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ETL

ETL. Prof. Navneet Goyal Department of Computer Science & Information Systems BITS, Pilani. Topics. Requirements Build or Buy? ETL Data Structures Data Flow Extract Clean & Conform Deliver Dimension Tables Fact tables Implementation & Operations. Introduction.

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ETL

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  1. ETL Prof. Navneet Goyal Department of Computer Science & Information Systems BITS, Pilani

  2. Topics • Requirements • Build or Buy? • ETL Data Structures • Data Flow • Extract • Clean & Conform • Deliver • Dimension Tables • Fact tables • Implementation & Operations

  3. Introduction • ETL system is the foundation of any DW system • Still the most under rated system! • An ETL system • Extracts data from source systems • Enforces data quality & consistency • Conforms data so that separate source systems could be used together • Delivers data in a presentation ready format that can be used by end users

  4. Introduction • ETL system makes or breaks a DW • Building an ETL system is a back room activity not visible to end users • Consumes almost 70-80% of resources needed for implementation & maintenance of a DW • Mission of ETL system: get data out of the source systems & load it into the DW

  5. Introduction • Extract • Extract relevant data • Transform • Transform data to DW format • Build keys, etc. • Cleansing of data • Load • Load data into DW • Build aggregates, etc.

  6. Choice of Architecture Tool Based ETL Simpler, Cheaper & Faster development • People with business skills & not much technical skills can use it. • Automatically generate Metadata • Automatically generates data Lineage & data dependency analysis • Offers in-line encryption & compression capabilities • Manage complex load balancing across servers

  7. Choice of Architecture Hand-Coded ETL • Quality of tool by exhaustive unit testing • Better metadata • Requirement may be just file based processes not database-stored procedures • Use of existing legacy routines • Use of in-house programmers • Unlimited flexibility

  8. Middleware & Connectivity Tools • Provide transparent access to source systems in heterogeneous computing environments • Expensive but often prove invaluable because they provide transparent access to DBs of different types, residing on different platforms • Examples: • IBM: Data Joiner • Oracle: Transparent Gateway • SAS: SAS/Connect • Sybase: Enterprise Connect

  9. Extraction Tools • Lot of tools available in the market • Tool selection tedious • Choice of tool depends on following factors: • Source system platform and DB • Built-in extraction or duplication functionality • Batch windows of the operational systems

  10. Extraction Methods • Bulk Extractions • Entire DW is refreshed periodically • Heavily taxes the network connections between the source & target DBs • Easier to set up & maintain • Change-based Extractions • Only data that have been newly inserted or updated in the source systems are extracted & loaded into the DW • Places less stress on the network but requires more complex programming to determine when a new DW record must be inserted or when an existing DW record must be updated

  11. Transformation Tools • Transform extracted data into the appropriate format, data structure, and values that are required by the DW • Features provided: • Field splitting & consolidation • Standardization • Abbreviations, date formats, data types, character formats, etc. • Deduplication

  12. Mission of ETL team To build the back room of the DW • Deliver data most effectively to end user tools • Add value to the data in the cleaning & conforming steps • Protect & document the lineage of data

  13. Mission of ETL team The back room must support 4 key steps • Extracting data from original sources • Quality assuring & cleaning data • Conforming the labels & measures in the data to achieve consistency across the original sources • Delivering the data in a physical format that can be used by query tools and report writers

  14. ETL Data Structures Data Flow Extract  Clean  Conform  Deliver • Back room of a DW is often called the data staging area • Staging means ‘writing to disk’ • ETL team needs a number of different data structures for all kinds of staging needs

  15. To stage or not to stage • Decision to store data in physical staging area versus processing it in memory is ultimately the choice of the ETL architect

  16. To stage or not to stage • A conflict between • getting the data from the operational systems as fast as possible • having the ability to restart without repeating the process from the beginning • Reasons for staging • Recoverability: stage the data as soon as it has been extracted from the source systems and immediately after major processing (cleaning, transformation, etc). • Backup: can reload the data warehouse from the staging tables without going to the sources • Auditing: lineage between the source data and the underlying transformations before the load to the data warehouse

  17. Designing the staging area • The staging area is owned by the ETL team • no indexes, no aggregations, no presentation access, no querying, no service level agreements • Users are not allowed in the staging area for any reason • staging is a “construction” site • Reports cannot access data in the staging area • tables can be added, or dropped without notifying the user community • Controlled environment

  18. Designing the staging area (contd…) • Only ETL processes can read/write the staging area (ETL developers must capture table names, update strategies, load frequency, ETL jobs, expected growth and other details about the staging area) • The staging area consists of both RDBMS tables and data files

  19. Staging Tables Volumetric Worksheet • Lists each table in the staging area with the following information: • Table Name: name or table or file in the DSA. One row in the WS for each staging table • Update Strategy: Indicates how a table is maintained. For persistent tables it will have data appended, updated, or perhaps deleted. Transient tables are truncated and reloaded with each process • Load Frequency: How often the table is loaded or changed by the ETL process. In real-time environment – continuously • ETL Jobs • Initial Row count:

  20. Staging Area data Structures in the ETL System • Flat files • fast to write, append to, sort and filter (grep) but slow to update, access or join • enables restart without going to the sources • XML Data Sets • Used as a medium of data transfer between incompatible data sources • Gives enough information to create tables using CREATE TABLE • Relational Tables • Metadata, SQL interface, DBA support

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