This course is perfect for absolute beginners who want to learn more about Collibra Data Quality & Observability. It provides an introduction to the main features of the tool, as well as tips and tricks for furthering knowledge. Learners will gain an understanding of how to set up their Collibra account, get familiar with the interface, connect to data, run jobs, and use features such as Data Patterns, Duplicates, Schema Monitoring, Record Monitoring, Source Monitoring, Data Shapes/Formatting, Data Outliers, Data Rules, Data Behaviours, Data Scheduling, Score Cards, The Explorer, Data Rules, Data Catalog, Reports, Data Alerts, and Admin options. Best practices are also discussed. Click this course to gain a comprehensive understanding of Collibra Data Quality & Observability and become an expert in no time!

This DQS Training course is designed to provide new learners with an introduction to the fundamentals of DQS. It covers a wide range of topics, such as terminologies, architecture, installation, data cleansing, data sources, domains and knowledge base, cleansing projects, SSIS packages for data cleansing, data deduplication, matching policies, deduplication projects, advanced DQS, term based relations, composite domains, creating regular expressions rules in a domain, domain data types, importing and exporting knowledge base and domains, opening, deleting, renaming, and unlocking knowledge base and projects, purpose of each DQS databases, backup and restore DQS databases, and DQS configurations and activity monitor. This course is perfect for those who want to learn the basics of DQS and gain a comprehensive understanding of the system.

This course provides learners with the opportunity to learn the key terminologies, basic concepts, and implementation techniques needed to build fully functional data quality implementations with the popular ETL tool Pentaho Data Integration and Data Quality tool DataCleaner. With 8 sessions, 43 lectures, and 103 minutes of content, learners will gain an understanding of the importance of data quality and the confidence to tackle the challenges of handling data of any volume and format. Downloadable resources and quizzes are also included to test knowledge. Upon completion, learners will have the knowledge and confidence to implement fully functional automated data quality solutions in their projects. Join this course to gain the skills to improve data quality by profiling, cleansing, and automating the DQM process with these tools.

This course is perfect for beginners looking to gain an understanding of data quality fundamentals. It covers topics such as data strategy, data governance, data architecture, data lifecycle management, analytics and data science, data quality objectives, and data quality dimensions and examples. Learners will also learn about data quality policies and procedures, data profiling, validation rules, facets, data cleansing, and more. Additionally, learners will gain an understanding of how data quality is linked to other data disciplines. By the end of the course, learners will have a comprehensive understanding of data quality and its importance in business performance. Sign up now to learn the fundamentals of data quality and how it can help your business succeed.