Hey community! Are you using the Variable Library in Fabric?! I'm doing some research for a future YouTube video, and it would be great to open up a bit of a discussion around how you're using them so far, considering they are now Generally Available! What use cases are you using them for? Any tips to share about how you set them up? Or perhaps you're using the REST API endpoints for variable libraries for more advanced use cases? Any experience, big or small, would be useful to know about - so please share below! 👇👇👇

Any preferred approaches/tips to follow when performing cleansing on data? As a first step data profiling makes sense where one can make use of public libraries for profiling e.g. ydata and great expectations to assist, after which you will get an idea how data is stored and used. Apart from applying the basic cleansing operations for e.g. on text fields to remove special and non-ascii characters and also remove more than 2 consecutive spaces, what else would be suggested? I guess this depends on the data owners which could vary based on requirements. More granular cleansing operations would require mappings to indicate a from and to value which could be put into a config table and then read via notebook processing when moving data between bronze and silver. Are the any other comments/steps/tips to consider as part of a best-practice approach?

Any preferred approaches/tips to follow when performing analysis & design for master data management? I would think that the identification of core business entities could be a first step? Then to also indicate non-transactional entities; entities shared across multiple systems; entities that are slow changing and hold the single source of truth. Next steps could involve improving data quality to get data curated and cleaned properly. Lastly, when it comes to endorsement I would think this could be from 2 viewpoints, from a consulting/suggested perspective and implementing the means to action from a customer/data owner view for e.g. identifying data owners, adding them to security groups and applying the adequate permissions in fabric to allow them to tag/endorse data with the master data badge/classification. Are the any other comments/steps/tips to consider as part of a best-practice approach?

Hi all! I would love to get your views and experiences with data quality and data validation in data analytics/ viz/ engineering. It would be great to hear your experiences on any of the following: - Is this an area you've worked in before? - If so, what were you doing and - how did you approach it? - Any particular tools or frameworks you used? - What were the outcomes for you and your company/ client? The reason I'm asking is that for me it's probably the most important part of any data strategy, and one that is often neglected. Fabric makes it simpler to implement data quality/ validation rules in nearly every part of the data analytics workflow/ system. If there's any interest, I'll look to create a series of videos on how to think about data quality and validation within Fabric, and how to actually implement measures that grow trust in your analytics systems, visualizations and insights.

Welcome to all new members, here's some links and information to help you get started! 𝗤𝘂𝗶𝗰𝗸 𝗟𝗶𝗻𝗸𝘀 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 - For an introduction to this community → Explore the Welcome Pack - New-ish to Fabric? → Check out our Fabric Foundation module - Studying for the DP-600? → Check out the DP-600 Module and the DP-600 category - Studying for the DP-700? → Check out the DP-700 Module and the DP-700 category - Want to get hands-on? → Check out Fabric Dojo 𝗛𝗼𝘄 𝘁𝗼 𝗲𝗻𝗴𝗮𝗴𝗲 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆? - Share your knowledge and experience! Even if you're relatively new to Fabric, or the community, your opinion and experiences are valued here! A great way to earn your first point(s) is to introduce yourself in the thread below 👇😀 Thank you for engaging and joining us on this exciting learning journey! 🙏 Will