Lots of ADF, lots of data, lots of automation and lots of lessons learned. Backups aren't as useful as they look, dedicated pools have several "interesting" features in the small print, ADF is not always your friend and how to mitigate the sometimes deadly embrace of throughput vs concurrency trade off.
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If you like writing SQL and need a cheap data platform in Azure, this session is for you. This session will describe the architecture for a metadata driven, minimalistic Azure data platform using Synapse Analytics (serverless SQL) and data lake. You will get to know when this works well, and when it does not.
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After this session, you will be able to conceptualize and implement different Power BI refresh scheduling patterns in ADF/Synapse. You’ll learn what limitations in Power BI you can overcome with an “external” orchestration tool as well as the different building blocks available. Finally, we will go through an example implementation as well as a step-by-step demo of how you can set this up yourself.
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Have you ever found yourself at the start of an Azure data engineering project, unsure about what tool to choose? Speak no more! In this session we will discuss three often used data engineering tools on Azure: - Azure Data Factory - Azure Databricks - Azure Synapse Analytics
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The aim of this session is to highlight the process of implementing Azure Synapse Link for Dataverse and to discuss the reality of working with the data as it is incrementally updated in Azure.
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In this session, seasoned data engineer and youtube grumbler Simon Whiteley takes us on a journey through the current industry trends and buzzwords, carving through the hype to get at the underlying ideals.
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This session session aims to give you that context. We'll look at how spark-based engines work and how we can use them within Synapse Analytics. We'll dig into Delta, the underlying file format that enables the Lakehouse, and take a tour of how the Synapse compute engines interact with it. Finally, we'll draw out our whole Lakehouse architecture
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Based on real world experience let’s think about just how far the breadth of our knowledge now needs to reach when starting from nothing and building a complete Microsoft Azure Data Analytics solution.
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Join us to learn why data governance is increasing in popularity and what you need to know about what data governance really is.
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In this session I'll guide you from through a secure reference architecture with Data Factory, Databricks, Data Lake, and Azure Synapse, working together as a secure, fully productionised platform. Each has their own idiosyncrasies, but this session will teach you the options available and the pitfalls to avoid.
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Explore the changes that have been made to Azure Synapse Analytics and how this has changed it’s use over time. We will review how these feature additions have impacted the methods used for transforming, storing, and querying data so that you can evaluate whether it would be a good fit for your data environment.
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Combining data from multiple sources can be performed using a number of different tools including Azure Synapse Integration/Azure Data Factory, Spark and Power BI Data Flows. We will explore which tool or combination of tools to select to best match your data environment.
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Let's take a look at Synapse Link - it's capabilities and restrictions! Synapse Link is Microsoft's new solution to push your OLTP data in near-real time to Azure Synapse for analytical purposes.
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In this demo-rich session you will learn how you can implement a framework for data quality validation and monitoring, spanning your end-to-end data platform including Power BI!
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In this session we will cover how to architect an end-to-end enterpise analytics platform so that we utilize the best of both worlds. The extremely versatile ETL capabilities of Synapse and the powerful data modelling in Power BI.
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SQLBits' has brought members of the Azure Synapse Program Group to Wales to ask YOUR questions. Bring your questions for: Dedicated SQL Pools, Serverless SQL Pools, Spark Pools, Pipelines, Kusto and everything else Synapse related.
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In this session, you will get to learn how to enrich your data in spark tables with models created using Automated ML in Azure Synapse Analytics. We will be looking at a demo where we will create regression and classification models using Automated ML and how to use these models for prediction in Azure Synapse Analytics.
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Are you planning a migrating of your data platform to Azure Synapse Analytics? Is it currently based on Azure Databricks, Azure Data Factory, and Azure Data Lake Storage? Make sure to grab these 10 tips! We'll talk about Spark compatibilities, orchestration pitfalls and solution deployment differences.
This session will walk you through three different ways to set up an affordable Azure based Data Warehouse solution. Covering pros and cons with each of the architectures.
Overview to some of the more popular Azure Data Engineering services used to analyze data
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