Bugraptors always remains up to date with latest technologies and ongoing trends in testing. Technology like ELT Testing bringing the great changes which arises the scope of testing by keeping in mind all the positive and negative scenarios.
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Building an Effective Data Warehouse ArchitectureJames Serra
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Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Slides are created to demonstrate about ETL Testing, some one who want to start and learn ETL Tesing can make use of this ppt. It includes contents related all ETL Testing schema
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
Data Warehouse â Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Edureka!
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This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial:
1. What Is The Need For BI?
2. What Is Data Warehousing?
3. Key Terminologies Related To Data Warehouse Architecture:
a. OLTP Vs OLAP
b. ETL
c. Data Mart
d. Metadata
4. Data Warehouse Architecture
5. Demo: Creating A Data Warehouse
Top 20 ETL Testing Interview Questions.pdfAnanthReddy38
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ETL Testing Training from Magnitia helps you to learn a step-by-step process that includes ETL Testing introduction, difference between OLAP and OLTP, RDBM, learning data warehousing concepts, its workflow, difference between data warehouse testing and database testing, deploying SQL for checking data and the basis for Business Intelligence.
Building an Effective Data Warehouse ArchitectureJames Serra
Â
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Slides are created to demonstrate about ETL Testing, some one who want to start and learn ETL Tesing can make use of this ppt. It includes contents related all ETL Testing schema
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
Data Warehouse â Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Edureka!
Â
This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial:
1. What Is The Need For BI?
2. What Is Data Warehousing?
3. Key Terminologies Related To Data Warehouse Architecture:
a. OLTP Vs OLAP
b. ETL
c. Data Mart
d. Metadata
4. Data Warehouse Architecture
5. Demo: Creating A Data Warehouse
Top 20 ETL Testing Interview Questions.pdfAnanthReddy38
Â
ETL Testing Training from Magnitia helps you to learn a step-by-step process that includes ETL Testing introduction, difference between OLAP and OLTP, RDBM, learning data warehousing concepts, its workflow, difference between data warehouse testing and database testing, deploying SQL for checking data and the basis for Business Intelligence.
What are the characteristics and objectives of ETL testing_.docxTechnogeeks
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ETL (Extract, Transform, Load) testing is a vital process in ensuring the accuracy, integrity, and performance of data as it moves through the ETL pipeline. It encompasses various characteristics and objectives aimed at validating data quality, transformation logic, error handling, and compliance with business rules and regulations. ETL testing is essential for maintaining reliable and efficient data processes in business intelligence and data warehousing projects.
Data lineage tracing is pivotal in ETL testing as it facilitates understanding, documenting, and visualizing the flow of data from source to destination. By tracking data transformations and movements, testers can effectively analyze, troubleshoot, and document data flows, ensuring transparency, accountability, and reliability in ETL processes.
When migrating from legacy systems, handling data consistency issues requires meticulous planning, including data profiling, mapping, cleansing, reconciliation, and thorough testing. This ensures a smooth transition and maintains data integrity across systems.
Testing slowly changing dimensions (SCDs) involves different approaches based on the type of SCD implemented, including Type 1, Type 2, Type 3, hybrid approaches, CDC mechanisms, and regression testing. Each approach ensures that dimensional data remains accurate and consistent over time.
By implementing comprehensive ETL testing strategies and leveraging various testing approaches, organizations can enhance data quality, ensure regulatory compliance, and make informed business decisions based on reliable data. ETL testing courses offer valuable opportunities for individuals to gain expertise in data quality assurance, preparing them for success in data-centric roles.
What is ETL testing and how to learn ETL testing.docxshivanikaale214
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ETL testing ensures data accuracy in data integration projects. To learn, understand ETL concepts, SQL, testing techniques, explore tools, practice with datasets, take courses, and gain hands-on experience. Testing resilience involves failure simulation, retry mechanisms, checkpointing, redundancy, and data recovery. Data encryption secures data in transit, complies with regulations, masks sensitive information, and validates integrity during ETL testing. Data migration accuracy is ensured through profiling, row count comparison, business rule validation, referential integrity checks, and user acceptance testing.
ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake.
What are the key points to focus on before starting to learn ETL Development....kzayra69
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Before embarking on your journey into ETL (Extract, Transform, Load) Development, it's essential to focus on several key points to build a robust foundation. Firstly, grasp the fundamental principles of ETL, encompassing data extraction, transformation, and loading processes. Acquire knowledge about data warehousing concepts as ETL often serves as a pivotal component in data warehousing projects. Furthermore, develop a solid understanding of SQL and databases, including tables, indexes, joins, and SQL syntax. Proficiency in programming languages like Python, Java, or scripting languages is also beneficial, depending on the chosen ETL tool or if building custom solutions. Explore popular ETL tools such as Informatica, Talend, Pentaho, or Apache NiFi to understand their features and capabilities. Additionally, familiarize yourself with techniques for ensuring data quality throughout the ETL process, including data validation, error handling, and data profiling. Understanding common data integration patterns such as batch processing and real-time processing is also crucial. These key points collectively lay the groundwork for effective ETL design, implementation, and maintenance, setting you on the path to success in the dynamic field of ETL Development.
ETL Testing Services - Safeguard Your DataBugRaptors
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ETL testing is done to ensure that the data that has been loaded from a source to the destination after business transformation is accurate. It also involves the verification of data at various middle stages that are being used between source and destination. To know more, visit our portfolio at www.bugraptors.com
Creating a Data validation and Testing StrategyRTTS
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Creating A Data Validation & Testing Strategy
Are you struggling with formulating a strategy for how to validate the massive amount of data continuously entering your data warehouse or data lake?
We can help you!
Learn how RTTSâ Data Validation Assessment provides:
- an evaluation of your current data validation process
- recommendations on how to improve your process and
- a proposal for successful implementation
This slide deck addresses the following issues:
- How do I find out if I have bad data?
- How do I ensure I am testing the proper data permutations?
- How much of my data needs to be validated and automated?
- Which critical data endpoints need to be tested?
- How do I test data in my cloud environments?
And much more!
For more information, visit:
https://www.rttsweb.com/services/solutions/data-validation-assessment
Automate data warehouse etl testing and migration testing the agile wayTorana, Inc.
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Data Warehouse, ETL & Migration projects are exposed to huge financial risks due to lack of QA automation. At iCEDQ, we suggest the agile rules based testing approach for all data integration projects.
Learn the fundamentals of ETL (Extract, Transform, Load) and the innovative concept of Zero ETL in data integration. Explore how traditional ETL processes handle data extraction, transformation, and loading, and discover the streamlined approach of Zero ETL, minimising complexities and optimising data workflows.
Know more at: https://bit.ly/3U6eWxH
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What is ETL testing & how to enforce it in Data Wharehouse
1.
2. Extraction, Transformation, and Loading are the three tenets that will
help you in checking the efficiency of the data.
Before we go any further, do you know what ELT testing is?
Majority of you I bet donât know much about it.
And thatâs the reason for me to write this blog. Here you will learn all
about ELT testing and its utilization in the data warehouse. So, without
any further ado, letâs get you started.
3. To check the correctness of data change against the signed off
business necessities and principles:
1. To confirm that expected data is loaded into data mart or data
warehouse center without loss of any data.
2. To approve the precision of compromise reports (if any e.g. if
there should arise an occurrence of examination of the report of
exchanges influenced through to bank ATM â ATM report versus
Financial balance Report).
3. To ensure finish processes meet performance and scalability
requirements.
4. Data security is additionally in some cases some portion of
ETL testing.
5. To assess the reporting efficiency.
4. ETL stands for Extraction, Transformation, and Loading. ETL Testing is a
sort of testing strategy which must be done with human cooperation
where it needs to test the Extraction, Transformation, and Loading of
information when moved from source to target with respect to the
Business Requirements.
To get data from the source and load it into the data warehouse âa
procedure of duplicating information starting with one database then onto
the next. It also includes checking the data at different stages that are
being utilized source and destination.
Along these lines, the information is first extracted from the Online
Analytical Processing (OLTP) database and changed by the information
distribution center blueprint and after that stacked into the Data
warehouse. Be that as it may, the information could likewise be from a
non-OLTP source.
5. #Extracting information from outside sources
#Transforming it to fit operational needs (which can incorporate
quality levels)
#Loading it into the end target (operational information store and
data warehouses)
6. ETL Testing is not quite the same as application testing since it
requires a data-driven testing approach. Some of the challenges in
ETL Testing are:
# ETL Testing includes contrasting of expansive volumes of
information ordinarily a large number of records.
# The information that should be tried is in heterogeneous data
sources (eg. databases, flat files).
# Data is regularly changed which may require complex SQL
questions for looking at the information.
# ETL testing is particularly subject to the accessibility of test
information with various test situations.
In fact that there are slight varieties in the kind of tests that should be
executed for each task, underneath are the most well-known sorts of
tests that should be improved the situation ETL Testing.
7. Testing, ideally by a free gathering, ought to be embraced to confirm and
approve the ETL procedure, in this way guaranteeing the quality, fulfillment and
heartiness of the information distribution center. There are assortments of
instruments that can be utilized for ETL testing. There are a few levels of testing
that ought to be performed in ETL testing. Some levels of testing are defined
below:-
Requirements Testing:
# Are the requirements finished?
# Are the requirements testable?
# Are the requirements clear (is there any uncertainty)?
Data Validation Testing:
#Guarantee that the ETL application appropriately rejects, replaces with default
esteems and reports invalid data.
# Confirm that information is changed accurately as indicated by framework
prerequisites and business rules
# Look at interesting estimations of key fields between source data and
warehouse data.
8. Integration Testing:
# Check that ETL functions work with upstream and downstream processes.
# Confirm the underlying heap of records on data warehouse.
# Test error log generation.
Report Testing:
# Check report data with the information source.
# Make SQL questions to confirm source/target data.
# Check field-level data.
Client Acceptance Testing:
# Confirm that the business rules have been met.
# verify that the framework is worthy to the client.
9. Execution Testing:
# Check that data loads and queries are executed within the timeframe.
# Confirm stack times with different measures of information to anticipate
adaptability.
Regression Testing:
# Guarantee that current functionality remains flawless at whatever point or
new code is implemented.
10. Besides essentially enhancing coordination, the advantage of utilizing ETL and data
warehouse is accomplishing quicker response time. The use of data distribution
centers permits the exchange and the examination procedures to work
autonomously. This empowers ventures to accomplish more prominent proficiency
both at the source and the data warehouse transactions processing and also faster
and better querying and analysis.
The second huge advantage of using ETL is enhancing overall data quality. The
three-advance procedure of extricating, changing, and stacking empowers ETL
analyzers to audit the rightness of data in each step. Therefore, ETL analyzers can
distinguish and solve data errors where they occur â in the source, in the data
warehouse, or during the transformation process.
Finally, using ETL additionally advances greater organizational and operational
efficiency. The ETL procedure guarantees that changes made to source data,
paying little heed to where the progressions are started, will be reflected in the data
warehouse. This enables diverse branches of ventures to actualize their own
particular ad hoc software or systems while being assured that the data they use
reflect the changes made by other departments. This engages them to take
activities that will profit their specializations while moving the entire organization
forward.
11. We, at BugRaptors, while performing Web Accessibility testing we are
also implementing thoughts to make sure that we can manage required
tasks and perform accessibility testing by keeping in mind all the positive
and negative scenarios.