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Azure Synapse Analytics Cookbook

You're reading from Azure Synapse Analytics Cookbook Implement a limitless analytical platform using effective recipes for Azure Synapse

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781803231501

Length 238 pages

Edition 1st Edition

Languages

PySpark

Tools

Azure

Concepts

Data Science

Authors (2):

Gaurav Agarwal(BLR)

Meenakshi Muralidharan

View More author details

Table of Contents (11) Chapters

Preface

1. Chapter 1: Choosing the Optimal Method for Loading Data to Synapse

2. Chapter 2: Creating Robust Data Pipelines and Data Transformation FREE CHAPTER

3. Chapter 3: Processing Data Optimally across Multiple Nodes

4. Chapter 4: Engineering Real-Time Analytics with Azure Synapse Link Using Cosmos DB

5. Chapter 5: Data Transformation and Processing with Synapse Notebooks

6. Chapter 6: Enriching Data Using the Azure ML AutoML Regression Model

7. Chapter 7: Visualizing and Reporting Petabytes of Data

8. Chapter 8: Data Cataloging and Governance

9. Chapter 9: MPP Platform Migration to Synapse

10. Other Books You May Enjoy

Exploring data with ADLS Gen2 to pandas DataFrame in Synapse notebook

In this recipe, we will learn how to create a Synapse Analytics workspace and create Synapse notebooks so that we can load data from an ADLS Gen2 Parquet file to a pandas DataFrame. Synapse notebooks are required for us to perform a detailed analysis of data in interactive session mode.

Getting ready

We will be using a public dataset for our scenario. This dataset will consist of New York yellow taxi trip data; this includes attributes such as trip distances, itemized fares, rate types, payment types, pick-up and drop-off dates and times, driver-reported passenger counts, and pick-up and drop-off locations. We will be using this dataset throughout this recipe to demonstrate various use cases:

To get the dataset, you can go to the following URL: https://www.kaggle.com/microize/newyork-yellow-taxi-trip-data-2020-2019.
The code for this recipe can be downloaded from the GitHub repository: https://github...

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Authors (2)

Agarwal(BLR)

Agarwal(BLR)

Gaurav Agarwal is a cloud solution architect at Microsoft India Corp. Ltd, working closely with Microsoft clients on Azure data and AI, Azure ML, big data, IoT, and Power BI. He has extensive experience in architecting and transforming data solutions for the modern cloud, and expertise in building large-scale enterprise data warehouse solutions.

See other products by Agarwal(BLR)

Muralidharan

Muralidharan

Meenakshi Muralidharan leads modern data platforms and is a renowned chief architect for an Indian multinational information technology services and consulting company. She has extensive experience in building large scale data platforms and applications in Microsoft Azure.

See other products by Muralidharan

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