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In this course, you will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies.
You will begin by understanding the core compute and storage technologies that are used to build an analytical solution. Then you’ll explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. You will also learn how to interactively explore data stored in files in a data lake.
The various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines will be introduced to you. You will also learn the various ways you can transform the data using the same technologies that is used to ingest data.
You will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. As a result you will come to understand the importance of implementing security to ensure that the data is protected at rest or in transit. You’ll will be shown how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.