The document discusses testing processes for data warehouses, including requirements testing, unit testing, integration testing, and user acceptance testing. It describes validating that requirements are complete and testable. Unit testing checks ETL procedures and mappings. Integration testing verifies initial and incremental loads as well as error handling. Integration testing scenarios include count validation, source isolation, and data quality checks. User acceptance testing tests full functionality for production use.
Data Warehouse Testing: It’s All about the PlanningTechWell
Today’s data warehouses are complex and contain heterogeneous data from many different sources. Testing these warehouses is complex, requiring exceptional human and technical resources. So how do you achieve the desired testing success? Geoff Horne believes that it is through test planning that includes technical artifacts such as data models, business rules, data mapping documents, and data warehouse loading design logic. Wayne shares planning checklists, a test plan outline, concepts for data profiling, and methods for data verification. He demonstrates how to effectively create a test strategy to discover empty fields, missing records, truncated data, duplicate records, and incorrectly applied business rules—all of which can dramatically impact the usefulness of the data warehouse. Learn common pitfalls, which can cost your business hundreds of thousands of dollars or more, when test planning shortcuts are taken. If you work in an environment that often performs data warehouse testing without proper planning and technical skills, this session is for you.
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
Data Warehouse Testing: It’s All about the PlanningTechWell
Today’s data warehouses are complex and contain heterogeneous data from many different sources. Testing these warehouses is complex, requiring exceptional human and technical resources. So how do you achieve the desired testing success? Geoff Horne believes that it is through test planning that includes technical artifacts such as data models, business rules, data mapping documents, and data warehouse loading design logic. Wayne shares planning checklists, a test plan outline, concepts for data profiling, and methods for data verification. He demonstrates how to effectively create a test strategy to discover empty fields, missing records, truncated data, duplicate records, and incorrectly applied business rules—all of which can dramatically impact the usefulness of the data warehouse. Learn common pitfalls, which can cost your business hundreds of thousands of dollars or more, when test planning shortcuts are taken. If you work in an environment that often performs data warehouse testing without proper planning and technical skills, this session is for you.
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
What is a Data Warehouse and How Do I Test It?RTTS
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
P2Cinfotech is one of the leading, Online IT Training facilities and Job Consultant, spread all over the world. We have successfully conducted online classes on various Software Technologies that are currently in Demand. To name a few, we provide quality online training for QA, QTP, Manual Testing, HP LoadRunner, BA, Java Technologies.
Unique Features of P2Cinfotech:
1. All online software Training Batches will Be handled by Real time working Professionals only.
2. Live online training like Real time face to face, Instructor ? student interaction.
3. Good online training virtual class room environment.
4. Special Exercises and Assignments to make you self-confident on your course subject.
5. Interactive Sessions to update students with latest Developments on the particular course.
6. Flexible Batch Timings and proper timetable.
7. Affordable, decent and Flexible fee structure.
8. Extended Technical assistance even after completion of the course.
9. 100% Job Assistance and Guidance.
Courses What we cover:
Quality Assurance
Business Analsis
QTp
JAVA
Apps Devlepoment Training
Register for Free DEMO:
www.p2cinfotech.com p2cinfotech@gmail.com +1-732-546-3607 (USA)
What is ETL testing & how to enforce it in Data WharehouseBugRaptors
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.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
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.
With data flowing from different mediums (RDBMS, SocialMedium, Legacy files),one of the efficacious mean for processing huge data is through Big Data, where Data Lake plays a critical role for storing Structured/Semi Structured and Unstructured Data. I have tried to give you a glimpse of , how testing of Data Lake is generally performed, what are the different types and approaches we follow.
What is a Data Warehouse and How Do I Test It?RTTS
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
P2Cinfotech is one of the leading, Online IT Training facilities and Job Consultant, spread all over the world. We have successfully conducted online classes on various Software Technologies that are currently in Demand. To name a few, we provide quality online training for QA, QTP, Manual Testing, HP LoadRunner, BA, Java Technologies.
Unique Features of P2Cinfotech:
1. All online software Training Batches will Be handled by Real time working Professionals only.
2. Live online training like Real time face to face, Instructor ? student interaction.
3. Good online training virtual class room environment.
4. Special Exercises and Assignments to make you self-confident on your course subject.
5. Interactive Sessions to update students with latest Developments on the particular course.
6. Flexible Batch Timings and proper timetable.
7. Affordable, decent and Flexible fee structure.
8. Extended Technical assistance even after completion of the course.
9. 100% Job Assistance and Guidance.
Courses What we cover:
Quality Assurance
Business Analsis
QTp
JAVA
Apps Devlepoment Training
Register for Free DEMO:
www.p2cinfotech.com p2cinfotech@gmail.com +1-732-546-3607 (USA)
What is ETL testing & how to enforce it in Data WharehouseBugRaptors
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.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
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.
With data flowing from different mediums (RDBMS, SocialMedium, Legacy files),one of the efficacious mean for processing huge data is through Big Data, where Data Lake plays a critical role for storing Structured/Semi Structured and Unstructured Data. I have tried to give you a glimpse of , how testing of Data Lake is generally performed, what are the different types and approaches we follow.
Deliver Trusted Data by Leveraging ETL TestingCognizant
We explore how extract, transform and load (ETL) testing with SQL scripting is crucial to data validation and show how to test data on a large scale in a streamlined manner with an Informatica ETL testing tool.
Completing the Data Equation: Test Data + Data Validation = SuccessRTTS
Completing the Data Equation
In this presentation, we tackle 2 major challenges to assuring your data quality:
1) Test Data Generation
2) Data Validation
We illustrate how GenRocket and QuerySurge, used in conjunction, can solve these challenges. Also see how they can be easily integrated into your Continuous Integration/Continuous Delivery pipeline.
Session Overview
- Primary challenges organizations are facing with their data projects
- Key success factors for data validation & testing
- How to setup a workflow around test data generation and data validation using GenRocket & QuerySurge
- How to automate this workflow in your CI/CD DataOps pipeline
to see the video, go to https://www.youtube.com/embed/Zy25i74l-qo?autoplay=1&showinfo=0
Data Warehouse Testing in the Pharmaceutical IndustryRTTS
In the U.S., pharmaceutical firms and medical device manufacturers must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11.
Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development.
Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11 (for example, Safety Data and Clinical Study project data). QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, has been effective in testing data warehouses used by Part 11-governed companies. The purpose of QuerySurge is to assure that your warehouse is not populated with bad data.
In industry surveys, bad data has been found in every database and data warehouse studied and is estimated to cost firms on average $8.2 million annually, according to analyst firm Gartner. Most firms test far less than 10% of their data, leaving at risk the rest of the data they are using for critical audits and compliance reporting. QuerySurge can test up to 100% of your data and help assure your organization that this critical information is accurate.
QuerySurge not only helps in eliminating bad data, but is also designed to support Part 11 compliance.
Learn more at www.QuerySurge.com
A Comprehensive Approach to Data Warehouse TestingMatteo G.docxronak56
A Comprehensive Approach to Data Warehouse Testing
Matteo Golfarelli
DEIS - University of Bologna
Via Sacchi, 3
Cesena, Italy
[email protected]
Stefano Rizzi
DEIS - University of Bologna
VIale Risorgimento, 2
Bologna, Italy
[email protected]
ABSTRACT
Testing is an essential part of the design life-cycle of any
software product. Nevertheless, while most phases of data
warehouse design have received considerable attention in the
literature, not much has been said about data warehouse
testing. In this paper we introduce a number of data mart-
specific testing activities, we classify them in terms of what
is tested and how it is tested, and we discuss how they can
be framed within a reference design methodology.
Categories and Subject Descriptors
H.4.2 [Information Systems Applications]: Types of
Systems—Decision support; D.2.5 [Software Engineering]:
Testing and Debugging
General Terms
Verification, Design
Keywords
data warehouse, testing
1. INTRODUCTION
Testing is an essential part of the design life-cycle of any
software product. Needless to say, testing is especially crit-
ical to success in data warehousing projects because users
need to trust in the quality of the information they access.
Nevertheless, while most phases of data warehouse design
have received considerable attention in the literature, not
much has been said about data warehouse testing.
As agreed by most authors, the difference between test-
ing data warehouse systems and generic software systems or
even transactional systems depends on several aspects [21,
23, 13]:
• Software testing is predominantly focused on program
code, while data warehouse testing is directed at data
and information. As a matter of fact, the key to data
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise, to
republish, to post on servers or to redistribute to lists, requires prior specific
permission and/or a fee.
DOLAP’09, November 6, 2009, Hong Kong, China.
Copyright 2009 ACM 978-1-60558-801-8/09/11 ...$10.00.
warehouse testing is to know the data and what the
answers to user queries are supposed to be.
• Differently from generic software systems, data ware-
house testing involves a huge data volume, which sig-
nificantly impacts performance and productivity.
• Data warehouse testing has a broader scope than soft-
ware testing because it focuses on the correctness and
usefulness of the information delivered to users. In
fact, data validation is one of the main goals of data
warehouse testing.
• Though a generic software system may have a large
number of different use scenarios, the valid combina-
tions of those scenarios are limited. On the other hand,
data warehouse systems are aimed at supporting any
views of data, so the possible combinat.
A Comprehensive Approach to Data Warehouse TestingMatteo G.docxmakdul
A Comprehensive Approach to Data Warehouse Testing
Matteo Golfarelli
DEIS - University of Bologna
Via Sacchi, 3
Cesena, Italy
[email protected]
Stefano Rizzi
DEIS - University of Bologna
VIale Risorgimento, 2
Bologna, Italy
[email protected]
ABSTRACT
Testing is an essential part of the design life-cycle of any
software product. Nevertheless, while most phases of data
warehouse design have received considerable attention in the
literature, not much has been said about data warehouse
testing. In this paper we introduce a number of data mart-
specific testing activities, we classify them in terms of what
is tested and how it is tested, and we discuss how they can
be framed within a reference design methodology.
Categories and Subject Descriptors
H.4.2 [Information Systems Applications]: Types of
Systems—Decision support; D.2.5 [Software Engineering]:
Testing and Debugging
General Terms
Verification, Design
Keywords
data warehouse, testing
1. INTRODUCTION
Testing is an essential part of the design life-cycle of any
software product. Needless to say, testing is especially crit-
ical to success in data warehousing projects because users
need to trust in the quality of the information they access.
Nevertheless, while most phases of data warehouse design
have received considerable attention in the literature, not
much has been said about data warehouse testing.
As agreed by most authors, the difference between test-
ing data warehouse systems and generic software systems or
even transactional systems depends on several aspects [21,
23, 13]:
• Software testing is predominantly focused on program
code, while data warehouse testing is directed at data
and information. As a matter of fact, the key to data
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise, to
republish, to post on servers or to redistribute to lists, requires prior specific
permission and/or a fee.
DOLAP’09, November 6, 2009, Hong Kong, China.
Copyright 2009 ACM 978-1-60558-801-8/09/11 ...$10.00.
warehouse testing is to know the data and what the
answers to user queries are supposed to be.
• Differently from generic software systems, data ware-
house testing involves a huge data volume, which sig-
nificantly impacts performance and productivity.
• Data warehouse testing has a broader scope than soft-
ware testing because it focuses on the correctness and
usefulness of the information delivered to users. In
fact, data validation is one of the main goals of data
warehouse testing.
• Though a generic software system may have a large
number of different use scenarios, the valid combina-
tions of those scenarios are limited. On the other hand,
data warehouse systems are aimed at supporting any
views of data, so the possible combinat.
Automate data warehouse etl testing and migration testing the agile wayTorana, Inc.
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.
Testing in the New World of Off-the-Shelf SoftwareJosiah Renaudin
Testing an off-the-shelf, sometimes called COTS, system? Often, project managers and stakeholders mistakenly believe that one benefit of purchasing software is that there is little, if any, testing required. This could not be further from the truth. Testing COTS software requires a different focus from traditional testing approaches. Although no software package will be delivered free of bugs, the testing focus from the purchasing organization’s perspective is not on validating the base functionality. Gerie Owen and Peter Varhol share a framework for testing COTS packages and discuss in detail each of the major focus areas―customizations and configurations, integration, data, and performance. Discover how to work with business processes and integration maps to design an effective test strategy. Whether you are testing a small COTS package or a large enterprise COTS application, join Gerie and Peter to learn how to focus your testing effectively and develop a new test skill set.
Software Project Management: Testing DocumentMinhas Kamal
Software Project Management: ResearchColab- Testing Document (Document-11)
Presented in 4th year of Bachelor of Science in Software Engineering (BSSE) course at Institute of Information Technology, University of Dhaka (IIT, DU).
Similar to Data Warehouse (ETL) testing process (20)
Being a fresher, it's challenging to get a job so here I'm documenting question bank which I've been asked during my job searching ( struggle ) time, this list of questions will help freshers candidates in interview of software testing jobs.
How does the QA team prepare test cases for Data Warehouse (BI) projects?Rakesh Hansalia
This document contains certain types of checks that can be done by QA team while preparing test cases or database queries for Data Warehouse (Business Intelligence ) projects.
This document contains some basic scenarios which should be tested while testing any BI (Business Intelligence) reports.
Prepared By:
Rakesh Hansalia
in.linkedin.com/in/rakeshhansalia/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
3. Introduction
This document details the testing process involved in data warehouse testing and test coverage
areas. It explains the importance of data warehouse application testing and the various steps of
the testing process.
About Data Warehouse
Data warehouse is the main repository of the organization's historical data. It contains the data
for management's decision support system. The important factor leading to the use of a data
warehouse is that a data analyst can perform complex queries and analysis (data mining) on the
information within data warehouse without slowing down the operational systems.
Data Warehouse definition
• Subject-oriented : Subject Oriented -Data warehouses are designed to help you analyse
data. For example, to learn more about your company's sales data, you can build a
warehouse that concentrates on sales. Using this warehouse, you can answer questions
like "Who was our best customer for this item last year?" This ability to define a data
warehouse by subject matter, sales in this case, makes the data warehouse subject
oriented. The data is organized so that all the data elements relating to the same real-
world event or object are linked together.
• Integrated : Integration is closely related to subject orientation. Data warehouses must
put data from disparate sources into a consistent format. The database contains data
from most or all of an organization's operational applications and is made consistent.
• Time-variant : The changes to the data in the database are tracked and recorded to
produce reports on data changed over time. In order to discover trends in business,
analysts need large amounts of data. A data warehouse's focus on change over time is
what is meant by the term time variant.
• Non-volatile : Data in the database is never over-written or deleted, once committed,
the data is static, read-only, but retained for future reporting. Once entered into the
warehouse, data should not change. This is logical because the purpose of a data
warehouse is to enable you to analyse what has occurred.
Testing Process for Data warehouse:
Testing for a Data warehouse consists of requirements testing, unit testing, integration testing
and acceptance testing.
Requirements Testing :
The main aim for doing Requirements testing is to check stated requirements for
completeness. Requirements can be tested on following factors.
1. Are the requirements Complete?
2. Are the requirements Singular?
3. Are the requirements Ambiguous?
4. Are the requirements Developable?
5. Are the requirements Testable?
In a Data warehouse, the requirements are mostly around reporting. Hence it becomes more
important to verify whether these reporting requirements can be provided using the data
available.
Successful requirements are those structured closely to business rules and address
functionality and performance. These business rules and requirements provide a solid
4. foundation to the data architects. Using the defined requirements and business rules, high
level design of the data model is created. Once requirements and business rules are
available, rough scripts can be drafted to validate the data model constraints against the
defined business rules.
Unit Testing :
Unit testing for data warehouses is WHITEBOX. It should check the ETL
procedures/mappings/jobs and the reports developed. This is usually done by the
developers.
Unit testing will involve following
1. Whether ETLs are accessing and picking up right data from right source.
2. All the data transformations are correct according to the business rules and data
warehouse is correctly populated with the transformed data.
3. Testing the rejected records that don’t fulfil transformation rules.
Integration Testing :
After unit testing is complete, it should form the basis of starting integration testing.
Integration testing should test out initial and incremental loading of the data warehouse.
Integration testing will involve following
1. Sequence of ETLs jobs in batch.
2. Initial loading of records on data warehouse.
3. Incremental loading of records at a later date to verify the newly inserted or updated
data.
4. Testing the rejected records that don’t fulfil transformation rules.
5. Error log generation.
The overall Integration testing life cycle executed is planned in four phases: Requirements
Understanding, Test Planning and Design, Test Case Preparation and Test Execution.
5. Scenarios to be covered in Integration Testing
Integration Testing would cover End-to-End Testing for DWH. The coverage of the tests
would include the below:
1. Count Validation
- Record Count Verification DWH backend/Reporting queries against source and
target as a initial check.
2. Source Isolation
- Validation after isolating the driving sources.
3. Dimensional Analysis
- Data integrity between the various source tables and relationships.
4. Statistical Analysis
- Validation for various calculations.
5. Data Quality Validation
- Check for missing data, negatives and consistency. Field-by-Field data verification
can be done to check the consistency of source and target data.
6. Granularity
- Validate at the lowest granular level possible (Lowest in the hierarchy E.g.
Country-City-Street – start with test cases on street).
7. Other validations
- Graphs, Slice/dice, meaningfulness, accuracy
.
QA Team Reviews BRD for
completeness.
QA Team builds Test Plan
Develop Test Cases and
SQL Queries
Test Execution
Process for Data warehouse Testing
Business Requirement
Document/Requirement
Traceability Matrix
Business Requirement
Document/Requirement
Traceability Matrix
Requirements TestingRequirements Testing
High Level Design
document
High Level Design
document
Review of HLDReview of HLD
Test Case PreparationTest Case Preparation
Functional TestingFunctional Testing
Regression TestingRegression Testing
Performance TestingPerformance Testing
User Acceptance Testing (UAT)User Acceptance Testing (UAT)
Unit TestingUnit Testing
6. Validating the Report data
Once the ETLs are tested for count and data verification, the data being showed onto the
reports hold utmost importance. QA team should verify the data reported with the
source data for consistency and accuracy.
1. Verify Report data with source
- Although the data present in a data warehouse will be stored at an aggregate level
compare to source systems. Here the QA team should verify the granular data
stored in data warehouse against the source data available.
2. Field level data verification
- QA team must understand the linkages for the fields displayed in the report and
should trace back and compare that with the source systems.
3. Creating SQLs
- Create SQL queries to fetch and verify the data from Source and Target.
Sometimes it’s not possible to do the complex transformations done in ETL. In such
a case the data can be transferred to some file and calculations can be performed.
User Acceptance Testing
Here the system is tested with full functionality and is expected to function as in production. At
the end of UAT, the system should be acceptable to the client for use in terms of ETL process
integrity and business functionality and reporting.
Conclusion
Evolving needs of the business and changes in the source systems will drive continuous change
in the data warehouse schema and the data being loaded. Hence, it is necessary that
development and testing processes are clearly defined, followed by impact-analysis and strong
alignment between development, operations and the business.
7. Validating the Report data
Once the ETLs are tested for count and data verification, the data being showed onto the
reports hold utmost importance. QA team should verify the data reported with the
source data for consistency and accuracy.
1. Verify Report data with source
- Although the data present in a data warehouse will be stored at an aggregate level
compare to source systems. Here the QA team should verify the granular data
stored in data warehouse against the source data available.
2. Field level data verification
- QA team must understand the linkages for the fields displayed in the report and
should trace back and compare that with the source systems.
3. Creating SQLs
- Create SQL queries to fetch and verify the data from Source and Target.
Sometimes it’s not possible to do the complex transformations done in ETL. In such
a case the data can be transferred to some file and calculations can be performed.
User Acceptance Testing
Here the system is tested with full functionality and is expected to function as in production. At
the end of UAT, the system should be acceptable to the client for use in terms of ETL process
integrity and business functionality and reporting.
Conclusion
Evolving needs of the business and changes in the source systems will drive continuous change
in the data warehouse schema and the data being loaded. Hence, it is necessary that
development and testing processes are clearly defined, followed by impact-analysis and strong
alignment between development, operations and the business.