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Why You Need ETL Testing

Why You Need ETL Testing and What You Need to Know

The Importance of Data

Data great is the important thing to the fulfillment of businesses. Bad facts ends in erroneous data that might incur outstanding loss; this in flip may want to probably cause failure of the commercial enterprise. In order to steer clear of it, facts desires to be processed and converted into great data, and in flip be said to the proper humans on the proper time.

Put simply, suitable facts is beneficial in offering insight. Businesses, whilst armed with this, can enhance the everyday selections they make. This isn’t only for management; it applies to each degree from the floor to the top. However, facts is not often beneficial in its uncooked state; it need to be processed and provided in a manner that works withinside the respective degree, in order that it is able to be applied appropriately.

If the accuracy of facts is low at the start of the procedure, it ends in loss of insight, and hence, the selections it affects also are in all likelihood to be bad. Therefore, corporations need to comprehend the criticality of facts and recognize that great is greater critical that quantity. Most humans prioritize best on collecting data with out giving significance to the accuracy of data and if/how it can be used for in addition processing.

Organizations that achieve the best ROI are the ones which degree the effect of bad great of facts in addition to the advantages of getting stepped forward and more advantageous facts. Metrics variety from shorter processing time, decreased hardware costs, shorter income cycles, correct analytics, decreased telemarketing costs, expanded go back on current generation investments (together with ETL applications), and better cross-promote and up-promote volumes and different advantages of stepped forward facts great.

What is ETL Testing

ETL stands for Extract, Transform and Load facts, and it’s miles predominantly performed through preferred software program gear like Informatica, Ab Initio, Datastage, OWB, SSIS, etc., to be had withinside the market. This facilitates to build, manage, and keep the integrated/migrated facts.


Extract is the procedure of extracting the preferred facts from exclusive homogenous or heterogenous facts reassets (databases/applications).


The extracted facts is then converted to the desired layout or shape as consistent with commercial enterprise desires. This procedure can occur on a separate staging environment. Depending on commercial enterprise desires, the transformation executed may be fundamental or advanced.

Basic Transformation: Data is subjected to Cleansing, Scrubbing, Deduplication, Format revision, and key restructuring.

Advanced Transformation: Data is subjected to Filtering, Joining, Splitting, Sorting, Transposing, Aggregation, Summarization, Derivation, and Integration.


Finally, the converted facts is loaded into the goal vacation spot together with databases or datawarehouses in a single complete load or in incremental mode as consistent with commercial enterprise necessities.

Need for ETL checking out

Transportation of facts from extraction to loading may want to bring about human or device mistakes which might bring about bad data conveyed throughout the enterprise. ETL checking out guarantees that such mistakes do now no longer occur, and eliminates/mitigates the insects primarily based totally on the subsequent strategies:

  • Data Completeness
  • Data Correctness
  • Data Integrity
  • Data Reconciliation
  • Data Transformation
  • Data Quality
  • Performance and scalability of device

List of exams performed:

  • Unit checking out
  • System checking out
  • System Integration checking out
  • Regression checking out
  • Performance checking out
  • User recognition checking out

Challenges confronted through testers in ETL/facts warehouse initiatives

Usually testers in ETL/facts warehouse initiatives face the subsequent challenges:

  • Frequent adjustments to necessities
  • Availability of supply facts best for a positive period
  • No get right of entry to to ETL gear or their code
  • Unavailability of supply to goal mapping documents
  • More reaction time required through SQL question
  • Verifying and validating facts comes from exclusive reassets with various codecs and systems
  • Unstable checking out environments
  • Huge extent of facts to take a look at

Strategic technique of Cigniti to assist in Data centric initiatives

Cigniti Technologies knows that correct facts is the important thing to reach to critical selections in any commercial enterprise. Hence, Cigniti has helped many customers in Banking, Finance, Insurance, and Retail area to attain 100% in facts great. We perceive insects, carry out root purpose evaluation and file them on the early degree of SDLC to lessen the value and time. Before getting ourselves into ETL checking out, we check out the exclusive structures, their models, tactics and commercial enterprise necessities for any inconsistencies or ambiguities. We carry out facts profiling/facts mining to higher recognize the tendencies and styles of facts and perceive any supply facts insects.

Data warehouse checking out

We evaluate facts from exclusive facts reassets having exclusive codecs/systems to the goal structures as consistent with the commercial enterprise rule. On relational databases, we run tremendous SQL queries on large volumes (terabytes) of facts to perceive the facts anomalies.

  • Data Checksum – Source to Target Counts
  • Source to Target facts checking out
  • Target to Source facts checking out

Business Intelligence Reports

We take a look at reviews generated through Cognos, Micro strategy, Tableau, SSRS, SAS, Crystal Reports, Pentaho, SAP BO and so on, for accuracy, hierarchy, granularity, security, and performance.

We at Cigniti Technologies recognize how a poorly written SQL question can take a toll on databases and degrade its performance. Keeping that during mind, we examine queries, music them to attain correct consequences with much less reaction time and recommend thoughts to remove bottlenecks in database performance.

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