7Strategies for Extracting, Transforming, and Loading

Programs

Tools

ETL

Operationalsystems

Warehouse

Gateways

Extraction, Transformation, and Loading Processes (ETL)

• Extract source data• Transform and cleanse

data• Index and summarize

• Load data into warehouse

• Detect changes• Refresh data

Data Staging Area

• The construction site for the warehouse

• Required by most implementations

• Composed of ODS, flat files, or relational server tables

• Frequently configured as multitier staging

ExtractTransformTransport Transform

Transport(Load)

Operationalenvironment

Stagingenvironment

Warehouseenvironment

Preferred Traditional Staging Model

Remote staging: Data staging area in its own environment, avoiding negative impact on the warehouse environment

Extracting Data

• Routines developed to select fields from source

• Various data formats

• Rules, audit trails, error correction facilities

• Various techniques

Examining Source Systems

• Production– Legacy systems– Database systems– Vertical applications

• Archive– Historical (for initial load)– Used for query analysis– May require transformations

Mapping

• Defines which operational attributes to use

• Defines how to transform the attributes for the warehouse

• Defines where the attributes exist in the warehouse

Designing Extraction Processes

• Analysis– Sources, technologies– Data types, quality, owners

• Design options– Manual, custom, gateway, third-party– Replication, full, or delta refresh

• Design issues– Batch window, volumes, data currency– Automation, skills needed, resources

• Maintenance of metadata trail

Importance of Data Quality

• Business user confidence

• Query and reporting accuracy

• Standardization

• Data integration

Benefits of Data Quality

Cleansed data is critical for:

• Standardization within the warehouse

• High quality matching on names and addresses

• Creation of accurate rules and constraints

• Prediction and analysis

• Creation of a solid infrastructure to support customer-centric business intelligence

• Reduction of project risk

• Reduction of long term costs

Guidelines for Data Quality

• Operational data should not be used directly in the warehouse.

• Operational data must be cleaned for each increment.

• Operational data is not simply fixed by modifying applications.

Transformation

Transformation eliminates operational data anomalies:

• Cleans

• Standardizes

• Presents subject-oriented data

ExtractTransformTransport Transform

Transport(Load)

Restructure

Consolidate

Cleanse

Transformation Routines

• Cleansing data

• Eliminating inconsistencies

• Adding elements

• Merging data

• Integrating data

• Transforming data before load

Why Transform?

• In-house system development

• Multipart keys

• Multiple encoding

• Multiple local standards

Why Transform?

• Multiple files

• Missing values

• Duplicate values

• Element names

Why Transform?

• Element meaning

• Input format

• Referential integrity

Why Transform?

Name and address:• No unique key• Missing data values (NULLs)• Personal and commercial names mixed• Different addresses for the same member• Different names and spelling for the same member• Many names on one line• One name on two lines• The data may be in a single field of no fixed format• Each component of an address is in a specific field

Integration (Match and Merge)

Source Target

Match and Merge

schema

Transformation Techniques

• Merging data– Operational transactions do not usually map

one-to-one with warehouse data.– Data for the warehouse is merged to provide

information for analysis.

• Adding keys to data

Transformation Techniques

Time

Transformation Techniques

Adding a date stamp:

• Fact table– Add triggers– Recode applications– Compare tables

• Dimension table

• Time representation– Point in time– Time span

Transformation Techniques

Creating summary data:

• During extraction on staging area

• After loading onto the warehouse server

109908 109908 01

Transformation Techniques

Creating artificial keys:

• Use generalized or derived keys

• Maintain the uniqueness of a row

• Use an administrative process to assign the key

• Concatenate operational key with number

• Easy to maintain

• Cumbersome keys

• No clean value for retrieval

Where to Transform?

Choose wisely where the transformation takes place:

• Operational platform

• Staging area

• Warehouse server

When to Transform?

Choose the transformation point wisely:

• Workload

• Environment impact

• CPU use

• Disk space

• Network bandwidth

• Parallel execution

• Load window time

• User information needs

Designing Transformation Processes

• Analysis– Sources and target mappings, business rules– Key users, metadata, grain, verify integrity of data

• Design options– Programming, Tools

• Design issues– Performance– Size of the staging area– Exception handling, integrity maintenance

Loading Data into the Warehouse

• Loading moves the data into the warehouse.

• Subsequent refresh moves smaller volumes.

• Business determines the cycle.

ExtractTransformTransport Transform

Transport(Load)

Operationalenvironment

Stagingenvironment

Warehouseenvironment

Extract versus Warehouse Processing Environment

• Extract processing builds a new database after each time interval.

• Warehouse processing adds changes to the database after each time interval.

T1 T2 T3

Operationaldatabases

T1 T2 T3

Operationaldatabases

First-Time Load

• Single event that populates the database with historical data

• Involves a large volume of data

• Uses distinct ETL tasks

• Involves large amounts of processing after load

Refresh

• Performed according to a business cycle

• Simpler task

• Less data to load than first-time load

• Less complex ETL

• Smaller amounts of postload processing

Building the Transportation Process

Specification:

• Techniques and tools

• File transfer methods

• The load window

• Time window for other tasks

• First-time and refresh volumes

• Frequency of the refresh cycle

• Connectivity bandwidth

Building the Transportation Process

• Test the proposed technique

• Document proposed load

• Gain agreement on the process

• Monitor

• Review

• Revise

Granularity

• Important design and operational issue

• Low-level grain: Expensive, high level of processing, more disk, detail

• High-level grain: Cheaper, less processing, less disk, little detail

• Space requirements– Storage– Backup– Recovery– Partitioning– Load

Post-Processing of Loaded Data

Extract Transform Transport

Summarize Index