Archive for the ‘ETL Exception & Error Handling’ Category

For every rule there is an exception; for each exception there are more exceptions…

Sunday, May 13th, 2007

To implement an ETL process there are many steps that are followed. One such step is creating a mapping document. This mapping document describes the data mapping between the source systems and the target and the rules of data transformation.

Ex. Table / column map between source and target, rules to identify unique rows, not null attributes, unique values, and range of a attributes, transformations rules, etc.

Without going into further details of the document, lets analyze the very next step. It seems obvious and natural to start development of the of the ETL process. The ETL developer is all fired up and comes up with a design document and starts developing, few days time the code is ready for data loading.
But unexpectedly (?) the code starts having issues every few days. Issues are found and fixed. And then it fails again. What’s happening? Analysis was done properly; rules were chalked out & implemented according to the mapping document. But why are issues popping up?  Was something missed?
Maybe not! Isn’t it, normal to have more issues in the initial lifetime of the processes?
Maybe Yes! You have surely missed ‘Source System Data Profiling’. The business analyst has told you rules as the how the data is structured in the source system and how it is supposed to behave; but he/she has not told you the ‘buts and ifs’ called as EXCEPTIONS for those rules.
To be realistic it is not possible for anyone to just read you all rules and exceptions like a parrot. You have to collaborate and dig the truth. The actual choice is yours, to do data profiling on the source system and try to break all the rules told by the analyst. Or you can choose to wait for the process to go live and then wakeup every night as the load fails.  If you are lucky enough you deal with an unhappy user every morning you go to the office. 
Make the right choice; don’t miss ‘Source system data profiling’ before actually righting a single line of code. Question every rule. Try to find exception to the rules. There must be at least 20 tables. One table on an average will have 30 columns; each column will have on an average 100k values. If you make matrix of number of tables * columns * data values, it will give the number of reasons the why your assumptions may be wrong.   It’s like unit testing source data even without loading. There is a reason why machines alone cannot do your job; there is reason why IT jobs are more paying.
Remember, ‘for every rule there is an exception; for each exception there are more exceptions…’

ETL Startegy to store data validation rules

Wednesday, September 6th, 2006

Every time there is movement of data the results have to be tested against the expected results. For every ETL process, test conditions for testing data are defined before/during design and development phase itself.  Some that are missed can be added later on.

Various test conditions are used to validate data when the ETL process is migrated from DEV-to->QA-to->PRD. These test conditions are can exists in the developer’s/tester’s mind /documented in word or excel. With time the test conditions either lost ignored or scattered all around to be really useful.

In production if the ETL process runs successfully without error is a good thing. But it does not really mean anything. You still need rules to validate data processed by ETL. At this point you need data validation rules again!

A better ETL strategy is to store the ETL business rules in a RULES table by target table, source system. These rules can be in SQL text. This will create a repository of all the rules in a single location which can be called by any ETL process/ auditor at any phase of the project life cycle.

There is also no need to re-write /rethink rules. Any or all of these rules can be made optional, tolerances can be defined, called immediately after the process is run or data can be audited at leisure.

This Data validation /auditing system will basically contain
A table that contains the rules,
A process to call is dynamically and
A table to store the results from the execution of the rules


Rules can be added dynamically with no cange to code.

Rules are stored permanantly.

Tolerance level can be changed with ever changing the code

Biz rules can be added or validated by business experts without worring about the ETL code.

NOTE: This post is applicable to all etl tools or databases like Informatica, DataStage, Syncsort DMExpress, Sunopsis  or Oracle, Sybase, SQL Server Integration Services (SSIS)/DTS, Ab Initio,   MS SQL Server, RDB, etc.

Introduction to Error and exception management.

Monday, July 3rd, 2006

ETL is all about transportation, transformation and organizing of data. Of anytime something moves (as a matter of fact even if you are perfectly stationary and items around moves) accidents are bound to happen. So any ETL specialist believes that their code is perfect and nothing can happen obviously lives in a fool’s paradise.

The next obvious thing is to design to manage accidents, like making a safer car or a factory. And as an ETL specialist if you don’t do it you are no different then others. As in any country there are laws for accidents and accident due to criminal negligence. Later being the worst.

How many times I have seen people putting ETL code into production without actually designing processes to prevent, manage or report accidents. Writing code is one thing writing production worthy code is another. Do ask yourself or your developers, “Is the code production worthy?”



A programmatic error that causes the the program to fail or makes the program run for uncontrolled time frame.
EXCEPTIONS: A program/code written to handle expected or unexpected errors gracefully so that the program continues run with logging the error and bypassing the erroneous conditions or even logging the error and gracefully exiting with error message.

More detailed description will come with topic…. ‘Unhandled exceptions results in Errors’.

Note: The topic on error and exceptions is relevant to Informatica, Data Stage, Abinitio, Oracle warehouse builder, PLSQL, SQLLDR, Transact SQL or any ETL other tools.

Multiple executions ETL process against same set of data.

Thursday, June 15th, 2006

Every ETL designer, developer & tester should always ask this question…”What will happen, if I run the ETL process multiple times, against the same data set?”

Answer: 1. I get the same result set.
Answer: 2. I get multiple result set.

If you go back to the original article on What is ETL & What ETL is not! You will immediately come to the conclusion that Answer 2 is incorrect, as ETL is not allowed to create data.

Why will the process run more than once against the same set of data? Many reasons, example most common being operators mistake, accidental kickoff, old set of data file remaining in the directory, staging table loaded more than once, intentional rerun of ETL process after correction of some data in source data set, etc. Without going into further details, I would advise ETL folks to always include in your process ways to prevent it from happening by one or more combinations of following methods…
1. Identify the primary key (logical/physical) and put update else insert logic.
2. Deleting the target data set before processing again (based on logical/physical primary key)
3. Preventing occurrences of multiple runs by flagging processed dates
4. Marking processed records with processed flags after commit
5. Prevention of multiple loads in the staging area
6. identifying duplicate records in stage area before the data gets processed
7. more…

So do these experiments in the development or test environment run the ETL process more than once, check the result! If you get the result 2 (copies of rows, with no way to distinguish or retiring the old rows)
The designer or the developer is wrong & if the process as passed the QA or testing then the tester is wrong.

Bottom line:
A test case to check multiple runs is must in life cycle of an ETL process.