Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Azure Synapse Analytics Cookbook
  • Toc
  • feedback
Azure Synapse Analytics Cookbook

Azure Synapse Analytics Cookbook

By : Agarwal(BLR), Muralidharan
4.7 (18)
close
Azure Synapse Analytics Cookbook

Azure Synapse Analytics Cookbook

4.7 (18)
By: Agarwal(BLR), Muralidharan

Overview of this book

As data warehouse management becomes increasingly integral to successful organizations, choosing and running the right solution is more important than ever. Microsoft Azure Synapse is an enterprise-grade, cloud-based data warehousing platform, and this book holds the key to using Synapse to its full potential. If you want the skills and confidence to create a robust enterprise analytical platform, this cookbook is a great place to start. You'll learn and execute enterprise-level deployments on medium-to-large data platforms. Using the step-by-step recipes and accompanying theory covered in this book, you'll understand how to integrate various services with Synapse to make it a robust solution for all your data needs. Whether you're new to Azure Synapse or just getting started, you'll find the instructions you need to solve any problem you may face, including using Azure services for data visualization as well as for artificial intelligence (AI) and machine learning (ML) solutions. By the end of this Azure book, you'll have the skills you need to implement an enterprise-grade analytical platform, enabling your organization to explore and manage heterogeneous data workloads and employ various data integration services to solve real-time industry problems.
Table of Contents (11 chapters)
close

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:

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete