Bert Lemmens

𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐛𝐲 𝐃𝐞𝐟𝐚𝐮𝐥𝐭! Starting May 20th, 2024, Copilot in Power BI will be enabled by default for all tenants, enhancing user experience and productivity. Updates include: -Privacy Enhancement: No prompts or data stored for abuse monitoring. -EU Data Boundary: Data remains in the EU by default. -Administrative Delegation: Capacity admins control Copilot settings. To opt out, disable the setting before May 20th! 👉My Copilot post: https://lnkd.in/eyj9q_FU https://lnkd.in/ebajAC6f #Copilot #MicrosoftFabric #PowerBI

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Power Query, Dax, Dataflow,.. 🤯 Mind mapping workshop for this morning to visualise and organise our ideas around developping robust semantic models with Power BI. #PowerBI #MindMapping #Datavisualization #SemanticModels

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Why does Stijn (Stan) Christiaens the Co-Founder of Collibra have the title 'Chief Data Citizen'? He explained all on Driven By Data: The Podcast a couple of weeks ago! A great episode which you can listen to below ⬇️ #data #value

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Switch before it hurts!🤕 That is the clear message our colleague Arto De Graef wrote in this latest #PowerBI blog. Lots of companies have done great without really paying too much attention to their data and its underlying processes. They have grown over time without doing so, but there is a risk in missing the next wave. Discover how Power BI will ease the switch and the vast amount of benefits this will bring to your reporting. If you aren't new to Power BI, check out our latest Webinar on Power BI + Copilot. That video is at the bottom of this blog. #PowerBI #ExcelReporting #ExcelToPowerBI

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Have you ever wondered what it means to be a Data Engineer and Cloud Architect? 🤔 Our Coder, Marc Vea, tells you about his journey to become a Data Engineer: his studies, experience and what he enjoys the most on the day to day. 👏 Do you want to know more about our roles and positions? Let us know in the comments! 💬👇 #SEATCODE #Coders #Careers #Jobs #Engineer

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[Tip] Configure Microsoft Fabric Database Mirroring for Snowflake In this tip, we explore how to configure mirroring for a Snowflake database to be available in Microsoft Fabric. Author: Koen Verbeeck >>> https://lnkd.in/eeugyrWx #sql #sqlserver #microsoftsqlserver #mssql #mssqlserver #fabric #mirroring #snowflake #sqlbi #businessintelligence

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#ESPC Session - Unlock the Potential of #Dataverse: A #ComparativeAnalysis with #SQLServer and #SharePoint List In this session Serge Luca, MVP and Isabelle Van Campenhoudt, MVP, will delve into the world of data management platforms, focusing on Microsoft’s Dataverse. Find out more: https://lnkd.in/etbE5av9

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🚀 New Article Alert! 🚀 Have you ever wanted to access all the necessary data fields in Microsoft Dynamics 365 #BusinessCentral for your Business Intelligence (#BI) projects? 🤔 I've just published a comprehensive guide on Medium that shows you how to unlock the full potential of BI using Custom APIs in Business Central. 🌐 In this article, you'll learn: 🔧 How to set up your development environment in Visual Studio Code 📊 How to create custom APIs that give you complete data access 🔍 How to seamlessly integrate these APIs with Power BI for dynamic analysis If you're ready to take your BI to the next level, this is a must-read! 💡 #BusinessIntelligence #MicrosoftDynamics365 #BusinessCentral #PowerBI #CustomAPIs #DataAnalytics

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Key takeaways from last night's #databricks meetup in Ghent: - PySpark > Pandas - Focus data engineering efforts on critical, time-consuming pipelines - Automated machine learning still requires in-depth data science knowledge - Don't make access management a bottleneck, implement a smooth process and access management tool Thanks David De Wilde, Timour Bilgic, Bart Vandekerckhove 🔐⬅ for the inspiring talks & #delaware for hosting! #databricks #automl #datashift

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Hello, everyone! I have witnessed experienced Data Engineers in the community, in particular 👨🏻‍💻 Jaco van Gelder, complaining that it is pretty unfortunate that we rarely see unit tests in data pipelines -- and they are right! Although Data Engineering is not Software Engineering, best practices such as testing and documentation are applicable and should be the standard. So, if your data team is still in this phase where it knows tests are important but still don't know how to start implementing them, check this article where we use Dagster and dlt to create and manage pipelines in a way that makes unit tests easy to implement and integrate in the development workflow. https://lnkd.in/eW3Ri_4R #dataplatformengineering #dagster #dlt #testing

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Heard this question today. "Do we need Copilot for Microsoft 365 licenses to use Copilot in Power BI?" Quick answer: no Longer answer: check this link:

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REALLY digging the Data Transporter tool by Bram Colpaert. This thing is making moving data from 4 different #dataverse environments all the way up to production a sinch! The beauty of it is it keeps the same GUIDS across all environments with options to create/update/delete.

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🚨 Microsoft just changed the game! 🚨 They’ve open-sourced bitnet.cpp, a super-efficient 1-bit LLM inference framework that runs directly on CPUs. This means you can now run 100B models on local devices with up to 6.17x speedups and 82.2% less energy – no GPU needed! ⚡ Huge shoutout to Gabriele Venturi for sharing this game-changing innovation. #LLM #AI #bitnetcpp #EnergyEfficiency #Microsoft #Innovation bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support fast and lossless inference of 1.58-bit models on CPU (with NPU and GPU support coming next). The first release of bitnet.cpp is to support inference on CPUs. bitnet.cpp achieves speedups of 1.37x to 5.07x on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by 55.4% to 70.0%, further boosting overall efficiency. On x86 CPUs, speedups range from 2.37x to 6.17x with energy reductions between 71.9% to 82.2%. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. More details will be provided soon. What does this mean for businesses? AI models can now run faster, cost less, and use less energy, making powerful AI more affordable without expensive hardware. Open Source is king in the AI race #LLM #AI #bitnetcpp #OpenSource #EnergyEfficiency #Microsoft #Innovation

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5 Valuable Features in Qlik by Thibault Verduyn #Qlik #Analytics Day 2024 element61 #Thoughtleading #Analytics #qlik #qliksense

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🚀 DE Interview 📢 Cloud Optimization ⌛ 1-Min-Read Optimizing Cloud Storage Using Spark With a Housekeeping Script 🧔🏽‍♂️Interviewer: Can you explain how you optimized cloud storage using Spark with a housekeeping script? 👨‍💻Candidate: 1️⃣ In one of our projects, we were facing challenges with rapidly increasing storage costs due to accumulating data in AWS S3. To address this, I implemented an optimization strategy using Spark along with a housekeeping script. 🧔🏽‍♂️Interviewer: That sounds interesting. Could you walk me through the steps you took? 👨‍💻Candidate: 1️⃣ First, I analyzed the storage patterns and identified that a significant portion of the data was either stale or less frequently accessed. 2️⃣ I then developed a Spark job to classify this data based on its usage and relevance, segregating it into active and inactive datasets. 3️⃣ The inactive data was compressed using efficient algorithms, and a housekeeping script was created to automate the compression process and manage the data lifecycle. 4️⃣ The script would regularly run to compress, archive, and move infrequently accessed data to a low-cost storage tier while deleting unnecessary files. Versioning in S3 was enabled to safeguard against accidental deletion. 🧔🏽‍♂️Interviewer: How did this impact your overall cloud storage management? 👨‍💻Candidate: 1️⃣ The implementation of the Spark-based solution and housekeeping script led to significant cost savings by reducing the storage footprint. 2️⃣ It ensured that only relevant data occupied primary storage layers, improving data retrieval speeds and system efficiency. 3️⃣ Additionally, the automated process minimized manual intervention, reducing errors and operational overhead. Summary: 1️⃣ Problem: Escalating cloud storage costs and inefficient data management. 2️⃣ Solution: Implemented Spark-based optimization and automated housekeeping script for data lifecycle management. 3️⃣ Impact: Reduced storage costs, enhanced data management efficiency, and streamlined cloud operations. #CloudOptimization #Spark #AWS #DataManagement #CostSavings #Automation #TechInnovation #CloudComputing #S3 #DataLifecycle

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