Library Hours
Monday to Friday: 9 a.m. to 9 p.m.
Saturday: 9 a.m. to 5 p.m.
Sunday: 1 p.m. to 9 p.m.
Naper Blvd. 1 p.m. to 5 p.m.
LEADER 00000cam a22005651i 4500
003 OCoLC
005 20240129213017.0
006 m d
007 cr |||||||||||
008 210709s2021 enk o 000 0 eng d
015 GBC1B2411|2bnb
016 7 020259547|2Uk
020 9781789618556|q(ebook)
020 178961855X
029 0 UKMGB|b020259547
035 (OCoLC)1268134426
037 9781789618556|bPackt Publishing Pvt. Ltd
037 10162379|bIEEE
040 UKMGB|beng|erda|epn|cUKMGB|dOCLCO|dEBLCP|dOCLCF|dUKAHL
|dOCLCO|dOCLCQ|dIEEEE|dOCLCO
049 INap
082 04 005.7
082 04 005.7|223/eng/20230811
099 eBook O'Reilly for Public Libraries
100 1 Raj, Phani,|eauthor.
245 10 Azure databricks cookbook :|baccelerate and scale real-
time analytics solutions using the apache spark-based
analytics service /|cPhani Raj, Vinod Jaiswal.|h[O'Reilly
electronic resource]
264 1 Birmingham :|bPackt Publishing,|c2021.
300 1 online resource
336 text|2rdacontent
337 computer|2rdamedia
338 online resource|2rdacarrier
505 0 Cover -- Title Page -- Copyright and Credits --
Contributors -- Table of Contents -- Preface -- Chapter 1:
Creating an Azure Databricks Service -- Technical
requirements -- Creating a Databricks workspace in the
Azure portal -- Getting ready -- How to do it... -- How it
works... -- Creating a Databricks service using the Azure
CLI (command-line interface) -- Getting ready -- How to do
it... -- How it works... -- There's more... -- Creating a
Databricks service using Azure Resource Manager (ARM)
templates -- Getting ready -- How to do it... -- How it
works... -- Adding users and groups to the workspace
505 8 Getting ready -- How to do it... -- How it works... --
There's more... -- Creating a cluster from the user
interface (UI) -- Getting ready -- How to do it... -- How
it works... -- There's more... -- Getting started with
notebooks and jobs in Azure Databricks -- Getting ready --
How to do it... -- How it works... -- Authenticating to
Databricks using a PAT -- Getting ready -- How to do it...
-- How it works... -- There's more... -- Chapter 2:
Reading and Writing Data from and to Various Azure
Services and File Formats -- Technical requirements --
Mounting ADLS Gen2 and Azure Blob storage to Azure DBFS --
Getting ready
505 8 How to do it... -- How it works... -- There's more... --
Reading and writing data from and to Azure Blob storage --
Getting ready -- How to do it... -- How it works... --
There's more... -- Reading and writing data from and to
ADLS Gen2 -- Getting ready -- How to do it... -- How it
works... -- Reading and writing data from and to an Azure
SQL database using native connectors -- Getting ready --
How to do it... -- How it works... -- Reading and writing
data from and to Azure Synapse SQL (dedicated SQL pool)
using native connectors -- Getting ready -- How to do
it... -- How it works...
505 8 Reading and writing data from and to Azure Cosmos DB --
Getting ready -- How to do it... -- How it works... --
Reading and writing data from and to CSV and Parquet --
Getting ready -- How to do it... -- How it works... --
Reading and writing data from and to JSON, including
nested JSON -- Getting ready -- How to do it... -- How it
works... -- Chapter 3: Understanding Spark Query Execution
-- Technical requirements -- Introduction to jobs, stages,
and tasks -- Getting ready -- How to do it... -- How it
works... -- Checking the execution details of all the
executed Spark queries via the Spark UI -- Getting ready
505 8 How to do it... -- How it works... -- Deep diving into
schema inference -- Getting ready -- How to do it... --
How it works... -- There's more... -- Looking into the
query execution plan -- Getting ready -- How to do it... -
- How it works... -- How joins work in Spark -- Getting
ready -- How to do it... -- How it works... -- There's
more... -- Learning about input partitions -- Getting
ready -- How to do it... -- How it works... -- Learning
about output partitions -- Getting ready -- How to do
it... -- How it works... -- Learning about shuffle
partitions -- Getting ready -- How to do it... -- How it
works...
520 Get to grips with building and productionizing end-to-end
big data solutions in Azure and learn best practices for
working with large datasets Key Features Integrate with
Azure Synapse Analytics, Cosmos DB, and Azure HDInsight
Kafka Cluster to scale and analyze your projects and build
pipelines Use Databricks SQL to run ad hoc queries on your
data lake and create dashboards Productionize a solution
using CI/CD for deploying notebooks and Azure Databricks
Service to various environments Book DescriptionAzure
Databricks is a unified collaborative platform for
performing scalable analytics in an interactive
environment. The Azure Databricks Cookbook provides
recipes to get hands-on with the analytics process,
including ingesting data from various batch and streaming
sources and building a modern data warehouse. The book
starts by teaching you how to create an Azure Databricks
instance within the Azure portal, Azure CLI, and ARM
templates. You'll work through clusters in Databricks and
explore recipes for ingesting data from sources, including
files, databases, and streaming sources such as Apache
Kafka and EventHub. The book will help you explore all the
features supported by Azure Databricks for building
powerful end-to-end data pipelines. You'll also find out
how to build a modern data warehouse by using Delta tables
and Azure Synapse Analytics. Later, you'll learn how to
write ad hoc queries and extract meaningful insights from
the data lake by creating visualizations and dashboards
with Databricks SQL. Finally, you'll deploy and
productionize a data pipeline as well as deploy notebooks
and Azure Databricks service using continuous integration
and continuous delivery (CI/CD). By the end of this Azure
book, you'll be able to use Azure Databricks to streamline
different processes involved in building data-driven apps.
What you will learn Read and write data from and to
various Azure resources and file formats Build a modern
data warehouse with Delta Tables and Azure Synapse
Analytics Explore jobs, stages, and tasks and see how
Spark lazy evaluation works Handle concurrent transactions
and learn performance optimization in Delta tables Learn
Databricks SQL and create real-time dashboards in
Databricks SQL Integrate Azure DevOps for version control,
deploying, and productionizing solutions with CI/CD
pipelines Discover how to use RBAC and ACLs to restrict
data access Build end-to-end data processing pipeline for
near real-time data analytics Who this book is for This
recipe-based book is for data scientists, data engineers,
big data professionals, and machine learning engineers who
want to perform data analytics on their applications.
Prior experience of working with Apache Spark and Azure is
necessary to get the most out of this book.
588 Description based on CIP data; resource not viewed.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Big data.
650 0 Microsoft Azure (Computing platform)
650 6 Données volumineuses.
650 6 Microsoft Azure (Plateforme informatique)
650 7 Big data|2fast
650 7 Microsoft Azure (Computing platform)|2fast
700 1 Jaiswal, Vinod,|eauthor.
776 08 |iPrint version:|z9781789809718
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781789809718/?ar
|zAvailable at O'Reilly for Public Libraries
938 Askews and Holts Library Services|bASKH|nAH37307265
938 ProQuest Ebook Central|bEBLB|nEBL6724609
994 92|bJFN