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Justin Delisi writes many informative blogs—in fact, he's on his way to 100 🤯 Here are 3 recent Snowflake blogs from him that several others have found helpful on their journey to utilizing the platform to its fullest 👇 ❄ Blog 1 covers Snowflake's best features for transforming your data 👇 https://lnkd.in/gGEuNZh8 ❄ Blog 2 explains what Snowflake Horizon is, what features it includes, and how you can use it 👇 https://lnkd.in/gf6hhq7m ❄ Blog 3 explores Snowflake's key features that promote high data availability and allow you to recover your data if any disaster occurs 👇 https://lnkd.in/g8ScM9gW

Dive into the world of structured query language and harness the potential of your databases like never before. From optimizing queries to mastering data manipulation, our SQL solutions propel your business forward. Gain insights, drive decisions, and scale your tech seamlessly with our expert guidance. Let's revolutionize your data journey together! 💻✨ #TechScalerSolutions #SQLMastery #DataDrivenDecisions #TechRevolution #UnlockThePowerOfData #SQLExperts #InnovationInTech

📊NEWS POWER BI : Update to Direct Lake documentation 📰We are pleased to announce a significant update of the Direct Lake documentation. Direct Lake accelerates time to data-driven decisions by unlocking incredible performance directly against OneLake, without the need to manage costly, time-consuming data refreshes for large volumes of data in the lake. ℹ️ 𝘈𝘳𝘵𝘪𝘤𝘭𝘦 𝘱𝘰𝘶𝘴𝘴é 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘲𝘶𝘦𝘮𝘦𝘯𝘵 𝘨𝘳â𝘤𝘦 à 𝘗𝘰𝘸𝘦𝘳 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘦

Generate consistent, error-free code with AnalyticsCreator"s innovative platform. Own the code you create. #CodeGeneration #Innovation No-Code Data Pipeline Solution https://hubs.ly/Q02Bxlwn0

This is how you unlock the full potential of your GraphQL schema with custom scalars like DateTime and Email! Dive in with graphql-scalars for smoother validation and stronger data structures. Read the full article to learn more

What is the data shuffling? The idea of data shuffling is commonly used for tables that have a HASH distribution. Shuffling involves moving data from one node to another, which is a costly process likely to slow down query execution. There are two main causes for data shuffling: Firstly, when joining two tables with distinct distributions, and secondly, when joining two tables, both HASH distributed, but with differing distribution keys. #azuredataengineer #datashuffling

Imagine never having to write a SQL query again. With our AI-powered solutions, complex data retrieval is now as simple as asking a question in plain language. Transform how you interact with your databases today and step into the future of seamless data management - https://datius.chat

A simple how-to on a domain specific sql generator from text using genAI. Given RAG and token sizes, this could cover an entire enterprise data model. https://lnkd.in/gJ-zQ5GR

Data lineage: how do we compute it in practice? There's plenty of literature on data lineage - and with Thomas TYGREAT, we have decided to dive deeper into the topic. This piece is about computing dataset lineage and field lineage. We explore: - How dataset lineage tracks relationships between data sources across tables and dashboards - Field lineage, from column-to-column transformations to visualization fields - Practical computation methods including SQL parsing, API integrations, and pattern recognition Curious to know how different types of lineage shape your data ecosystem? Full article below 👇 https://lnkd.in/e9m2XV_F #DataEngineering #DataGovernance #Analytics #DataLineage #data #datacatalog

How to get Sempy (Semantic-link) to run when being triggered from a data pipeline which runs a Notebook in Fabric https://lnkd.in/euAaRr-f