Reading Time: < 1 minute Earlier you learned about setting up data pipelines in the Data Factory experience in Microsoft Fabric. Today, learn a bit about how to monitor their execution. https://learn.microsoft.com/fabric/data-factory/monitor-pipeline-runs The post 40 Days of Fabric: Day 8 – Monitoring Pipelines appeared first on DataVeld ..read more

Reading Time: < 1 minute The Microsoft Fabric Data Factory experience contains both Pipelines and Dataflows. Pipelines is more of the classic PaaS Data Factory data engineering pipeline, while Dataflows traces its lineage to Power BI. With Microsoft Fabric, however, the new Dataflows Gen2 feature to load and transform data is available. Among other features, you can even save the data to alternative destinations in Gen2! Day 7 explores building a basic dataflow. Compare and contrast it with pipelines from yesterday to see what may fit your workflow. https://learn.microsoft.com/fabric/data-f ..read more

Reading Time: < 1 minute Last day of Orientation Week! Today, get a summary of Microsoft Fabric terminology including core terms for each Fabric experience. https://learn.microsoft.com/en-us/fabric/get-started/fabric-terminology Check back Monday for Day 6 and the start of “Experience of the Week”! The post 40 Days of Fabric: Day 5 – Terminology appeared first on DataVeld ..read more

Reading Time: < 1 minute For Day 4 in the 40 Days of Fabric, learn about Direct Lake and the advantages over traditional DirectQuery or Import storage modes in Microsoft Power BI. https://learn.microsoft.com/power-bi/enterprise/directlake-overview The post 40 Days of Fabric: Day 4 – Direct Lake appeared first on DataVeld ..read more

Reading Time: < 1 minute After OneLake yesterday, now learn more about the basic concepts of Lakehouse in Microsoft Fabric: https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview The post 40 Days of Fabric: Day 3 – Lakehouse appeared first on DataVeld ..read more

Reading Time: < 1 minute Yesterday included an introduction to the SaaS foundation of Microsoft Fabric, personas, and OneLake. Next for Day 2, learn more about how OneLake acts like a “OneDrive for Data”: One data lake per organization, the open Delta Lake format, with data virtualization available with Shortcuts to help eliminate silos, a file explorer for Windows, and more. https://learn.microsoft.com/fabric/onelake/onelake-overview The post 40 Days of Fabric: Day 2 – OneLake appeared first on DataVeld ..read more

Reading Time: < 1 minute Forty Days of Fabric! Learn a bit about Microsoft Fabric every day. Maybe you caught the announcements around Microsoft Build nearly three weeks ago, but there is so much new material to digest with the evolution of Microsoft analytics with Fabric. How is it different from Power BI? How does it extend Power BI? What are new terms like OneLake, Lakehouse, and more? Better people than I have all the answers to these types of questions, but I also hope to explore Fabric in “bite size chunks”. Let’s kick it off easy on Day 1 with “What is Fabric?” https://learn.microsof ..read more

Reading Time: 3 minutes Tableau has Python packages for working with its Hyper API, both for native operations (tableauhyperapi) or through pandas (pantab). With the recently announced public preview of Microsoft Fabric, you can also easily work with data sourced from Tableau Hyper files in a Fabric Lakehouse. This post explores using the Notebook feature in Microsoft Fabric to convert a Hyper file that’s been loaded into a lakehouse as a file and convert it to parquet using pantab. From there, you can load as a Delta Lake table for use with other Fabric experiences such as Power BI. To get st ..read more

Reading Time: 3 minutes Data visualization and design have been essential skills for anyone who wants to communicate insights from data effectively. Creating engaging and informative visuals and reports often requires a lot of time and effort, however; and involves multiple iterations of data analysis, design choices, and feedback loops. Is this lengthy process still relevant in the era of generative AI, where we can now automatically generate visuals and reports from data? Even in its infancy, generative AI has upended the way we work. AI is now used for diverse purposes such as generating re ..read more

Reading Time: 3 minutes Data is generated by every click, swipe, and interaction we have online. It is collected by businesses, governments, and organizations to understand their customers, markets, and operations. It is analyzed both manually and automatically by tools, AI algorithms, and models to generate insights, predictions, and recommendations. It’s easy to get caught up in all the noise. To make the most of data, we need data literacy. Data literacy is the ability to read, understand, create, and communicate with data. It is a skill that empowers us to ask the right questions, find the ..read more