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Azure offers a wide range of services that can be combined into a BI solution in the cloud.
This session focuses on the deeper integration of SQL Server Integration Services (SSIS) in Azure Data Factory (ADF) and the broad extensibility of Azure-SSIS Integration Runtime (IR).
In this session we'll look at ETL metadata, use it to drive process execution, and see benefits quickly emerge. I'll show how a metadata-first approach reduces complexity, enhances resilience and allows ETL processing to become self-organising.
We’ll take a look at how to approach making an Azure Databricks based ETL solution from start to finish. Along the way it will become clear how Azure Databricks works and we will use our SSIS knowledge to see if it can handle common use-cases
SQL Server 2019 expands on the Polybase feature from SQL Server 2019 by providing a robust data virtualization solution to reduce the need for ETL and data movement. Come learn how new data connectors work with sources like Oracle, MongoDB, CosmosDB, Terradata, and HDFS.
Getting better performance from your ETL
What data profiling is & why you should do it.
To build analytical models, we need to start by extracting, transforming, cleaning, preparing and loading the data. This session analyzes a set of scenarios that may happen during the ETL step using the Power Query in Power BI.
This session is about using the Business Intelligence Markup Language (Biml) to monitor and control your orchestration patterns. By automatically analyzing the results in ETL logs, we’ll be able to automate our staging orchestration!
How to build a Live Data Warehouse using Change Tracking capabilities
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