ETL

By Dr. Gabriel

ETL Process

• 4 major components:– Extracting

• Gathering raw data from source systems and storing it in ETL staging environment

– Cleaning and conforming• Processing data to improve its quality, format it, merge from

multiple sources, enforce conformed dimensions

– Delivering• Loading data into data warehouse tables

– Managing• Management of ETL environment

ETL: Extracting

• Data profiling

• Identifying data that changed since last load

• extraction

ETL: Cleaning and Conforming

• Data cleansing

• Recording error events

• Audit dimensions

• Deduping

• Creating and maintaining conformed dimensions and facts

ETL: Delivering

• Implementation of SCD logic• Surrogate key generation• Managing hierarchies in dimensions• Managing special dimensions such as date and

time, junk, mini, shrunken, small static, and user-maintained dimensions– Mini dimensions

• used to track changes of dimension attribute when type 2 technique is infeasible.

• Similar to junk dimensions Typically is used for large dimensions

• Combinations can be built in advance or on the fly• Built from dimension table input

ETL: Delivering (Cont)

– Small static dimensions• Dimensions created by the ETL system without

real source• Lookup dimensions for translations of codes, etc.

– User maintained dimensions• Master dimensions without real source system• Descriptions, groupings, hierarchies created for

reporting and analysis purposes.

ETL: Delivering (Cont)

• Fact table loading• Building and maintaining bridge dimension

tables• Handling late arriving data• Management of conformed dimensions• Administration of fact tables• Building aggregations• Building OLAP cubes• Transferring DW data to other environment for

specific purposes

ETL: Managing

• Management of ETL environment– Goals

• Reliability• Availability• Manageability

– Job scheduler– backup system– Recovery and restart system– Version control system

ETL: Managing (Cont.)

• Version migration system• Workflow monitor• Sorting system• Analyzing dependencies and lineage• Problem escalation system• Parallelization• Security system• Compliance manager• Metadata repository manager

ETL Process

• Planning– High level source to target data flow diagram– Selection and implementation of ETL tool– Development of default strategies for

dimension management, error handling, and other processes

– Development data transformations diagrams by target table

– Development of job sequencing

ETL Process

• Developing one-time historic load– Build and test the historic dimension and fact

tables load

• Developing incremental load process– Build and test dimension and fact tables

incremental load processes– Build and test aggregate table loads and/or

OLAP processing– Design, build, and test the ETL system

automation

ETL Tools: Build vs Buy

• Many off-the-shelf tools exist

• Benefits are not seen right away– Setup– Learning curve

• High-end tools may not justify value for smaller warehouses

Off-the-shelf ETL ToolsTool Vendor

Oracle Warehouse Builder (OWB) Oracle 

Data Integrator (BODI) Business Objects

IBM Information Server (Ascential) IBM

SAS Data Integration Studio SAS Institute

PowerCenter Informatica 

Oracle Data Integrator (Sunopsis) Oracle

Data Migrator Information Builders

Integration Services Microsoft

Talend Open Studio Talend

DataFlow Group 1 Software (Sagent)

Data Integrator Pervasive

Transformation Server DataMirror

Transformation Manager ETL Solutions Ltd.

Data Manager Cognos

DT/Studio Embarcadero Technologies

ETL4ALL IKAN

DB2 Warehouse Edition IBM

Jitterbit Jitterbit

Pentaho Data Integration Pentaho 

ETL Specification Document

• Can be as large as 100 pages per business process; In reality, the work starts after the high level design is documented in a few pages.

• Source-to-target mappings• Data profiling reports• Physical design decisions• Default strategy for extracting from each major

source system• Archival strategy• Data quality tracking and metadata• Default strategy for managing changes to

dimension attributes

ETL Specification Document (Cont)

• System availability requirements and strategy• Design of data auditing mechanism• Location of staging areas• Historic and incremental load strategies for each

table– Detailed table design– Historic data load parameters (# of months) and

volumes (# of rows)– Incremental data volumes

ETL Specification Document (Cont)

– Handling of late arriving data– Load frequency– Handling of changes in each dimension attribute

(types 1,2,3)– Table partitioning– Overview of data sources; discussion of source-

specific characteristics– Extract strategy for the source data– Change data capture logic for each source table– Dependencies– Transformation logic (diagram or pseudo code)

ETL Specification Document (Cont)

– Preconditions to avoid error conditions– Recovery and restart assumptions for each

major step of the ETL pipeline– Archiving assumptions for each table– Cleanup steps– Estimated effort

• Overall workflow• Job sequencing• Logical dependencies

Loading Pointers

• One time historic load– Disable RI constraints (FKs) and re-enable

them after the load is complete– Drop indexes and re-create them after the

load is complete– Use bulk loading techniques– Not always the case

Loading Pointers (Cont)

• Incremental load

Loading Pointers (Cont)

– Sometimes historic and incremental load logic is the same; many times- is similar.

– Updating aggregations, if necessary– Error handling

Sample: Generation of Surrogate Keys on SQL Server

As simple as:DECLARE @i INTEGER SELECT @i = MAX(ID) + 1 FROM TableNameBut may not work with concurrent processes

ORCreate PROCEDURE pGetNextID (@SeedName VARCHAR(32), @SeedValue BIGINT OUTPUT) AS

UPDATE Lookup_Seed SET @SeedValue = SeedValue = SeedValue + 1

WHERE SeedID = @SeedName

Lookup_Seed table:SeedID varchar (32)SeedValue bigint

Questions ?