Matillion’s Post

Hey LinkedIn fam!! I'm thrilled to share my latest blog post on Mastering "Real-Time Data Processing with Azure"! In this blog, I dive deep into the dynamic realm of real-time data processing and how Azure is revolutionising the game. From streamlining workflows to boosting efficiency, Azure offers powerful tools and services for handling data in real-time. If you're into data, analytics, or simply exploring the endless possibilities of Azure, this read is for you. Feel free to drop your thoughts and let's spark some engaging conversations. https://lnkd.in/dmbyGCFH #Azure #RealTimeData #DataProcessing #DataAnalytics #AzurePlatform

👀 Just a reminder for folks looking for a proven, innovative & secure (Microsoft) Data and AI platform that is a Leader in Forrester's Data Lakehouse Wave, (Azure) Databricks has you covered 🚀😎 #offthecharts #thereallakehouse #noshortcuts #databricks

Databricks has been named a 𝐋𝐞𝐚𝐝𝐞𝐫 again in Cloud data management systems by Gartner! 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬 1) Data science and AI: Databrick’s vision for the future includes the ability to use Databricks-developed large language models (LLM) to train on a customer’s own data and use the results of this training to customize other models as well as allow nontechnical users to query the Lakehouse Platform with natural language. 2) Unity Catalog: Unity Catalog provides fine-grained access control and metadata management and lineage for Databricks tables, files, notebooks, machine learning models and data in federated databases. Unity Catalog can access any data source that uses standard storage objects, like Parquet and Delta Lake, as well as other data sources such as MySQL, Microsoft SQL Server and Snowflake. 3) Scalability: With its platform built on a highly scalable data lake foundation, Databricks has provided its customers with an extreme level of scalability for appropriate tasks. Databricks customers have systems that can process petabytes of data a day or as little as a few gigabytes. 𝐂𝐚𝐮𝐭𝐢𝐨𝐧𝐬 1) Less mature relational capabilities: The relational capabilities of the Databricks Lakehouse are still relatively new and not as well-established as other leaders in this area for traditional relational workloads. 2) Product breadth can complicate focus: Because Databricks provides such a broad set of capabilities in a single, unified platform, users may find it challenging to understand its full range of capabilities or to find the right components to use for a particular use case. 3) Pricing models: As Databricks increases its pricing flexibility with additional options, such as a serverless option to go along with its standard consumption-based model, discovering and using the most optimal model for a particular set of use cases can be challenging. #databricks #gartnermagicquadrant #2023

Hey Connections, Please take a look at my new blog on Overview of Azure DataBricks. #databricks #data DataTheta.

A step-by-step guide on Microsoft Azure Data Factory with an example use case. thanks for Hevo Data for helping me on this. https://lnkd.in/dSme6Wkv

"Why is Sigma so blazingly fast on my 40 billion row table?" - asks every Sigma prospect customer when we run proofs on their product Cloud Data Warehouse....enter Alpha Query and Partial Evaluation with Sigma! Nerd out a little and discover how these techniques can revolutionize your data analysis process. #DataAnalytics #Sigma #Snowflake #Databricks

Hey, LinkedIn fam! 👋 I stumbled upon this fantastic article by Piet Hein that delves into the world of Azure Databricks and Microsoft Fabric integration. 🌐💡 As someone passionate about data engineering, SQL, and cloud technologies, I found Piet’s insights incredibly valuable. He breaks down the technical aspects and shares practical tips for seamless integration. 🤓🔍 🔗 Read the article here: https://lnkd.in/g3E2wnVh 🔥 #Azure #Databricks #CloudComputing #DataEngineering #MicrosoftFabric #TechInsights #IntegrationTips #BigData #DataScience #AI #MachineLearning #DevOps #CodingLife #TechCommunity #LearnAndGrow #Innovation #DataOps #CloudSolutions #Microsoft #ProfessionalDevelopment #CareerGrowth #SQL #Database

Finally, you now can use natural language to create queries in SQL or Python on your Petabyte sized, auto-scaling & serverless data warehouse! Ask Gemini to optimize for cost or performance Let Gemini explain queries in your native tongue It is truly a new way to Cloud! #Google advocate

Do you dream of unified logging for Azure Data Factory and Databricks Notebooks? Of course you do! Well, S Daniel Zafar has done most of the heavy lifting for you, showing how Azure Log Analytics can bring all of your logs into one place for easy debugging and analysis. https://lnkd.in/eHrBF2GM #databricks #azureloganalytics #azuredatafactory

Delta Lake liquid clustering replaces table partitioning and ZORDER to simplify data layout decisions and optimize query performance. Please find the below details to go through the same. Practical usage of Liquid clustering looks promising and shows good performance compared to partitioning and ZORDER. For further reading , please go through below links. Databricks Liquid clustering GA : https://lnkd.in/gZaKcqRx How to implement it on Azure: https://lnkd.in/gfpftFix How to implement it on AWS: https://lnkd.in/gGbmHXhD How to implement it on GCP: https://lnkd.in/g9YgD_KD Announcement of Delta 4.0 with Liquid clustering: https://lnkd.in/gbHfgA7k