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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Join this session to learn what Azure Data Explorer (aka Kusto) is, see it in action and get a quick start in this great platform for observational analytics.
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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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Are you able to fully understand the state of your data in your systems? Are you able to evaluate the quality of your data in all processes? In this session you will learn how easy it is to integrate HEDDA.IO into your existing processes, how it can provide you with a continuous view of the quality of your data and how it can inform you directly in the event of errors, warnings or major changes to individual parameters. HEDDA.IO brings data observability to your process and platforms.
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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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Gain insight into how data can be loaded, processed, and uploaded to other environments in a self-service process from a wide variety of environments in a fully controlled and audited manner. Whether SQL Server, Data Lake, Delta Lake, SAP HANA or many other data sources and targets, WOODY.IO helps you implement clear governance processes into your self-service integration processes.
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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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In this session we will look to understand the components and patterns that are required to set your data platform up for success when defining a high-level architecture. At a high-level we will cover areas from ingestion through to transformation and serving and look to identify the key components and architectural considerations required at each stage. During the session we’ll cover areas from security to error handling and metadata-driven execution to give you an understanding of the basics required to put a successful data platform solution together. This session is suitable for anybody that has a basic understanding of the components in the Azure data stack who wants to understand how to put them together to build a best-practice solution.
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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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HEDDA.IO is a central data quality management solution that connects departments, data stewards and data engineers. It helps to easily integrate standardization, cleansing, matching and enrichment tasks into existing processes.
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Be part of a live audience recording of an episode of Knee-Deep in Tech!
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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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