Introduction to ETL Data Warehousing.ppt
- 1. ETL The process of updating the data warehouse.
- 2. Recent Developments in Data Warehousing: A Tutorial Hugh J. Watson Terry College of Business University of Georgia hwatson@terry.uga.edu http://www.terry.uga.edu/~hwatson/dw_tutorial.ppt
- 3. Two Data Warehousing Strategies Enterprise-wide warehouse, top down, the Inmon methodology Data mart, bottom up, the Kimball methodology When properly executed, both result in an enterprise-wide data warehouse
- 4. The Data Mart Strategy The most common approach Begins with a single mart and architected marts are added over time for more subject areas Relatively inexpensive and easy to implement Can be used as a proof of concept for data warehousing Can perpetuate the “silos of information” problem Can postpone difficult decisions and activities Requires an overall integration plan
- 5. The Enterprise-wide Strategy A comprehensive warehouse is built initially An initial dependent data mart is built using a subset of the data in the warehouse Additional data marts are built using subsets of the data in the warehouse Like all complex projects, it is expensive, time consuming, and prone to failure When successful, it results in an integrated, scalable warehouse
- 6. Data Sources and Types Primarily from legacy, operational systems Almost exclusively numerical data at the present time External data may be included, often purchased from third-party sources Technology exists for storing unstructured data and expect this to become more important over time
- 7. Extraction, Transformation, and Loading (ETL) Processes The “plumbing” work of data warehousing Data are moved from source to target data bases A very costly, time consuming part of data warehousing
- 8. Recent Development: More Frequent Updates Updates can be done in bulk and trickle modes Business requirements, such as trading partner access to a Web site, requires current data For international firms, there is no good time to load the warehouse
- 9. Recent Development: Clickstream Data Results from clicks at web sites A dialog manager handles user interactions. An ODS (operational data store in the data staging area) helps to custom tailor the dialog The clickstream data is filtered and parsed and sent to a data warehouse where it is analyzed Software is available to analyze the clickstream data
- 10. Data Extraction Often performed by COBOL routines (not recommended because of high program maintenance and no automatically generated meta data) Sometimes source data is copied to the target database using the replication capabilities of standard RDMS (not recommended because of “dirty data” in the source systems) Increasing performed by specialized ETL software
- 11. Sample ETL Tools Teradata Warehouse Builder from Teradata DataStage from Ascential Software SAS System from SAS Institute Power Mart/Power Center from Informatica Sagent Solution from Sagent Software Hummingbird Genio Suite from Hummingbird Communications
- 12. Reasons for “Dirty” Data Dummy Values Absence of Data Multipurpose Fields Cryptic Data Contradicting Data Inappropriate Use of Address Lines Violation of Business Rules Reused Primary Keys, Non-Unique Identifiers Data Integration Problems
- 13. Data Cleansing Source systems contain “dirty data” that must be cleansed ETL software contains rudimentary data cleansing capabilities Specialized data cleansing software is often used. Important for performing name and address correction and householding functions Leading data cleansing vendors include Vality (Integrity), Harte-Hanks (Trillium), and Firstlogic (i.d.Centric)
- 14. Steps in Data Cleansing Parsing Correcting Standardizing Matching Consolidating
- 15. Parsing Parsing locates and identifies individual data elements in the source files and then isolates these data elements in the target files. Examples include parsing the first, middle, and last name; street number and street name; and city and state.
- 16. Correcting Corrects parsed individual data components using sophisticated data algorithms and secondary data sources. Example include replacing a vanity address and adding a zip code.
- 17. Standardizing Standardizing applies conversion routines to transform data into its preferred (and consistent) format using both standard and custom business rules. Examples include adding a pre name, replacing a nickname, and using a preferred street name.
- 18. Matching Searching and matching records within and across the parsed, corrected and standardized data based on predefined business rules to eliminate duplications. Examples include identifying similar names and addresses.
- 19. Consolidating Analyzing and identifying relationships between matched records and consolidating/merging them into ONE representation.
- 20. Data Staging Often used as an interim step between data extraction and later steps Accumulates data from asynchronous sources using native interfaces, flat files, FTP sessions, or other processes At a predefined cutoff time, data in the staging file is transformed and loaded to the warehouse There is usually no end user access to the staging file An operational data store may be used for data staging
- 21. Data Transformation Transforms the data in accordance with the business rules and standards that have been established Example include: format changes, deduplication, splitting up fields, replacement of codes, derived values, and aggregates
- 22. Data Loading Data are physically moved to the data warehouse The loading takes place within a “load window” The trend is to near real time updates of the data warehouse as the warehouse is increasingly used for operational applications
- 23. Meta Data Data about data Needed by both information technology personnel and users IT personnel need to know data sources and targets; database, table and column names; refresh schedules; data usage measures; etc. Users need to know entity/attribute definitions; reports/query tools available; report distribution information; help desk contact information, etc.
- 24. Recent Development: Meta Data Integration A growing realization that meta data is critical to data warehousing success Progress is being made on getting vendors to agree on standards and to incorporate the sharing of meta data among their tools Vendors like Microsoft, Computer Associates, and Oracle have entered the meta data marketplace with significant product offerings
Editor's Notes
- #2: This presentation is based on a tutorial presented by Hugh Watson at the 2001 AMCIS. It provides material for an introduction to data warehousing. Customize it for your own purposes.
- #3: There is still debate over which approach is best.
- #4: The key is to have an overall plan, processes, and technologies for integrating the different marts. The marts may be logically rather than physically separate.
- #5: Even with the enterprise-wide strategy, the warehouse is developed in phases and each phase should be designed to deliver business value.
- #6: It is not unusual to extract data from over 100 source systems. While the technology is available to store structured and unstructured data together, the reality is that warehouse data is almost exclusively structured -- numerical with simple textual identifiers.
- #7: ETL tends to be “pick and shovel” work. Most organization’s data is even worse than imagined.
- #8: As data warehousing becomes more critical to decision making and operational processes, the pressure is to have more current data, which leads to trickle updates.
- #9: The ODS is used to support the web site dialog -- an operational process -- while the data in the warehouse is analyzed -- to better understand customers and their use of the web site.
- #10: It’s changing, but COBOL extracts are still the most common ETL process. There are multiple reasons for this -- the cost of specialized ETL software, in-house programmers who have a good knowledge of the COBOL based source systems that will be used, and the peculiarities of the source systems that make the use of ETL software difficult.
- #11: You might go to the vendors’ web sites to find a good demo to show your students.
- #12: Here’s a couple of examples: Dummy data -- a clerk enters 999-99-9999 as a SSN rather than asking the customer for theirs. Reused primary keys -- a branch bank is closed. Several years later, a new branch is opened, and the old identifier is used again.
- #13: Data cleansing is critical to customer relationship management initiatives.
- #14: A good example to use is cleansing customer data. Most students can identify with receiving multiple copies of the same catalog because the company is not doing a good data cleansing job.
- #15: The record is broken down into atomic data elements.
- #16: External data, such as census data, is often used in this process.
- #17: Companies decide on the standards that they want to use.
- #18: Commercial data cleansing software often uses AI techniques to match records.
- #19: All of the data are now combined in a standard format.
- #20: Data staging is used in cleansing, transforming, and integrating the data.
- #21: Aggregates, such as sales totals, are often precalculated and stored in the warehouse to speed queries that require summary totals.
- #22: Most loads involve only change data rather than a bulk reloading of all of the data in the warehouse.
- #23: The importance of meta data is now realized, even though creating it is not glamorous work.
- #24: Historically, each vendor had their own meta data solution -- which was incompatible with other vendors’ solutions. This is changing.