Download

Ahsan Abdullah, Data Warehousing, ETL Detail: Data Extraction & Transformation

Upload
0
Embed
Share
Upload and view presentations on any device and embed the player to your website! --- > >
Upload PPT
Upload PPT

Download this Presentation

daphne

2021-9-25 published at Sports&Entertainment category

Presentation Transcript

1.Ahsan Abdullah 1 Data Warehousing Lecture-18 ETL Detail: Data Extraction & Transformation Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp National University of Computers & Emerging Sciences, Islamabad Email: ahsan1010@yahoo.com
2.Ahsan Abdullah 2 ETL Detail: Data Extraction & Transformation
3.Ahsan Abdullah 3 Extracting Changed Data Incremental data extraction Incremental data extraction i.e. what has changed, say during last 24 hrs if considering nightly extraction. Efficient when changes can be identified This is efficient, when the small changed data can be identified efficiently. Identification could be costly Unfortunately, for many source systems, identifying the recently modified data may be difficult or effect operation of the source system. Very challenging Change Data Capture is therefore, typically the most challenging technical issue in data extraction. ONLY yellow part will go to Graphics
4.Ahsan Abdullah 4 Source Systems Two CDC sources Modern systems Legacy systems ONLY yellow part will go to Graphics
5.Ahsan Abdullah 5 CDC in Modern Systems Time Stamps Works if timestamp column present If column not present, add column May not be possible to modify table, so add triggers Triggers Create trigger for each source table Following each DML operation trigger performs updates Record DML operations in a log Partitioning Table range partitioned, say along date key Easy to identify new data, say last week’s data ONLY yellow part will go to Graphics
6.Ahsan Abdullah 6 CDC in Legacy Systems Changes recorded in tapes Changes occurred in legacy transaction processing are recorded on the log or journal tapes. Changes read and removed from tapes Log or journal tape are read and the update/transaction changes are stripped off for movement into the data warehouse. Problems with reading a log/journal tape are many: Contains lot of extraneous data Format is often arcane Often contains addresses instead of data values and keys Sequencing of data in the log tape often has deep and complex implications Log tape varies widely from one DBMS to another. ONLY yellow part will go to Graphics
7.Ahsan Abdullah 7 Advantages Immediate. No loss of history Flat files NOT required CDC Advantages: Modern Systems Modern Systems
8.Ahsan Abdullah 8 Advantages No incremental on-line I/O required for log tape The log tape captures all update processing Log tape processing can be taken off-line. No haste to make waste. CDC Advantages: Legacy Systems Legacy Systems
9.Ahsan Abdullah 9 Major Transformation Types Format revision Decoding of fields Calculated and derived values Splitting of single fields Merging of information Character set conversion Unit of measurement conversion Date/Time conversion Summarization Key restructuring Duplication
10.Ahsan Abdullah 10 Format revision Decoding of fields Calculated and derived values Splitting of single fields Covered in issues Covered in De-Norm ONLY yellow part will go to Graphics Major Transformation Types
11.Ahsan Abdullah 11 Merging of information Character set conversion Unit of measurement conversion Date/Time conversion Not really means combining columns to create one column. Info for product coming from different sources merging it into single entity. ONLY yellow part will go to Graphics For PC architecture converting legacy EBCIDIC to ASCII For companies with global branches Km vs. mile or lb vs Kg November 14, 2005 as 11/14/2005 in US and 14/11/2005 in the British format. This date may be standardized to be written as 14 NOV 2005. Major Transformation Types
12.Ahsan Abdullah 12 Aggregation & Summarization How they are different? Why both are required? Grain mismatch (don’t require, don’t have space) Data Marts requiring low detail Detail losing its utility Adding like values Summarization with calculation across business dimension is aggregation. Example Monthly compensation = monthly sale + bonus ONLY yellow part will go to Graphics Major Transformation Types
13.Ahsan Abdullah 13 Key restructuring (inherent meaning at source) i.e. 92424979234 changed to 12345678 Removing duplication ONLY yellow part will go to Graphics Incorrect or missing value Inconsistent naming convention ONE vs 1 Incomplete information Physically moved, but address not changed Misspelling or falsification of names Major Transformation Types
14.Ahsan Abdullah 14 Data content defects Domain value redundancy Non-standard data formats Non-atomic data values Multipurpose data fields Embedded meanings Inconsistent data values Data quality contamination
15.Ahsan Abdullah 15 Domain value redundancy Unit of Measure Dozen, Doz., Dz., 12 Non-standard data formats Phone Numbers 1234567 or 123.456.7 Non-atomic data fields Name & Addresses Dr. Hameed Khan, PhD ONLY yellow part will go to Graphics Data content defects Examples
16.Ahsan Abdullah 16 Embedded Meanings RC, AP, RJ received, approved, rejected Data content defects Examples

Latest Release