Test Driven Development in Business Intelligence. Test-driven development (TDD) is a agile software development process which aims to improve software quality by focusing on early and regular testing. As my final comment, to get the largest benefit out of unit testing and to ensure a robust testing process, all tests should be setup. Testing in ETL, Data-Centric Projects. To intelligently design ETL processes. Once the common types objects are understood, reusable templates for ETL can be developed, regardless of business logic. It will give the number of reasons the why your assumptions may be wrong. It’s like unit testing source data even without. A test plan is a detailed document that outlines the test strategy. Unit Testing B) API Testing C) Integration Testing. Download a sample Test Plan Template.
- What Is Etl Testing Process
- Etl Testing Tutorial
- Unit Testing Template For Etl Listing
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- Testing Template For Electronic Health Record
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- Unit Testing Template For Etl Listing Lookup
- Estimation of ETL effort is not always a fun and so is with any estimation. The most popular way of estimating the effort needed to complete the job are Work Breakdown Structure (WBS) and Function Point Analysis (FPA).But you need to have a good understanding of things like source, target, resources on project, etc. It is a good place to start.
- SSIS Tester is a testing framework built on top of SQL Server Integration Services. It enables you to test packages, tasks and precedence constraints. It supports two types of tests: 1) unit and 2) integration tests. SSIS Tester helps you to develop you ETL process in the test driven manner and to identify errors early in the development process.
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Can anyone tell me the basic test scenarios and checklist for ETL process testing for beginner level with any example ? Download xbox one controller driver for windows 7.
Niels van Reijmersdal
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AnandAnand
3 Answers
ETL Testing is one of the scenarios where the testing is straightforward but the coding is complicated. As an overview, you should be looking to test each of the elements: Extract, Transform and Load individually and then all of them again as an integrated process.
In the systems I've tested in the past, failures would be flagged and stored, allowing a manual operator to intervene and do what ever needed doing to make the data right. Your system may not need this fallback option but I would suggest that there's some thought given to it.
From a testing perspective, this appears to be the simplest step. You need to prove that the target data can be extracted and stored. You need to know what data is available and then prove that, after the extraction process, you have all the data you expect.
Things to consider:
- Do you need all the data or just a subset of the fields?
- Do you need all the data or just a subset of the records?
- What format should the extracted data be in? (Windows, Unix. that sort of thing)
- Will the new repository have enough storage capacity?
- Will the new repository be accessible to the Transformation process?
- What happens if data doesn't extract correctly?
- If the extraction is automated and runs daily, what happens over a weekend or a long bank holiday if there's a problem? Does it queue up, is it time stamped?
- How is the environment kept clean? What happens to stale extracted data?
- Does the extraction process handle unexpected / invalid data?
Transformation Best free church projector software download.
This is the bit that everyone focuses on but is usually the bit that's the simplest (as in most traditional) in that it involves a tester proving that some code does what it should. As such, you can expect the defect cycle (finding and fixing) to be the most predictable. Portable hdd scan and repair.
Testing Transformation is all about getting the right data. Understand what each of the transformation rules are and what data boundaries will affect it. I usually spend a great deal of time planning and refining my data requirements and thrashing through the transformation rules with the Developers and Business Owner.
I would isolate each transformation rule and test it in isolation, then identify any transformation rules that overlap and test each overlapping area in isolation.
File sync utility for mac os x. Things to consider:
- What does each transformation rule do?
- Is each rule doing the right thing?
- What data does it affect?
- What format of input data is expected?
- What format of output data should be generated?
- What are the expected real life data boundaries?
- How much data is the transformation process expected to handle in a given time period?
- What happens if one or more transformation rule fails?
- What happens if the data extract contains invalid data?
Load
Much like the Extraction phase, this seems simple on paper. All you're doing is loading data into a new system. You need to know what data you have and then how it looks in the final respository after the upload process.
To do this, of course, you need to be able to interrogate the final respository directly, avoiding any additional 'Extract' stages that could muddy the water.
Things to consider:
- When you query the data, are you seeing what's -actually- there or something that's been packaged by another process? (which would be bad)
- What format of data does the upload process require?
- How quickly does it need to upload the data?
- What happens if the upload fails?
- What happens if the same data is uploaded multiple times?
- What happens if invalid data is uploaded?
- Bearing in mind that the data should be in its permanent home, what is the performance of the system like?
- Will it be able to expand to handle future data loads?
Integration
By this point, you're confident that all the individual -technical- elements are working but now you have to test the business processes that make it all work.
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- How often does the end to end process happen?
- Do the right tasks happen in the right order?
- Is there enough time available for each process to complete before the next one kicks off?
- What happens if one or more of the phases fails?
- How is the automated process monitored to ensure that everything is working?
- Over time, the acceptable input data criteria may change. What contingencies are in place to ensure that today's system will still be working in 5 years time?
That's a starter for 10. I hope it gives you some ideas.
Niels van Reijmersdal
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Dave MDave M
Please find below the list of ETL testing scenarios for beginners:
Validation of Mapping Document
A mapping document serves as a requirement specification document for the ETL testers. Testers should verify that the mapping document has all the required information which is as follows:-• Source Database information• Transformation rules• Target Database information• Change log
Tape reading 101 course download game. Validation of database Schema
Schema validation includes comparison of database schema against the mapping document. This test ensures that there is low probability of ETL process failures because of data type mismatch between the processed data and the tables that are designed to hold this data.Schema check includes the following:-• Name of tables • Number of columns• Name of columns• Data types• Lengths of data types
Validation of constraints
Mapping document contains the details related to the database constraints. Testing is performed to ensure that the constraints are defined for the tables as mentioned in the mapping document. For example, is the column “Nullable” or “Not Nullable”
Record count validation
This is one of the very basic but important tests while validating data. This test ensures that records in source and target tables are as expected. Usually the number of records remains the same, but there are cases when the number of records can increase or decrease depending on the requirement.When the number of records remain same, it is known as a “Passive Transformation”. On the other hand, when the number of records change, it is known as an “Active Transformation”.
Validation of correctness of dataThis is a very important test to ensure the integrity of data. There are different business rules that impact the data as it moves from source to data warehouse. So, it is important to ensure that no data is lost during processing. This can be done by designing test cases around this requirement and analyzing some individual records manually.
Validation of transformation logic
From BI stand point, transformation logic plays a vital role in converting the data into meaning information for the end users to analyze.For every table, all the business rules are stated in the mapping document. And testers need to be very cautious while validating the transformation logic. Incorrect implementation of transformation logic can result in incorrect data after processing; ultimately providing misleading information to the end users.So, it is of utmost importance for testers to ensure that the business transformations have taken place correctly from source to target as per the mapping document. And no transformation rule should be left unchecked. For instance, if the source does not have data to test some specific scenario then data must be simulated in testing environment in order to test such scenario.
Validation of data for duplication
On many occasions, it has been observed that there are some common transformation rules that process the data in one particular way even for different inputs from different sources. In some other cases, source data comprises of data from multiple columns and is processed and populated in a single column in the target; after processing.In such cases, there are high chances of duplication of data in the data warehouse tables. However, these are just examples as there can be multiple reasons that can contribute to duplication of data.Duplicate data is redundant in nature and does not bring any value. Quicktime pro 7 for mac download. Moreover, it can adversely impact the performance of data operations being performed on tables (that contain too many duplicates).So, ETL testers should always include tests that can uncover duplicate data in data warehouse tables as it moves from source to target. Here, testers should check for the unique keys, primary keys.
AalokAalok
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I got some information in web for ETL testing as below points, Is it useful for me .Awaiting for your suggestion and feedback
Verify data is mapped correctly from source to target systemVerify all tables and their fields are copied from source to targetVerify keys configured to be auto-generated are created properly in target systemVerify that null fields are not populatedVerify data is neither garbled nor truncatedVerify data type and format in target system is as expectedVerify there is no duplicity of data in the target systemVerify transformations are applied correctlyVerify that the precision of data in numeric fields is accurateVerify exception handling is robust
Reconciliation check- record count between the STG (staging) tables and target tables are same after applying filter rulesInsert a record which is not loaded into target table for given key combinationCopy records, sending same records that are already loaded into target tables-should not be loadedUpdate a record for a key when value columns changed on day_02 loadsDelete the records logically in the target tablesValues loaded by process tablesValues loaded by reference tables
Check if the target and source data base are connected well and there are no access issues.For a full load, check the truncate option and ensure its working fine.While loading the data, check for the performance of the sessionCheck for non-fatal errors.Verify you can fail the calling parent task if the child task fails.Verify that the logs are updatedVerify mapping and workflow parameters are configured accuratelyVerify the number of tables in source and target systems is the sameCompare the attributes from stage tables to that of the target tables. They should be matched.
Display date and timeDecimal precision for key figuresIn a given page display the number of rows and columnsFree characteristics in the reportHow are blank values/data displayed for both characteristics and key figures in the reportWhether search for characteristics is based on key or key&text as applicableDoes search option on text is case sensitive- Upper, Lower or both
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Testing is an investigation processthat is conducted to check the quality of the product. Product can either be anapplications or data. The quality of the data can only be determined bychecking data against some existing standards by following a set of processes.By doing so, you find out the symptoms in form of invalid/incorrect data thathappened because of erroneous processes. So the>
- Application Testing: The focus of this article is>
- ETL Processes/Data Movement: When you apply ETL processes on source database, and transform and load data in the target database
- System Migration/Upgrade: When you migrate your database from one database to another or you upgrade an existing system where the database is currently running.
- Data-Centric Testing
The> - Technical Testing: Technical testing ensures that the data is moved, copied, or loaded from the source system to target system correctly and completely. Technical testing is performed by comparing the target data against the source data. Following is a list of functions that can be performed under technical testing:
-
- Checksum Comparison: The>
- Reconciliation: Reconciliation ensures that the data in the target system is in agreement with the overall system requirements. Following are the couple of examples of how the reconciliation helps in achieving high quality data:
-
- Internal reconciliation: In this type of reconciliation, the data is compared within the system against the corresponding data set. For example shipping would also always be less than or equal to the orders. If the shipping ever exceeds the orders then it means data is invalid.
- External reconciliation: In this type of reconciliation, data in system is compared against its counterpart in other systems. For example, in a module or in an application, number of employees can never be more than the number of employees in HR Employee database. Because HR Employee database is the master database that keeps record of all the employees. If such a situation occurs where the number of employees anywhere in the system is more than the HR Employee database, then the data is invalid.
My company developed the ICE± product,which targets to accomplish all types of testing mentioned in this article. Formore information, visit www.iCEdq.comOracle recycle bin flashback table featureWhen you do ‘Select * from tab;’ sometimes, you will be surprised to see some tables with garbage names. Welcome tothe world of Oracle Recycle Bin Feature. Because of this feature, Oracle savesthe dropped table in recycle bins until you clear it.
1. Empty recycle bin use the command:
PURGE RECYCLEBIN;
2. To drop the table without storing the table in recycle bin use:
DROP TABLE employee PURGE;
3. To restore the table from the recycle bin use:
FLASHBACK TABLE employee TO BEFORE DROP;
So don’t forget to clean your database /Schema once in a while.
Here’s your chance to laugh at them.
If you read this article, probably you will end up knowing something more thanthem. This is not because you will find the definition of type II dimension,but for an entirely different reason.
To be continued…
To clearly explain the pitfallsof Type II dimension, let’s take an example. In the example, there are threetables, that is, DIM_Instrument, FACT_Trade, and FACT_Settlement. Each of thetables contains data as shown below:
DIM_Instrument table
In the Dim_Instrument table, theproperty of instrument IBM changes from X to Y. So to maintain type IIdimensions, a new entry is added in the table by updating the status of currententry to obsolete (denoted by ‘O’) and adding date in the TODT column as well.In the new entry, the ToDT column is NULL and the status is current (denoted by‘C’).
FACT_Trade table
The trade table containsinformation about just one trade and that was executed on April 29th, 2011, which means itwas processed for InstrumentKey ‘1’ as Instrumentkey ‘2’ did not exist on April 29th, 2011.
FACT_Settlement table
Generally, it takes three daysfor a trade to settle. So the trade that was executed on 29th April 2011, gotsettled only on 2-MAY-2011. During this period the property of instrument ‘IBM’changed on May 1st 2011, a new entry was made in the DIM_Instrument table forInstrument IBM, which incremented the instrumentKey to 2.
Now in the settlement table, theinstrument key against the same trade is different, which can cause theissues/concerns that are described using various scenarios.
Scenario 1:
Get data for each trade and settlement for the current instrument, using thefollowing query:
SELECTT.INSTRUMENTKEY, T.TradeDate as Sec_Date
From
DIM_INSTRUMENT I, FACT_TRADE T
WHERE
T.InstrumentKey=I.InstrumentKey
and I.Status=’C’
UNION ALL
SELECT S.INSTRUMENTKEY, S.SettlementDt asSec_Date
From
DIM_INSTRUMENT I, FACT_SETTLEMENT S
WHERE
S.InstrumentKey=I.InstrumentKey
and I.Status=’C';
The output does not show any datafrom Fact_Trade table.Because the current record in Dim_instrument is with InstrumentKey as ’2′ and the Fact_Trade table does not contain InstrumentKey ’2′.
It happens because theinstrumentkey is changed between trade execution and trade settlement. Though,the settlement is done against the trade processed on April 29th, 2011 but withthe change in InstrumentKey in between, there is no way to retrieve it.
Scenario 2:
Rather than querying data forcurrent instrument, get data for all the instrument records. You can do so byusing the following query:
SELECTT.INSTRUMENTKEY, T.TradeDate as Sec_Date
From
DIM_INSTRUMENT I, FACT_TRADE T
WHERE
T.InstrumentKey=I.InstrumentKey
UNION ALL
SELECT S.INSTRUMENTKEY, S.SettlementDt asSec_Date
From
DIM_INSTRUMENT I, FACT_SETTLEMENT S
WHERE
S.InstrumentKey=I.InstrumentKey
The above query returns thefollowing output:
If you analyze the outputclosely, it returns the trade information for InstrumentKey ’1′ but no settlement informationfor InstrumentKey ’1′. Similarly for InstrumentKey’2′ there is no trade information but the settlement information.This output can be best viewed as the following:
For an instrument when tradeexists, settlement does not exist and vice versa.
Scenario 3:
Maintain a relationship between two fact tables by maintaining a common key(i.e. TradeID) and join both the tables based on this common key. In this case,the TradeID uniquely identifies the trade and settlement data that refer to thesame trade.
But this scenario results in fact-to-fact relationship, which is notrecommended because of the following reasons:
- Fact tables being the largest tables adversely impact the performance of the join.
- It is not a practical scenario when multiple fact tables need to be joined because doing so complicates the design and is not always possible to achieve.
Posted in Data Architecture, DataModeling, Realtime ETL
7 Responsesto “Pitfalls of type II dimension”
June 16, 2011 at3:27 pmThe problemmentioned is very real life. Even I faced this issue and I have a woork aroundfor that. It is should be taken care in the data model. Add onePARENT_INSTRUMENT_KEY attribute in the DIM_INSTRUMENT table to track that both1 and 2 are under the PARENT_INSTRUMENT_KEY. For eg. 1, A1; 2, A1… N, AN.June 25, 2011 at3:36 pmFriend canyou explain what is Type II dimension? When we talk about type II dimension itis a concept woven around the with the help of tools. As Sourav mentioned itcan be corrected at data model.Problem umentioned is clearly data model issue not concepts. What do u say?July 1, 2011 at1:57 pmSai, I’mglad you mentioned this. If we look carefully, this article tried to bring intonotice the pitfalls of Type II dimensions. As mentioned earlier, type IIrequires the primary key change for the same identity to maintain the history.Once the primary key changes, we can very well imagine what kind of results itcan produce.It is notunusual for people to use ‘Reference Data’ and ‘Master Data’interchangeably without understanding the differences.
As far as the solution is concerned, it can be implemented the way you want. Itshould not be assumed that there is no solution to the issues reported here. Infact, the solution for this has to be implemented at the Data Model level.
Sometimes the problem and the solution are so closely woven that we prefer notto look at them separately.
Lets try to understand the differences with an example of sales transaction.
A sales transaction containsinformation like….
Store,
Products Sold,
Sales Person,
Store Name,
Sales Date,
Customer,
Price,
Quantity,
etc.
Attributes from the above examplecan be separated into two types: Factual (transactional)and Dimensionalinformation
Price and Quantity are measurable attributes of a transaction.
Store, Products Sold, Sales Person, Store Name, Sales Date, and Customer aredimensional attributes of a transaction.
We can see that the dimensionaldata is already embedded in the transaction. And with dimensional attributes wecan successfully complete the transaction.Dimensional data that directly participates in a transaction ismaster data.
But is thelist of dimensional attributes in the transaction complete?
Asking few analytical questions can help us discover the answer.
-What is the Male to Female ratio of customersdoing purchase at the store?
-What type of products are customers buying? Ex:Electronic, Computers, Toys
-What type of Store is it? Ex: Web store, Brick& Mortar, Telesales, Catalog Sales
The above questions cannot beanswered by attributes in the transaction. These dimensional data is missing inthe transactions. This missingdimensional data that does not directly participate in transaction but areattributes of the dimension is reference data.
Why it is important for an ETL person to understand the differences? Well oncethe ‘Reference Data Management’ (RDM) was popular then suddenly in lastfew years there is this new word ‘Master Data Management’ (MDM). These wordsmean different things and they have significant implication on how they aremanaged. But that will be a topic of discussion for some futurepost! I hope this article will help clear atleast someconfusion.
Loading & testing fact/transactional/balances(data), which is valid between dates!This is going to be a very interestingtopic for ETL & Data modelers who design processes/tables to load fact ortransactional data which keeps on changing between dates. ex: prices of shares,Company ratings, etc.The table above shows an entity in thesource system that contains time variant values but they don’t change daily.The values are valid over a period of time; then they change.1 .What the tablestructure should be used in the data warehouse?Maybe Ralph Kimball or Bill Inmon cancome with better data model! But for ETL developersor ETL leads the decision is already made so lets look for a solution.2. What should be theETL design to load such a structure?- There is one to one relationship between the source row and the target row.
- There is a CURRENT_FLAG attribute, that means every time the ETL process get a new value it has add a new row with current flag and go to the previous row and retire it. Now this step is a very costly ETL step it will slow down the ETL process.
- From the report writer issue this model is a major challange to use. Because what if the report wants a rate which is not current. Imagine the complex query.
Design B- In this design the sanpshot of the source table is taken every day.
- The ETL is very easy. But can you imagine the size of fact table when the source which has more than 1 million rows in the source table. (1 million x 365 days = ? rows per year). And what if the change in values are in hours or minutes?
- But you have a very happy user who can write SQL reports very easily.
- Can there be a comprimise. How about using from date (time) – to date (time)! The report write can simply provide a date (time) and the straight SQL can return a value/row that was valid at that moment.
- However the ETL is indeed complex as the A model. Because while the current row will be from current date to- infinity. The previous row has to be retired to from date to todays date -1.
- This kind of ETL coding also creates lots of testing issues as you want to make sure that for nay given date and time only one instance of the row exists (for the primary key).
Which design is better, I have used alldepending on the situtation.There are various cases where the ETLcan miss and when planning for test cases and your plan should be to preciselytest those. Here are some examples of test plansa. There should be only one value for agiven date/date timeb. During the initial load when thedata is available for multiple days the process should go sequential and createsnapshots/ranges correctly.c. At any given time there should beonly one current row.NOTE: This post is applicable to alletl tools or databases like Informatica, DataStage, Syncsort DMExpress,Sunopsis or Oracle, Sybase, SQL Server Integration Services (SSIS)/DTS, AbInitio, MS SQL Server, RDB, etcIt is a normal practice in datawarehouse to de normalizes (Or once auto corrected as demoralize) as the data modelfor performance. I am not going to discuss the benefits vs. issues withde-normalization. As by the time it comes to the ETL guy the fate of themodel is already decided.
Let’s look at the model in thesource side, which is perfectly normalized.
Now let’s look at the denormalized model on the target side.
Next lets think of delta logicfor loading of the dim_employee table. Ideally you would only check changes inthe employee table. Then if there is any changes after the last load date time; then get those rows from ref_employee and do the lookup to get the department& the designation and load it into the target table.
The issue with this delta logic is that it has not considered the effect of denormalization of employee table on the target side. If you carefully look atthe two de normalized attributes dept_name and emp_designation_desc, the ETLprocess will miss any changes in the parent tables, so only new employees orupdated employee will get the new definition of department & designation.And any employee that has not been updated in the source side will still havethe same dept_name & emp_designation_desc. This is wrong.
The reason it is wrong is the ETLdelta logic only picked the row from the employee table when it changed andignored the changes in the dept & designation tables. The truth of thematter is, ” For any de normalized target table data (affected rows) should bere-captured from the source, any time there is change in the driving/core tableas well as when there is change in any parent tables to which the driving tablerefers to.” In this case, even if there is change in department or designationtable, all the rows affected on the employee tables should be re-processed.
It might seem very simple, butETL developers/designers/modelers always miss this point. Also once developedit is very difficult to catch.
The next question is how youwould catch the affected rows. Well there are ways to write SQL that combinethe three tables (in this case) and treat them as one single entity and thepull rows based on the any update_dttm greater than the last ETL run. Figureout the SQL…
1. Reference data
2. Dimensional data (master data)
3. Transactional data
4. Transactions
5. Balances
6. Summary/Aggregations
7. Snapshots
8. Staging
9. Out triggers/mini dimensions
10. Log tables
11. Meta data tables
12. Security tables
13. Configuration tables
Programmatic control is lost whenidentity columns are used in Sybase and SQL Server. I do not recommend usingIdentity columns to create surrogate keys during ETL process. There are manymore reasons for that. Oracle has the sequence feature which is usedextensively by Oracle programmers. I have no clue why other vendors are notproviding the same. This custom code has been used extensively by me andthoroughly tested. I ran multiple processes simultaneously to check if there isdeadlock and also made sure that the process returns different sequences todifferent client process.
Notes: -
1. The table should have ‘ROW LEVEL LOCKING’
2. The sequence generator process is stateless (See more details in ObjectOriented Programming)
3. Create one row for each target table in the sequence master table. Do nottry to use one sequence for multiple tables. It will work but probably is not agood idea.
Step 1:-Create a table with following structure.
CREATE TABLEsequence_master (
sequence_nm varchar (55) NOT NULL ,
sequence_num integer NOT NULL
)
GO
Step 2:-Create a stored procedure that will return the next sequence.
CREATE PROCEDURE p_get_next_sequence
@sequence_name varchar(100)
AS
BEGIN
DECLARE @sequence_num INTEGER
— Returns an error if sequence row is enteredinto the table.
SET @sequence_num = -1
UPDATEsequence_master
SET @sequence_num = sequence_num =sequence_num + 1
WHERE Sequence_name = @sequence_name
RETURN@sequence_num
END
GO
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 resultset.
Answer: 2. I get multiple result set.
If you go back to the original articleon What is ETL & What ETL is not! You will immediately come to theconclusion that Answer 2 is incorrect, as ETL is not allowed to create data.
Why will the process run morethan once against the same set of data? Many reasons, example most common beingoperators mistake, accidental kickoff, old set of data file remaining in thedirectory, staging table loaded more than once, intentional rerun of ETLprocess after correction of some data in source data set, etc. Without goinginto further details, I would advise ETL folks to always include in yourprocess ways to prevent it from happening by one or more combinations offollowing methods…
1. Identify the primary key (logical/physical) and put update else insertlogic.
2. Deleting the target data set before processing again (based onlogical/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 theseexperiments in the development or test environment run the ETL process morethan once, check the result! If you get the result 2 (copies of rows, with no way to distinguishor retiring the old rows)
The designer or the developer is wrong & if the process as passed the QA ortesting then the tester is wrong.
Bottom line:
A test caseto check multiple runs is must in life cycle of an ETL process.
This could be long topic ofdiscussion. Following are the main issues I would like to discuss onstaging table /database design.
1. Whystaging area is needed?
Unlike OLTP systems that createtheir own data through an user interface data warehouses source their data fromother systems. There is physical data movement from source database to datawarehouse database. Staging area is primarily designed to serve as intermediateresting place for data before it is processed and integrated into the targetdata warehouse. This staging are serves many purpose above and beyond theprimary function
a. The data is most consistent with the source. It is devoid of anytransformation or has only minor format changes.
b. The staging area in a relation database can be read/ scanned/ queried usingSQL without the need of logging into the source system or reading files(text/xml/binary).
c. It is a prime location for validating data quality from source or auditingand tracking down data issues.
d. Staging area acts as a repository for historical data if not truncated
e. Etc.
2. What isthe difference between staging area as compared to other areas of datawarehouse?
a. Normally tables in anyrelational database are relational. Normally tables are not stand alone. Tableshave relationship with at least one or more tables. But the staging areagreatly differs in this aspect. The tables are random in nature. They are morebatch oriented. They are staged in the hope that in the next phase of loadthere will be a process that will identify the relationship with other tablesand during such load a relationship will be established.
3. Whatshould the staging table look like?
a. The key shown is a meaninglesssurrogate key but still it has been added the reason being; as may times thedata coming from a source has no unique identifier or some times the uniqueidentifier is a composite key; in such cases when data issue is found with anyof the row it is very difficult to identify the particular row or even mentionit. When a unique row num is assigned to each row in the staging table itbecomes really easy to reference it.
b. Various dates have added tothe table; please refer date discussion here.
c. The data type has been kept asstring because this data type ensures that a bad format or wrong data type rowwill be at least populated in the stage table for further analysis orfollow-up.
d. Source system column has beeadded to keep a data reference so that next process step can use this value andcan have dynamic behavior during process based on the source system. Also itsupports reuse of table, data partitioning etc.
e. Note the table has source astable qualifier as prefix this distinguishes the table from other sourcesystem. Example customer from another system called MKT.
d. Other columns can be addedexample processed flag to indicate if the row has been processed by the downstream application. It also provides incremental restart abilities for the downstream process. Also exception flag can be added to the table to indicatethat while processing the table an exception or error was raised hence the rowis not processed.
4. Which designto choose?
a. Should the table be truncatedand loaded?
b. Should the table will be append only?
c. Should default data type to be left as alpha numeric string (VARCHAR)?
d. Should constraints be enforced?
e. Should there be a primary Key?
It is normally based on thesituation but if not sure or you don’t want to think then design suggested hereshould more than suffice your requirement
Sometimes an ETL process runsconsiderably slow speed. During test for the small result set it might fly butwhen a million rows are applied the performance takes a nosedive. There can bemany reasons for slow ETL process. The process can be slow because read,transformation, load. Lets eliminate the transformation and load for the sakeof discussion.
For ETL process to be slow onread side here are some reasons .1. No indexes on joins and/or ‘where clause’2. Query badly written. 3. Source not analyzed. Out of these three letsrule out 1 & 2.
In the past most of the databases had RULE based optimizer in the INIT.ORAfile, but with new development and specially Data warehouses ‘CHOOSE’ optimizeris preferred. With ‘CHOOSE’ option the query uses COST based optimizer if thestatistics are available for the tables in the query.
There are two methods to gatherstatistics 1. DBMS_STATS package, 2. ANALYZE command. Oracle does not recommendANALYZE command going forward for various reasons (its a command, cannotanalyze external tables, it gathers stats that are not essential, inaccuratestats on partitioned tables and indexes, in future ANALYZE will not supportcost-based optimizer, no Monitoring for stale statistics, etc.)
DBMS_STATS.GATHER_SCHEMA_STATS(ownname =>’DWH’, options =>’GATHER AUTO’);
This Package will gather all necessary statistics automatically (there is noneed to write a process to check for tables that have stale or no stats).Oracle implicitly determines which objects need new statistics, and determineshow to gather those statistics. So once it is put in the ETL flow you can sleepat home and everybody will be happy.
Bottom Line:
After major data load the tables should be analyzed with an automated process.Also please do not use ANALYZE , as in future it will notcollect statistics needed for COB. The code can be found here.
This package should be used as the part of ETL workflow tomake sure as batch processes are run the benefit of statistics are available tothe next process in the que.
Also ETL programmers can callthis package within the ETL process/procedure in the begening so that if thetables required for process are not analyzed they will be analyzedautomatically. Also if the procedure is going to modify lot of data inthe table then the package can be called just before the end of theprocedure.
A database can have multiplesources. Multiple sources may contain a data set of entirely different subjectareas, but some data set will intersect. Example Sales data and salary datawill have employee as the common set.
Between two or more sources, thesubjects, entities or even attributes can be common. So can we integrate thedata easily? Mathematically it seems very easy, but the real world is not justabout numbers or exact same string values. Every thing can be similar or samebut may be not represented in the exact same manner in all the sources. Thedifferences in representation of same information and facts between two or moresources can create some of the most interesting challenges in Data Integration.
Dataintegration: -The first step in dataintegration is identification of common elements
1. Identify the common entities.
Example, Employee dimension can come from Sales system, Payroll System, etc.Products can come from manufacturing, sales, purchase etc. Once the commonentity is identified its definition should be standardized. Example, doesemployee include fulltime employees as well as temporary workers?
2. Identify the common attributes.
What are the attributes that are common to employee, 1st name, 2nd name, lastname, date of joining, etc? Each attribute should be defined.
3. Identify the common values
Same information can be represented in different forms in multiple sourcesystem. Example, male sex, can be represented as ‘M’ or ‘1′ or ‘male’ or some thing else by each source system. A commonrepresentation must be decided (example, ‘Male’). Also if necessary afinite set of values should be established. Example the employee sex =(‘Male’,’ Female’) and it will not allow more than these two values.
The second step is theidentification of Data Steward who will own the responsibility and ownershipfor particular set data elements.
The third step is to design anETL process to integrate the data into the target. This is the mostimportant area in the implementation of an ETL process for data integration.This topic will be discussed in more detailed under its own heading.
The final fourth step is toestablish a process of maintenance, review & reporting of such elements.
To implement an ETL process thereare many steps that are followed. One such step is creating a mapping document.This mapping document describes the data mapping between the source systems andthe target and the rules of data transformation.
Ex. Table / column map betweensource and target, rules to identify unique rows, not null attributes, uniquevalues, and range of a attributes, transformations rules, etc.
Without going into further details of the document, lets analyze the very nextstep. It seems obvious and natural to start development of the of the ETLprocess. The ETL developer is all fired up and comes up with a design documentand starts developing, few days time the code is ready for data loading.
But unexpectedly (?) the code starts having issues every few days. Issues arefound and fixed. And then it fails again. What’s happening? Analysis was doneproperly; rules were chalked out & implemented according to the mappingdocument. But why are issues popping up? Was something missed?
Maybe not! Isn’t it, normal to have more issues in the initial lifetime of theprocesses?
Maybe Yes! You have surely missed ‘Source System Data Profiling’. The businessanalyst has told you rules as the how the data is structured in the sourcesystem and how it is supposed to behave; but he/she has not told you the ‘butsand ifs’ called as EXCEPTIONS for those rules.
To be realistic it is not possible for anyone to just read you all rules andexceptions like a parrot. You have to collaborate and dig the truth. The actualchoice is yours, to do data profiling on the source system and try to break allthe rules told by the analyst. Or you can choose to wait for the process to golive and then wakeup every night as the load fails. If you are luckyenough you deal with an unhappy user every morning you go to the office.
Make the right choice; don’t miss ‘Source system data profiling’ beforeactually righting a single line of code. Question every rule. Try to findexception to the rules. There must be at least 20 tables. One table on anaverage 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 givethe number of reasons the why your assumptions may be wrong. It’slike unit testing source data even without loading. There is a reason whymachines 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 aremore exceptions…’
Every time there is movement ofdata the results have to be tested against the expected results. For every ETLprocess, test conditions for testing data are defined before/during design anddevelopment phase itself. Some that are missed can be added later on.
Various test conditions are usedto validate data when the ETL process is migrated fromDEV-to->QA-to->PRD. These test conditions are can exists in thedeveloper’s/tester’s mind /documented in word or excel. With time the testconditions either lost ignored or scattered all around to be really useful.
In production if the ETL processruns successfully without error is a good thing. But it does not really meananything. You still need rules to validate data processed by ETL. Atthis point you need data validation rules again!
A better ETL strategy is to storethe ETL business rules in a RULES table by target table, source system. Theserules can be in SQL text. This will create a repository of all the rules in asingle location which can be called by any ETL process/ auditor at any phase ofthe project life cycle.
There is also no need to re-write/rethink rules. Any or all of these rules can be made optional, tolerances canbe defined, called immediately after the process is run or data can be auditedat leisure.
This Datavalidation /auditing system will basically contain
A table thatcontains the rules,
A process to call is dynamically and
A table to store the results from the execution of the rules
Benefits:
Rules can be added dynamicallywith no cange to code.
Rules are stored permanantly.
Tolerance level can be changedwith ever changing the code
Biz rules can be added orvalidated by business experts without worring about the ETL code.
ETL is all about transportation,transformation and organizing of data. Of anytime something moves (as a matterof fact even if you are perfectly stationary and items around moves) accidentsare bound to happen. So any ETL specialist believes that their code is perfectand nothing can happen obviously lives in a fool’s paradise.
The next obvious thing is todesign to manage accidents, like making a safer car or a factory. And as an ETLspecialist if you don’t do it you are no different then others. As in anycountry there are laws for accidents and accident due to criminal negligence.Later being the worst.
How many times I have seen peopleputting ETL code into production without actually designing processes toprevent, manage or report accidents. Writing code is one thing writingproduction worthy code is another. Do ask yourself or your developers, “Is thecode production worthy?”
Next
ERRORS:
A programmatic error that causesthe the program to fail or makes the program run for uncontrolled time frame.
EXCEPTIONS: A program/code written to handle expected or unexpected errorsgracefully so that the program continues run with logging the error andbypassing the erroneous conditions or even logging the error and gracefullyexiting with error message.
More detailed description willcome with topic…. ‘Unhandled exceptions results in Errors’.
Note: The topic on error andexceptions is relevant to Informatica, Data Stage, Abinitio, Oracle warehousebuilder, PLSQL, SQLLDR, Transact SQL or any ETL other tools.
An attribute with a context is ameaningless attribute. (Even if it has a definition associated with it).
One of the interesting phases in ETLis the data mapping exercise. By the time section.For example, if you have mentioned that you will be testing the existing interfaces, whatwould be the procedures you would follow to notify the key people to represent theirrespective areas, as well as allotting time in their schedule for assisting you in4.0 TESTING STRATEGYDescribe the overall approach to testing. For each major group of features or featurecombinations, specify the approach which will ensure that these feature groups areadequately tested. Specify the major activities, techniques, and tools which are used toThe approach should be described in sufficient detail to permit identification of the majortesting tasks and estimation of the time required to do each one.Definition:Specify the minimum degree of comprehensiveness desired. Identify the techniqueswhich will be used to judge the comprehensiveness of the testing effort (for example,determining which statements have been executed at least once). Specify any additionalcompletion criteria (for example, error frequency). The techniques to be used to traceParticipants:What Is Etl Testing Process
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