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ETL Design Methodology DocumentMicrosoft SQL Server Integration Services

Durable Impact Consulting, Inc.

www.durableimpact.com

Version 1.0 Wes Dumey

Copyright 2006 Protected by the Open Document License

ETL Methodology Document

Document Licensing StandardsThis document is protected by the copyright laws of the United States of America. In order to facilitate development of an open source ETL Methodology document, users are permitted to modify this document at will, with the expressed understanding that any changes that improve upon or add to this methodology become property of the open community and must be forwarded back to the original author for inclusion in future releases of this document. This document or any portion thereof may not be sold or bartered for any form of compensation without expressed written consent of the original author. By using this document you are agreeing to the terms listed above.

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ETL Methodology Document

OverviewThis document is designed for use by business associates and technical resources to better understand the process of building a data warehouse and the methodology employed to build the EDW. This methodology has been designed to provide the following benefits: 1. A high level of performance 2. Scalable to any size 3. Ease of maintenance 4. Boiler-plate development 5. Standard documentation techniques

ETL DefinitionsTerm ETL Extract Transform Load Definition The physical process of extracting data from a source system, transforming the data to the desired state, and loading it into a database The logical data warehouse designed for enterprise information storage and reporting A small subset of a data warehouse specifically defined for a subject area

EDW Enterprise Data Warehouse DM Data Mart

Documentation SpecificationsA primary driver of the entire process is accurate business information requirements. Durable Impact Consulting will use standard documents prepared by the Project Management Institute for requirements gathering, project signoff, and compiling all testing information.

ETL Naming ConventionsTo maintain consistency all ETL processes will follow a standard naming methodology.

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TablesAll destination tables will utilize the following naming convention: EDW__ There are six types of tables used in a data warehouse: Fact, Dimension, Aggregate, Staging, Temp, and Audit. Sample names are listed below the quick overview of table types. Fact a table type that contains atomic data Dimension a table type that contains referential data needed by the fact tables Aggregate a table type used to aggregate data, forming a pre-computed answer to a business question (ex. Totals by day) Staging Tables used to store data during ETL processing but the data is not removed immediately Temp tables used during ETL processing that can immediately be truncated afterwards (ex. storing order ids for lookup) Audit tables used to keep track of the ETL process (ex. Processing times by job) Each type of table will be kept in a separate schema. This will decrease maintenance work and time spent looking for a specific table. Table Name EDW_RX_FACT EDW_TIME_DIM EDW_CUSTOMER_AG ETL_PROCESS_AUDIT STG_DI_CUSTOMER ETL_ADDRESS_TEMP Explanation Fact table containing RX subject matter Dimension table containing TIME subject matter Aggregate table containing CUSTOMER subject matter Audit table containing PROCESS data Staging table sourced from DI system used for CUSTOMER data processing Temp table used for ADDRESS processing

ETL ProcessingThere following types of ETL jobs will be used for processing. This table lists the job type, naming convention, and explains the job functions.

Job Type Extract

Load PSA Source and LoadTemp

Explanation Extracts information from a source systems & places in a staging table Loads the persistent staging area Sources information from Page 4 of 12

Naming Convention Extract ExtractDICustomer LoadPSA Source 6/13/2012

ETL Methodology Document STG tables & performs column validation and loads temp tables used in processing Lookup Unload Dimensions Lookup and unload dimension tables into flat files LookupUnloadFacts Lookup and unload fact tables into flat files TransformFacts Transform the fact subject area data and generate insert files TransformDimensions Transform the dimension subject area data and generate insert files QualityCheck Checks the quality of the data before loaded into the EDW Aggregate Aggregates data Update Records Loads/Inserts the changed records into the EDW SourceSTGDICustomer

LookupUnloadDimensions

LookupUnloadFacts TransformFacts

TransformDimensions

QualityCheck QualityCheckCustomer Aggregate Update Records

ETL Job StandardsAll ETL jobs will be created with a boiler-plate approach. This approach allows for rapid creation of similar jobs while keeping maintenance low.

CommentsEvery job will have a standard comment template that specifically spells out the following attributes of the job: Job Name: LoadPSA Purpose: Load the ETL_PSA_CUSTOMERS Predecessor: Extract Customers Date: July 10, 2007 Author: Wes Dumey Revision History: April 21, 2007 Created the job from standard template May 22, 2007 Added new columns to PSA tables In addition there will also be a job data dictionary that describes every job in a table such that it can be easily searched via standard SQL.

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Persistent Staging AreasData will be received from the source systems in its native format. The data will be stored in a PSA table following the naming standards listed previously. The table will contain the following layout: Column ROW_NUMBER DATE STATUS_CODE Data Type NUMBER DATE CHAR(1) Explanation Unique for each row in the PSA Date row was placed in the PSA Indicates status of row (I inducted, P processed, R rejected) Code uniquely identifying problems with data if STATUS_CODE = R Batch number used to process the data (auditing)

ISSUE_CODE

NUMBER

BATCH_NUMBER Data columns to follow

NUMBER

AuditingThe ETL methodology maintains a process for providing audit and logging capabilities. For each run of the process, a unique batch number composed of the time segments is created. This batch number is loaded with the data into the PSA and all target tables. In addition, an entry with the following data elements will be made into the ETL_PROCESS_AUDIT table. Column DATE BATCH_NUMBER PROCESS_NAME PROCESS_RUN_TIME PROCESS_STATUS ISSUE_CODE Data Type DATE NUMBER VARCHAR TIMESTAMP CHAR NUMBER Explanation (Index) run date Batch number of process Name of process that was executed Time (HH:MI:SS) of process execution S SUCCESS, F FAILURE Code of issue related to process failure (if F) Row count of records processed during run

RECORD_PROCESS_COUNT NUMBER

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ETL Methodology Document The audit process will allow for efficient logging of process execution and encountered errors.

QualityDue to the sensitive nature of data within the EDW, data quality is a driving priority. Quality will be handled through the following processes: 1. Source job - the source job will contain a quick data scrubbing mechanism that verifies the data conforms to the expected type (Numeric is a number and character is a letter). 2. Transform the transform job will contain matching metadata of the target table and verify that NULL values are not loaded into NOT NULL columns and that the data is transformed correctly. 3. QualityCheck a separate job is created to do a cursory check on a few identified columns and verify that the correct data is loaded into these columns.

Source QualityA data scrubbing mechanism will be constructed. This mechanism will check identified columns for any anomalies (ex. Embedded carriage returns) and value domains. If an error is discovered, the data is fixed and a record is written in the ETL_QUALITY_ISSUES table (see below for table definition).

Transform QualityThe transformation job will employ a matching metadata technique. If the target table enforces NOT NULL constraints, a check will be built into the job preventing NULLS from being loaded and causing a jobstream abend.

Quality CheckQuality check is the last point of validation within the jobstream. QC can be configured to check any percentage of rows (0-100%) and any number of columns (1-X). QC is designed to pay attention to the most valuable or vulnerable rows with the data sets. QC will use a modified version of the data scrubbing engine used during the source job to derive correct values and reference rules listed in the ETL_QC_DRIVER table. Any suspect rows will be pulled from the insert/update files, updated in the PSA table to a R status and create an issue code for the failure. Logging of Data Failures

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ETL Methodology Document Data that fails the QC job will not be loaded into the EDW based on defined rules. An entry will be made into the following table (ETL_QUALITY_ISSUES). An indicator will show the value of the column as defined in the rules (H HIGH, L LOW). This indicator will allow resources to be used efficiently to trace errors. ETL_QUALITY_ISSUES Column DATE BATCH_NUMBER PROCESS_NAME COLUMN_NAME COLUMN_VALUE EXPECTED_VALUE ISSUE_CODE SEVERITY Data Type DATE NUMBER VARCHAR VARCHAR VARCHAR VARCHAR NUMBER CHAR Explanation Date of entry Batch number of process creating entry Name of process creating entry Name of column failing validation Value of column failing validation Expected value of column failing validation Issue code assigned to error H HIGH, L LOW

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