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Month: August 2022

Synapse Fundamentals for Tuning – Indexes Part1

Posted on August 31, 2022September 28, 2022 by Daniel Crawford

If you have been around SQL Server for any length of time, you know by now that indexes are critical for performance.  In Synapse Dedicated SQL Pools, indexes play a lesser role in query tuning because they do not impact the DSQL plans but rather impact the SQL plans on each of the distributions for…

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Synapse Fundamentals for Performance Tuning – Distribution

Posted on August 12, 2022September 28, 2022 by Daniel Crawford

If you are coming from a SQL Server world, what are the fundamental concepts in Synapse that are essential to building a performant Dedicated SQL Pool?  How does Synapse differ from regular SQL Server?  Let’s start with the most basic foundational principle of distribution.  You might hear the term “MPP” thrown around when we talk…

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Synapse Fundamentals for Performance Tuning – Statistics

Posted on August 12, 2022December 9, 2022 by Daniel Crawford

Statistics in Synapse Dedicated SQL Pools is without question the most important and the most frequently overlooked reason for poor query performance.  SQL Server is forgiving in that autocreate and autoupdate statistics are frequently enabled but Synapse only offers autocreate statistics.   Autoupdate statistics is not an option available in Synapse today.  This leads to a…

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Synapse Dedicated SQL Pool Execution Plans

Posted on August 4, 2022August 12, 2022 by Daniel Crawford

Synapse execution plans are specifically for distributed SQL (DSQL) and are not the same as SQL Server execution plans.  SQL Server execution plans show the types of join patterns (hash, nested, loop), bookmark lookups, sorts, etc…  Whereas Synapse execution plans show you how data must move between the different distributions of a Synapse Dedicated SQL…

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Recent Synapse Videos

Microsoft Fabric is a SaaS platform that lets you easily connect, process, and visualize data, all within a single, secure environment. Instead of maintaining your own security infrastructure, Fabric takes care of it for you, leveraging Microsoft’s resources and expertise to keep your data protected, patched, monitored, and compliant. 

By moving your data to the cloud and working with analytics tools like Power BI, Data Factory, and the next-generation Synapse, you get built-in encryption for data at rest and in transit, along with disaster recovery options. This means you can confidently focus on building data solutions without worrying about the underlying security. 

 

🎙 Meet the Speakers: 

👤 Guest from Product Group: Santhosh Kumar Ravindran, Senior Product Manager 

 

Santhosh Ravindran currently leads Spark Compute and Settings for Microsoft Fabric Spark. He focuses on building capabilities that meet the data engineering needs like Spark Pools, Queueing and Scheduling, and Job orchestration for big data workloads in large enterprises using Spark. Prior to this, Santhosh was a Product Manager and Engineering Lead building Metadata scanners, access policy orchestration, lineage and data catalog systems for enterprise data estates as part of the Microsoft Governance Platform (Microsoft Purview). 

 

LinkedIn:  https://www.linkedin.com/in/thisissanthoshkumar/   

Twitter:  https://www.twitter.com/iamsanthoshkr  

 

 

👤 Host: Estera Kot, Principal Product Manager at Microsoft. 

LinkedIn: https://www.linkedin.com/in/esterakot/ 
Twitter: https://twitter.com/estera_kot 

 

👍 Liked this video? Don't forget to hit the 'Like' button and share it with your peers!
Microsoft Fabric Product Group presents: Security in Fabric Data Engineering
Fabric Apache Spark Diagnostic Emitter for Logs and Metrics is now in public preview. This new feature allows Apache Spark users to collect Spark logs, job events, and metrics from their Spark applications and send them to various destinations, including Azure Event Hubs, Azure Storage, and Azure Log Analytics. It provides robust support for monitoring and troubleshooting Spark applications, enhancing your visibility into application performance. 

 What Does the Diagnostic Emitter Do? 

The Fabric Apache Spark Diagnostic Emitter enables Apache Spark applications to emit critical logs and metrics that can be used for real-time monitoring, analysis, and troubleshooting. Whether you’re sending logs to Azure Event Hubs, Azure Storage, or Azure Log Analytics, this emitter simplifies the process, allowing you to collect data seamlessly and store it in your preferred destinations.  

 

Blog post: https://blog.fabric.microsoft.com/en-US/blog/announcing-the-fabric-apache-spark-diagnostic-emitter-collect-logs-and-metrics/  

 

🎙 Meet the Speakers: 

👤 Guest Expert: Jenny Jiang, Principal Product Manager | Fabric Data Engineering 

LinkedIn: https://www.linkedin.com/in/jenny-jiang-8b57036/ 

👤 Host: Estera Kot, PhD, Principal Product Manager at Microsoft. 

LinkedIn: https://www.linkedin.com/in/esterakot/ 

Twitter: https://twitter.com/estera_kot 

👍 Liked this video? Don't forget to hit the 'Like' button and share it with your peers!
Microsoft Fabric Spark Diagnostic Emitter for Logs and Metrics
The T-SQL notebook feature in Microsoft Fabric lets you write and run T-SQL code within a notebook. You can use T-SQL notebooks to manage complex queries and write better markdown documentation. It also allows direct execution of T-SQL on connected warehouse or SQL analytics endpoint. By adding a Data Warehouse or SQL analytics endpoint to a notebook, T-SQL developers can run queries directly on the connected endpoint. BI analysts can also perform cross-database queries to gather insights from multiple warehouses and SQL analytics endpoints. 

🎙 Meet the Speakers: 

👤 Guest from Microsoft Fabric Product Group: Qixiao Wang, Principal Program Manager in Microsoft | Fabric Spark 

Linkedin: https://www.linkedin.com/in/%E5%90%AF%E9%9C%84-%E7%8E%8B-24368233/  

 

👤 Host: Estera Kot, Principal Product Manager at Microsoft. 

LinkedIn: https://www.linkedin.com/in/esterakot/ 
Twitter: https://twitter.com/estera_kot 

 

👍 Like this video? Don't forget to hit the 'Like' button and share it with your network! 

🔔 Stay Updated: For more insights into Microsoft Fabric Data Engineering and Data Science, and all things tech, make sure to subscribe to our channel and hit the notification bell so you never miss an episode!
Microsoft Fabric Product Group presents T-SQL support in Microsoft Fabric Notebooks
AutoML (Automated Machine Learning) is a collection of methods and tools that automate machine learning model training and optimization with little human involvement. The aim of AutoML is to simplify and speed up the process of choosing the best machine learning model and hyperparameters for a given dataset, which usually demands much skill and computing power. 

  

🎙 Meet the Speakers: 

👤 Guest from Microsoft Fabric Product Group: Misha Desai, Senior Program Manager  

 

Misha is a Senior Product Manager based in Seattle, WA, specializing in model tracking, training, and governance within the Fabric Data Science team. 

Linkedin: www.linkedin.com/in/misha-desai-6034a362 

 

👤 Host: Estera Kot, Senior Product Manager at Microsoft and a member of the Fabric Product Group. She holds the role of Product Owner for Apache Spark-based runtimes in Microsoft Fabric and Synapse Analytics. Estera is a Data & AI Architect and is passionate about computer science. 
LinkedIn: https://www.linkedin.com/in/esterakot/ 
Twitter: https://twitter.com/estera_kot 

🔔 Stay Updated: For more insights into Microsoft Fabric Data Engineering and Data Science, and all things tech, make sure to subscribe to our channel and hit the notification bell so you never miss an episode!
Low Code AutoML UI in Microsoft Fabric Data Science
With high concurrency mode, we’re bringing a game-changing way to orchestrate your data ingestion and transformation processes in enterprise workflows. Notebooks in pipelines now leverage shared, high-performance sessions, combining speed with cost-efficiency—all while maintaining top-tier security. 

 Imagine a pipeline with five notebooks, each running 5 minutes. Normally, the 3-minute Spark start time per step would push your pipeline to 40 minutes. With high concurrency, the total runtime drops to 28 minutes—a 30% speed improvement. 

Unlock faster workflows, lower costs, and a seamless data journey with high concurrency mode. Get ready to experience the next level of pipeline orchestration! 🎉 

https://blog.fabric.microsoft.com/en-US/blog/introducing-high-concurrency-mode-for-notebooks-in-pipelines-for-fabric-spark/  

🎙 Meet the Speakers: 

👤 Guest from Product Group: Santhosh Kumar Ravindran, Senior Product Manager 

 

Santhosh Ravindran currently leads Spark Compute and Settings for Microsoft Fabric Spark. He focuses on building capabilities that meet the data engineering needs like Spark Pools, Queueing and Scheduling, and Job orchestration for big data workloads in large enterprises using Spark. Prior to this, Santhosh was a Product Manager and Engineering Lead building Metadata scanners, access policy orchestration, lineage and data catalog systems for enterprise data estates as part of the Microsoft Governance Platform (Microsoft Purview). 

 

LinkedIn:  https://www.linkedin.com/in/thisissanthoshkumar/   

Twitter:  https://www.twitter.com/iamsanthoshkr  

 

 

👤 Host: Estera Kot, Principal Product Manager at Microsoft. 

LinkedIn: https://www.linkedin.com/in/esterakot/ 
Twitter: https://twitter.com/estera_kot 

 

👍 Liked this video? Don't forget to hit the 'Like' button and share it with your peers!
High Concurrency Mode for Notebooks in Pipelines for Fabric Spark
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