Learning Objectives of CP-TDM Click here to visit CPTDM website"Knowledge with experience is power; certification is just a by-product"
What is CP-TDM?
CP-TDM stands for "Certified Professional –Test Data Management". This certification is prepared and honored by "Agile Testing Alliance" & "University Teknologi Malaysia"
The course is applicable for all roles and not just "testers". Knowledge, experience & certification is consciously designed to focus on services and all aspects of TDM (Data Profiling, Analysis, data generation, data sub setting, and data Masking).
How is it useful?
Challenges like unavailability of test data, Poor Data quality, data storage are pretty much common across all projects. Lack of Test data results into challenges of test execution for key test scenarios. Regulations like GDPR, HIPAA, an understanding of PII (Personally identifiable information) and its protection, are today’s imminent needs.
This course introduces participants to the principles of test data management including recap of database and warehouse concepts. It starts with the need of test data, planning for test data, analysis of available data for different types of testing vis a vis available test environment. Participants are then introduced to data generation, masking, anonymization, profiling, sub-setting in depth to understand its significance and details.
The course emphasizes on practical implementation. It will help to understand real world problems and find their optimum solutions. There will be dedicated hands on lab exercises on any one TDM Tool for Data masking, Data Generation, sub setting. Candidates will be able to design a Test data approach and implement TDM solutions at an enterprise level.
With the case studies, we will explain in a real-life project scenario, how to analyze a given TDM case/ problem, decide the possible TDM solution/s (masking/ generation or other based on type of requirement), estimate efforts, designing and executing the planned solution.
Am I Eligible?
1. Good understanding of database concepts.
2. Need to have an experience/ exposure in Software Development life cycle
3. Interest and motivation to build a career as a Test Data Management Professional.
Participants should preferably carry their own laptops (BYOD) with at least 4-8 GB RAM. In case of not having a laptop, participants may have to use available shared desktops
CP-TDM is designed specifically for corporate and working professionals alike. If you are a corporate you can opt for either 5 half days course or 3 days course followed by an examination. If you are a working professional opt for 3 days course followed by an examination.
1. Participants will be given a handbook of working with a leading TDM tool.
2. Participants will be given course notes and lab exercise handouts.
Learning Objectives of CP-TDM
1. Test Data Management Overview
1.1 Introductions, Challenges faced in any testing project
Introduction with participants, understand specific objectives if any, from participants, Interaction with people and will try to understand the test data related problem faced in their projects. Discussion on Data quality, Data storage problems, Data protection issues arising out of compliances like GDPR, HIPAA etc.
1.2 How TDM solution caters to challenges of testing project
Brief introduction to TDM services like Synthetic data generation, Data Masking, Data Archiving, Data Sub setting.
1.3 Lab Exercise 1: (TDM Tool Installation)
2. Data Generation 2.1 Synthetic data generation without maintaining referential integrity
Learn to generate test data with metadata (schema) information as input.2.2 Synthetic data generation maintaining referential integrity
Understand the intricacies involved in generating test data. Understand the importance of referential integrity to maintain the Data consistency within Data warehouse.2.3 Introduction to Lab Exercise 2:
Generating simple data set with Schema (e.g. Generation of PII fields using TDM tool uploading data in Oracle DB)
3. Data Masking
3.1 Overview of Data security compliances GDPR, HIPPA, PCI DSS etc.
Understanding the scope of GDPR and other compliances, detailed discussion on PII fields.
3.2 Masking principles
Understand Irreversibility of masking, Understand terminologies – Tokenization, masking, encryption. Understand synonyms of masking - De-identification v/s re-identification, data anonymization, data obfuscation.
3.3 Recap of Data warehousing fundamentals
Learn the difference between Surrogate Key and Natural Key. Different layers present in Data warehouse (Staging, Core, Semantic layers)
3.4 Data discovery and profiling
Learn about data profiling utilities, learn how to identify the sensitive fields present within a database.
3.5 Types of Masking (Static & Dynamic)
3.6 Masking methodologies (Shuffling, Substitution, Nulling)
Learn to mask the data in different ways and understand how to choose appropriate masking method as per the requirement Learn to identify the difference between Data Masking and data encryption
3.7 Overview of market leading tools (IBM OPTIM/Delphix / Solix)
3.8 Lab Exercise 3
Identify PII in a sample data set, understand data exploration, mask PII identified in earlier steps.
4. Data Sub Setting
4.1 Importance of creating subset of data in non -prod is
4.2 Creating a right sized data subset maintaining referential integrity
4.3 Lab exercise 4:
Creation of smaller subset of Data for Non- Prod Environment using Open source tool JAILER
5. Data archiving and restoration
5.1 Importance of data archiving
Data is increasing exponentially, and its storage is a pain area and Data archiving can very well solve this problem. Discussion on typical situations where Data Archiving is essential.
5.2 How to restore the archive the data
6. Case Study (Implementing masking at an Enterprise level)
Detailed discussion on the approach, strategy & planning for implementing masking at an enterprise level. Understanding GDPR and categorizing data based on sensitivity, identify most critical applications, prioritize & implement an apt TDM solution. This will be executed throughout the duration of course via different lab exercises as mentioned in above Learning objectives.
7. Recap & Deep Dive – Advanced topics.
Recap of entire session and address specific objectives from participants if not addressed in the course curriculum
Concept of Data Lake - Understand the concept of Data lake and how it leverages the data archiving services
Discuss Case - Using an in-house product specific masking solution v/s commercial masking tool – A SAP Hana case.