Blockchain Council’s Post

A few months ago, I wrote a critical post about Databricks' Lakeview Dashboards, which I considered one of my least favorite Databricks features. Here is an updated take, now that they are generally available. Pros: -UI has drastically improved in terms of stability. -The interface's minimalism might appeal to non BI developers that need simple visualizations. -It shallowly touches on important features that over time, if properly developed, might make it shine more. Neutral: -The AI chart builder has potential, but currently is very rough around the edges. It is in beta. -Right clicking on a widget and exporting the dataset prepared for the specific widget into Excel was pretty straight forward, though it did not preserve the order of the data. Cons: -When building a dataset, there is no ability to see Unity Catalog comments either before or after bringing in fields. -No ability to center text in titles/descriptions and generally speaking, it lacks simple refinements across the broad elements it offers that are crucial to building a decent looking dashboard. -Couldn't customize axis value formatting or labels on values. -Can't make filter target only a specific chart and there is no cross-filtering. -Couldn't unsnap filters from other widgets. -Can't undo/redo action when developing. Closing Thoughts -Lakeview Dashboards do the trick for very basic reporting/monitoring needs that do not need drill downs and are not meant to be shared with executives or managers, in a similar way as Notepad does the trick for notes instead of a Word document. -Right now, the focus is very heavy on "checking the boxes" for basic needs, and does not appear to be on developing a dashboard building experience that BI developers enjoy and that non-dev stakeholders enjoy as a product. For now, BI tools such as Sigma Computing (great development experience and visuals) and Power BI (fantastic capabilities) remain my go-to for developing dashboards. Hope you find this helpful!

Choosing the “𝐁𝐄𝐒𝐓” tool for data analytics 📶 depends on various factors, including your specific needs, the complexity of your data, and your expertise. Here are some top contenders, each with unique strengths:   1️⃣ 𝐓𝐚𝐛𝐥𝐞𝐚𝐮   • 𝑺𝒕𝒓𝒆𝒏𝒈𝒕𝒉𝒔: Intuitive drag-and-drop interface, excellent data visualization, and comprehensive support for various data sources. • 𝑩𝒆𝒔𝒕 𝒇𝒐𝒓: Users who need to create interactive and shareable dashboards with minimal coding.   2️⃣ 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈   • 𝑺𝒕𝒓𝒆𝒏𝒈𝒕𝒉𝒔: Seamless integration with Microsoft products, strong data visualization capabilities, and robust data manipulation features. • 𝑩𝒆𝒔𝒕 𝒇𝒐𝒓: Businesses heavily invested in the Microsoft ecosystem.   3️⃣ 𝐑 𝐚𝐧𝐝 𝐑𝐒𝐭𝐮𝐝𝐢𝐨   • 𝑺𝒕𝒓𝒆𝒏𝒈𝒕𝒉𝒔: Extensive statistical analysis capabilities, highly customizable, and a large repository of packages. • 𝑩𝒆𝒔𝒕 𝒇𝒐𝒓: Data scientists requiring advanced analytics and custom analysis.   4️⃣ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐰𝐢𝐭𝐡 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 (𝐏𝐚𝐧𝐝𝐚𝐬, 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛, 𝐒𝐞𝐚𝐛𝐨𝐫𝐧, 𝐒𝐜𝐢𝐤𝐢𝐭-𝐋𝐞𝐚𝐫𝐧)   • 𝑺𝒕𝒓𝒆𝒏𝒈𝒕𝒉𝒔: Versatile and powerful for data manipulation, visualization, and machine learning; large supportive community. • 𝑩𝒆𝒔𝒕 𝒇𝒐𝒓: Users comfortable with coding who need a flexible and comprehensive data analytics solution.   5️⃣ 𝐒𝐩𝐚𝐫𝐤   • 𝑺𝒕𝒓𝒆𝒏𝒈𝒕𝒉𝒔: Handles big data efficiently, supports multiple languages (Java, Scala, Python, R), and offers real-time processing. • 𝑩𝒆𝒔𝒕 𝒇𝒐𝒓: Large-scale data processing and analysis.   6️⃣ 𝐐𝐥𝐢𝐤𝐕𝐢𝐞𝐰/𝐐𝐥𝐢𝐤 𝐒𝐞𝐧𝐬𝐞   • 𝑺𝒕𝒓𝒆𝒏𝒈𝒕𝒉𝒔: Strong data discovery and visualization, associative data model for flexible data exploration, and robust analytics capabilities. • 𝑩𝒆𝒔𝒕 𝒇𝒐𝒓: Users who need an associative data model for intuitive data exploration. 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧: ➤ 𝘛𝘩𝘦 𝘣𝘦𝘴𝘵 𝘵𝘰𝘰𝘭 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘯𝘦𝘦𝘥𝘴 𝘸𝘪𝘭𝘭 𝘥𝘦𝘱𝘦𝘯𝘥 𝘰𝘯 𝘺𝘰𝘶𝘳 𝘴𝘱𝘦𝘤𝘪𝘧𝘪𝘤 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘮𝘦𝘯𝘵𝘴, 𝘣𝘶𝘥𝘨𝘦𝘵, 𝘢𝘯𝘥 𝘦𝘹𝘪𝘴𝘵𝘪𝘯𝘨 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘴𝘵𝘢𝘤𝘬. ➤ 𝘍𝘰𝘳 𝘨𝘦𝘯𝘦𝘳𝘢𝘭 𝘱𝘶𝘳𝘱𝘰𝘴𝘦𝘴 𝘢𝘯𝘥 𝘦𝘢𝘴𝘦 𝘰𝘧 𝘶𝘴𝘦, 𝘛𝘢𝘣𝘭𝘦𝘢𝘶 𝘢𝘯𝘥 𝘗𝘰𝘸𝘦𝘳 𝘉𝘐 𝘢𝘳𝘦 𝘦𝘹𝘤𝘦𝘭𝘭𝘦𝘯𝘵 𝘤𝘩𝘰𝘪𝘤𝘦𝘴. ➤ 𝘍𝘰𝘳 𝘮𝘰𝘳𝘦 𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘥 𝘢𝘯𝘥 𝘤𝘶𝘴𝘵𝘰𝘮𝘪𝘻𝘢𝘣𝘭𝘦 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴, 𝘗𝘺𝘵𝘩𝘰𝘯 𝘢𝘯𝘥 𝘙 𝘢𝘳𝘦 𝘩𝘪𝘨𝘩𝘭𝘺 𝘳𝘦𝘤𝘰𝘮𝘮𝘦𝘯𝘥𝘦𝘥. ➤ 𝘍𝘰𝘳 𝘣𝘪𝘨 𝘥𝘢𝘵𝘢 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘴, 𝘚𝘱𝘢𝘳𝘬 𝘪𝘴 𝘢 𝘴𝘵𝘳𝘰𝘯𝘨 💪 𝘤𝘰𝘯𝘵𝘦𝘯𝘥𝘦𝘳. Evaluate these tools based on your specific use case to find the best fit.

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Databricks Genie focuses on simplifying the process of writing SQL queries and performing data analysis within the Databricks platform, which is specialized for handling large datasets and machine learning tasks. Power BI Copilot is aimed at enhancing the creation and management of reports and dashboards in Power BI by providing AI-driven suggestions and natural language assistance, helping users to better visualize and interpret business data. #genie #databricks #powerbi #copilot

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I recently had the opportunity to dig in "Data Cleaning with Power BI" by Gus Frazer. It’s a resource that offers a clear look into data cleaning, simplifying the process and enabling readers to turn raw data into meaningful insights. In my role, where data is critical, I found the book’s emphasis on data quality and the practical use of Power Query to be very informative. This book stands out as a resource for anyone keen on refining their data handling skills within Power BI. The datasets featured in the book are accessible on GitHub, allowing for hands-on learning. Highlighted topics include: -The significance role of data cleaning in the analytical process. -Core concepts and strategies for effective data cleaning. -Actionable advice on common data cleaning tasks. -Usage of data quality and profiling to uncover data patterns and irregularities. -Advanced cleaning techniques utilizing Power Query, alongside R and Python scripts, and machine learning. -Fundamentals of data modeling and managing relationships in Power BI. -Using automation and AI to streamline data cleaning. The book is an excellent fit for data analysts, BI professionals, business analysts, data scientists and anyone involved in data-driven roles. Interested in enhancing your data cleaning prowess? The book is available here: https://packt.link/Ai4EC #DataCleaning #PowerBI #BusinessIntelligence #DataAnalytics #BI #PowerQuery

Conversational analytics is coming to BigQuery. Here's why it matters 👇 In many organisations, self-serve BI often feels like a myth. You've likely heard: “It never works like it should,” or “We tried it, but nobody adopted it – it just doesn’t work.” But I’ve seen firsthand what happens when it does work, and it’s nothing short of transformative. This new update brings the line between data analysts and end users all the way up to the data warehouse level. Previously, extracting meaningful insights from a database required knowing which table to query and writing SQL code to get the data out. But now, BigQuery handles both tasks for you. All you need to do is ask the right question. Granted, this will only work with a well-structured data warehouse 😉

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📢 Exciting News from Microsoft Power BI! 🚀 Thrilled to announce the general availability of the Tabular Model Definition Language (TMDL)! 🎉 TMDL is a powerful new feature that allows you to define and manage your tabular models programmatically. This means you can automate the creation and management of your models, making it easier to scale and maintain your BI solutions. Key benefits include: 🔧 Enhanced automation and scripting capabilities 🔄 Improved version control and collaboration 🔍 Greater transparency and control over model definitions Whether you're a data engineer, BI developer, or analyst, TMDL is set to revolutionize how you work with tabular models. Check out the full announcement here: https://lnkd.in/d-KCCzHK #PowerBI #DataAnalytics #BI #TabularModel #TMDL #DataEngineering #Automation #Innovation