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Course Description: DP-3012: Implementing a Data Analytics Solution with Azure Synapse Analytics
This is a course designed to train students on SQL Serverless and Dedicated Spark Pools and provide data cleansing instructions and ELT process using Synapse pipelines, which is very similar to those who are familiar with Azure Data Factory (ADF) to move data to Synapse Dedicated Pool database.
Intermediate - Azure Microsoft Fabric - Administrator Data Engineer
Addressed to
The audience should be familiar with notebooks that use different languages and a Spark engine, such as Databricks, Jupyter Notebooks, Zeppelin notebooks, and more. They should also have some experience with SQL, Python, and Azure tools, such as Data Factory.
Training Route
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Introduction to Azure Synapse Analytics: Learn about the features and capabilities of Azure Synapse Analytics – a cloud-based platform for big data processing and analysis.
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Using Azure Synapse Serverless SQL Pool to Query Files in a Data Lake: With Azure Synapse Serverless SQL Pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.
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Data Analysis with Apache Spark in Azure Synapse Analytics: Apache Spark is one of the core technologies for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.
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Using Delta Lake in Azure Synapse Analytics: Delta Lake is an open-source relational storage area for Spark that you can use to implement a data lake architecture in Azure Synapse Analytics.
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Data Analysis in a Relational Data Warehouse: Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions and are used as the foundation for data models, reporting, and analysis.
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Building a data pipeline in Azure Synapse Analytics: Pipelines are the essence of a data analytics solution. Pipelines are the essence of a data analytics solution.
Prerequisites
Familiarity with Azure services and experience with Azure Machine Learning and Mlflow is recommended. Additionally, you should have experience performing machine learning-related tasks using Python .
Language
- Course: English / Spanish
- Labs: English / Spanish
