Aggregates
Regular Expression support
MapReduce support
JDBC/ODBC support
Partitions and Buckets (for performance optimization)
In The Works: Indices, Columnar Storage, Views, Microstrategy compatibility, Explode/Collect
More details: http://wiki.apache.org/hadoop/Hive
5

The Hadoop Query Tool Ecosystem
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In Memory
ETL
Query Authoring
Spreadsheet
BI/OLAP
Developer
Reporting
Stats/Math
MicroStrategy
IBM Cognos
SAP BOBJ
Microsoft SSRS
Jaspersoft
Pentaho
Informatica
Pervasive
IBM DataStage
Microsoft SSIS
Talend
Kettle
Karmasphere
Eclipse
Cascading
PowerPivot
QlikTech
EdgeSpring
Tableau
Karmasphere
Quest (Toad)
SAS
IBM SPSS
Matlab
R/RHIPE
Mahoot
Hama
SAP Crystal
Actuate/BIRT
IBM BigSheets
Datameer
Cloudera Enterprise
Cloudera’s Distribution for Hadoop
Hadoop is very flexible, use the right tool for the job at hand.

Toad for Cloud (for Query Authoring)
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Hadoop
RDBMS
Learn more at: http://www.ToadForCloud.com

Karmasphere (for Developers and Analysts)
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Tableau (for Advanced Visualization)
9

Datameer (for Analysts, Spreadsheet UI)
10

MicroStrategy (for interactive Dashboards)
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Talend (for Extract-Tranform-Load, aka ETL)
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General Advice for Choosing the Right Tool.
First and foremost, what problem are you trying to solve? And what is your skill set? Use the tool that gets you there fastest.
What is the learning curve involved with this new tool?
Does the tool interoperate with other systems?
Is the tool leveraging the investment in Pig/Hive?
Does the tool lock you in to a proprietary file format?
Is the tool certified for Cloudera’s Distribution of Hadoop?
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Appendix
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Hive Agile Data Types
STRUCTS:
SELECT mytable.mycolumn.myfield FROM …
MAPS (Hashes):
SELECT mytable.mycolumn[mykey] FROM …
ARRAYS:
SELECT mytable.mycolumn[5] FROM …
JSON:
SELECT get_json_object(mycolumn, objpath
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Cloudera - Amr Awadallah - Hadoop World 2010

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