Data modelingzone geoffrey-clark-v2

Physical Database Design for MPP and Columnar Databases

Geoffrey ClarkPrincipal at Lucidata, Inc.

September 2013

copywrite, Lucidata, 2013

Conceptual, Logical, Physical

• Conceptual links to Business Strategy.– This is now becoming more quantitative

• Logical maps to the Business Semantics.– Con-way example

• Physical maps to your Data Stores– These will be more varied and heterogeneous in

the future, due to specialization.


HBR Business Strategy

The New Dynamics of Competition, Michael D. Ryall, Harvard Business Review, June 2013

Michael Porter’s Five Forces has dominated strategic and competitive analysis since 1979. This analysis has largely been conceptual in nature.

Quantitative analysis on structured data in context is changing the nature of business culture, and improving business decisions.

This drives the demand for data modeling and management.


Design and Evolution• Hierarchies

– 14th Century Europe and the Financial Revolution– Aggregations & Allocations

• Cards, Tapes – physical analog media• Computer Science

– Moore’s Law• Processor Speed Improvements• Memory Improvements• Media Improvements – Punch Cards, Tape, Disk, Memory

• Design for Context & the Future– Character encoding - Internationalization– Calendars – Gregorian, Fiscal, Lunar, ... Y2K?

• Files and Fields– Separation of Data and Metadata– Modern versions -> XML, JSON

• Joins!– Data Sets – Super types, Sub types– Associations describe Networks!


Technology’s Improvement Pace


... and Demand Forecast


Separation of Church and State

• Operational uses– Capture the data, hand-entered <- validation– A Data Flow, such as Order to Cash cycle– Con-way example of PRO(-gressive) numbers

• Analytical uses– Desire for reports, Reporting crashes the

Operational cycle, Cash flow problem.– Banished from OLTP, go make an ODS


The Star SchemaThe purpose of business computers is to sort data. A graphical representation of sorted data is called a ‘Star Schema’.

– Michael Silves, Principal at Datamorphosis

• The right design at the right time, becomes default doctrine for DW– Early RDBMS (Relational Data Base Management Systems)

• Low memory, slow disks, slow CPU• Big Demand, with questions that spanned the datasets• Performance issues over large datasets

– Interview Business people to get questions• Pre-process the data, based on business questions

– Separation into Dimensions and Facts/Metrics• Link to Business Semantics• OLAP (On-Line Analytical Processing)• Educate Users on Aggregation and Allocation• Conformed Dimensions across Departments to give an Enterprise-wide view of the data.

• But as technology changes, problems emerge– Ad-hoc questions require redesign & rework– With business hierarchies when one concept is both a fact & dimension, e.g. Shipment– Fact tables become difficult to distribute for MPP ... e.g. Teradata prefers a normalized DW

• Example – transportation networks



Example – Multi-Modal Freight

• Shipments are agreements between a Carrier and a Shipper to move goods between two places.

• Shipments can be split into “ProFreight” (which is assigned a cost via activity-based costing).

• Shipments/ProFreight are composed of Freight handling units.

• Freight can be “re-tendered” to another carrier, in which case is is linked to the original and the new Shipment.

• Freight moves between places on one or many “VFCs” or Containers.

• Containers are moved between places on Trips.

Kimball on Transportation, 3NF


Kimball on Transportation, Star


Table Level DW diagram


Dim Modeling Dogma

• “Our carefully normalized data model can not be translated into a star schema... “– Dimensional modeling is necessary in order to

generate correct queries – Any (normalized) data model can be transformed

in a dimensional model... – ... and there exists an algorithm to do it


Dim Modeling Example


Star option considered


Bridge table(remember, we tried this)

We tried this with hesmith When selecting a main hierarchy is has too much of a downside, and you don’t have a weight factor …



Multi-fact option considered

Oracle’s Algorithmic approach


Basic DW diagram


Build Dimensional Model in BI


Freight moves through Networks


Information Factory & MPP

• Normalized Base– Integrate data once

• Source -> Normalized -> Denormalized -> OK• Source -> Denormalized? -> Un-normalized -> ?

– Detect problems and fix them once!• Does not preclude Data Marts• Massive Parallel Processing– Data distribution

• Optimizations – Broadcast, Co-location, Re-distribution• Scalability, the quest for 1:1• Normalized data - reduced IO, better match for


Bob Conway’s Rapid Methodology


Core Model with many Roles

TransactionTables

Reference Tables


Power of Conformed Dimensions


Example Data Model & Hierarchy


Data Flow and Usage


Cubes and In-memory BI

• Multi-Dimensional OLAP (MOLAP)– Drag-and-Drop OLAP environment, analysts become

capable of self-service.– Dealt with Ragged Hierarchies, common in Financial

data such as General Ledger (GL)– Limited by memory size– Pressure for more dimensionality floods cube size,

build times from relational sources exceed load windows ...

• Relational OLAP (ROLAP)copywrite, Lucidata, 2013

But a network this size choked it


Columnar vs Row-wise

• Physically store data by Column vs Row– Rather like Fifth Normal Form.– If Semantically Organized, then Rapid Response to

user’s ad-hoc aggregation requests.– Prefers batch loading, always loads once per

column, even if loading one row.• Continues to Appear and Operate as a normal

Row-wise cousin.


Columnar IO example

Compression becomes much more effective

Reading a Column is like reading a Row


Design Pattern for Log DataData Stewards for

Master DataData Stewards for

Metadata

Architects integrate data and metadata

Architects organize data for

analysis with physical in mind

Architects identify levels for analysis, and distributionColumnar

MPP


Importance of Reference Data


Infobright’s Database Landscape 2011


Analytic Database ComparisonActian

ParAccelIBM

NetezzaHP

VerticaGreenplum

Teradata

Sybase IQ


Gartner’s Magic Quadrant


Hadoop (Cloudera & Hortonworks)

“Although it’s true that Hadoop can be valuable as an analytic silo, most organizations will prefer to get the most business value out of Hadoop by integrating it with—or into—their BI, DW, DI, and analytics technology stacks.” – Philip Russom TDWI http://tdwi.org/webcasts/2013/04/integrating-hadoop-into-business-intelligence-and-data-warehousing.aspx


Hadoop for Analytics?

Analytics performs best on Structured

Data, for good reasons.

Maintain MPP strengths in the solution through

Architecture.copywrite, Lucidata, 2013

Message from Hortonworks (Hadoop)

“Although it’s true that Hadoop can be valuable as an analytic silo, most organizations will prefer to get the most business value out of Hadoop by integrating it with—or into—their BI, DW, DI, and analytics technology stacks.” – Philip Russom TDWI http://tdwi.org/webcasts/2013/04/integrating-hadoop-into-business-intelligence-and-data-warehousing.aspxcopywrite, Lucidata, 2013

Hadoop as ETL


Data Flow Reference Architecture


Message from Neo4J NoSQL


Message from MongoDB (NoSQL)

http://www.slideshare.net/fullscreen/mongodb/schema-design-by-example/1copywrite, Lucidata, 2013

Message from Couchbase (NoSQL)

http://www.couchbase.com/why-nosql/nosql-databasecopywrite, Lucidata, 2013

Data modelingzone geoffrey-clark-v2

Travel

withor intotheir

analytics

analytic silo

structured

business

data

copywrite

design