MCA Microsoft Certified Associate Azure Data Engineer Study Guide : Exam DP-203.

Format:

Book

Author/Creator:

Perkins, Benjamin.

Series:

Sybex Study Guide Series

Language:

English

Subjects (All):

Microsoft Azure (Computing platform)--Examinations--Study guides.

Microsoft Azure (Computing platform).

Database management--Examinations--Study guides.

Database management.

Physical Description:

1 online resource (1011 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2023.

Summary:

Prepare for the Azure Data Engineering certification--and an exciting new career in analytics--with this must-have study aide In the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203, accomplished data engineer and tech educator Benjamin Perkins delivers a hands-on, practical guide to preparing for the challenging Azure Data Engineer certification and for a new career in an exciting and growing field of tech. In the book, you'll explore all the objectives covered on the DP-203 exam while learning the job roles and responsibilities of a newly minted Azure data engineer. From integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions, you'll get up to speed quickly and efficiently with Sybex's easy-to-use study aids and tools. This Study Guide also offers: Career-ready advice for anyone hoping to ace their first data engineering job interview and excel in their first day in the field Indispensable tips and tricks to familiarize yourself with the DP-203 exam structure and help reduce test anxiety Complimentary access to Sybex's expansive online study tools, accessible across multiple devices, and offering access to hundreds of bonus practice questions, electronic flashcards, and a searchable, digital glossary of key terms A one-of-a-kind study aid designed to help you get straight to the crucial material you need to succeed on the exam and on the job, the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203 belongs on the bookshelves of anyone hoping to increase their data analytics skills, advance their data engineering career with an in-demand certification, or hoping to make a career change into a popular new area of tech.

Contents:

Cover Page

Title Page

Copyright Page

Acknowledgments

About the Author

About the Technical Editor

Contents at a Glance

Contents

Table of Exercises

Introduction

Part I Azure Data Engineer Certification and Azure Products

Chapter 1 Gaining the Azure Data Engineer Associate Certification

The Journey to Certification

How to Pass Exam DP-203

Understanding the Exam Expectations and Requirements

Use Azure Daily

Read Azure Articles to Stay Current

Have an Understanding of All Azure Products

Azure Product Name Recognition

Azure Data Analytics

Azure Synapse Analytics

Azure Databricks

Azure HDInsight

Azure Analysis Services

Azure Data Factory

Azure Event Hubs

Azure Stream Analytics

Other Products

Azure Storage Products

Azure Data Lake Storage

Azure Storage

Azure Databases

Azure Cosmos DB

Azure SQL Server Products

Additional Azure Databases

Azure Security

Azure Active Directory

Role-Based Access Control

Attribute-Based Access Control

Azure Key Vault

Azure Networking

Virtual Networks

Azure Compute

Azure Virtual Machines

Azure Virtual Machine Scale Sets

Azure App Service Web Apps

Azure Functions

Azure Batch

Azure Management and Governance

Azure Monitor

Azure Purview

Azure Policy

Azure Blueprints (Preview)

Azure Lighthouse

Azure Cost Management and Billing

Summary

Exam Essentials

Review Questions

Chapter 2 CREATE DATABASE dbName

The Brainjammer

A Historical Look at Data

Variety

Velocity

Volume

Data Locations

Data File Formats

Data Structures, Types, and Concepts

Data Structures

Data Types and Management

Data Concepts

Data Programming and Querying for Data Engineers.

Data Programming

Querying Data

Understanding Big Data Processing

Big Data Stages

ETL, ELT, ELTL

Analytics Types

Big Data Layers

Part II Design and Implement Data Storage

Chapter 3 Data Sources and Ingestion

Where Does Data Come From?

Design a Data Storage Structure

Design an Azure Data Lake Solution

Recommended File Types for Storage

Recommended File Types for Analytical Queries

Design for Efficient Querying

Design for Data Pruning

Design a Folder Structure That Represents the Levels of Data Transformation

Design a Distribution Strategy

Design a Data Archiving Solution

Design a Partition Strategy

Design a Partition Strategy for Files

Design a Partition Strategy for Analytical Workloads

Design a Partition Strategy for Efficiency and Performance

Design a Partition Strategy for Azure Synapse Analytics

Identify When Partitioning Is Needed in Azure Data Lake Storage Gen2

Design the Serving/Data Exploration Layer

Design Star Schemas

Design Slowly Changing Dimensions

Design a Dimensional Hierarchy

Design a Solution for Temporal Data

Design for Incremental Loading

Design Analytical Stores

Design Metastores in Azure Synapse Analytics and Azure Databricks

The Ingestion of Data into a Pipeline

Event Hubs and IoT Hub

Apache Kafka for HDInsight

Migrating and Moving Data

Chapter 4 The Storage of Data

Implement Physical Data Storage Structures

Implement Compression

Implement Partitioning

Implement Sharding

Implement Different Table Geometries with Azure Synapse Analytics Pools

Implement Data Redundancy

Implement Distributions.

Implement Data Archiving

Azure Synapse Analytics Develop Hub

Implement Logical Data Structures

Build a Temporal Data Solution

Build a Slowly Changing Dimension

Build a Logical Folder Structure

Build External Tables

Implement File and Folder Structures for Efficient Querying and Data Pruning

Implement a Partition Strategy

Implement a Partition Strategy for Files

Implement a Partition Strategy for Analytical Workloads

Implement a Partition Strategy for Streaming Workloads

Implement a Partition Strategy for Azure Synapse Analytics

Design and Implement the Data Exploration Layer

Deliver Data in a Relational Star Schema

Deliver Data in Parquet Files

Maintain Metadata

Implement a Dimensional Hierarchy

Create and Execute Queries by Using a Compute Solution That Leverages SQL Serverless and Spark Cluster

Recommend Azure Synapse Analytics Database Templates

Implement Azure Synapse Analytics Database Templates

Additional Data Storage Topics

Storing Raw Data in Azure Databricks for Transformation

Storing Data Using Azure HDInsight

Storing Prepared, Trained, and Modeled Data

Part III Develop Data Processing

Chapter 5 Transform, Manage, and Prepare Data

Ingest and Transform Data

Transform Data Using Azure Synapse Pipelines

Transform Data Using Azure Data Factory

Transform Data Using Apache Spark

Transform Data Using Transact-SQL

Transform Data Using Stream Analytics

Cleanse Data

Split Data

Shred JSON

Encode and Decode Data

Configure Error Handling for the Transformation

Normalize and Denormalize Values

Transform Data by Using Scala

Perform Exploratory Data Analysis

Transformation and Data Management Concepts

Transformation

Data Management

Data Modeling and Usage.

Data Modeling with Machine Learning

Usage

Chapter 6 Create and Manage Batch Processing and Pipelines

Design and Develop a Batch Processing Solution

Design a Batch Processing Solution

Develop Batch Processing Solutions

Create Data Pipelines

Handle Duplicate Data

Handle Missing Data

Handle Late-Arriving Data

Upsert Data

Configure the Batch Size

Configure Batch Retention

Design and Develop Slowly Changing Dimensions

Design and Implement Incremental Data Loads

Integrate Jupyter/IPython Notebooks into a Data Pipeline

Revert Data to a Previous State

Handle Security and Compliance Requirements

Design and Create Tests for Data Pipelines

Scale Resources

Design and Configure Exception Handling

Debug Spark Jobs Using the Spark UI

Implement Azure Synapse Link and Query the Replicated Data

Use PolyBase to Load Data to a SQL Pool

Read from and Write to a Delta Table

Manage Batches and Pipelines

Trigger Batches

Schedule Data Pipelines

Validate Batch Loads

Implement Version Control for Pipeline Artifacts

Manage Data Pipelines

Manage Spark Jobs in a Pipeline

Handle Failed Batch Loads

Chapter 7 Design and Implement a Data Stream Processing Solution

Develop a Stream Processing Solution

Design a Stream Processing Solution

Create a Stream Processing Solution

Process Time Series Data

Design and Create Windowed Aggregates

Process Data Within One Partition

Process Data Across Partitions

Handle Schema Drift

Configure Checkpoints/Watermarking During Processing

Replay Archived Stream Data

Monitor for Performance and Functional Regressions.

Optimize Pipelines for Analytical or Transactional Purposes

Handle Interruptions

Transform Data Using Azure Stream Analytics

Monitor Data Storage and Data Processing

Monitor Stream Processing

Part IV Secure, Monitor, and Optimize Data Storage and Data Processing

Chapter 8 Keeping Data Safe and Secure

Design Security for Data Policies and Standards

Design a Data Auditing Strategy

Design a Data Retention Policy

Design for Data Privacy

Design to Purge Data Based on Business Requirements

Design Data Encryption for Data at Rest and in Transit

Design Row-Level and Column-Level Security

Design a Data Masking Strategy

Design Access Control for Azure Data Lake Storage Gen2

Implement Data Security

Implement a Data Auditing Strategy

Manage Sensitive Information

Implement a Data Retention Policy

Encrypt Data at Rest and in Motion

Implement Row-Level and Column-Level Security

Implement Data Masking

Manage Identities, Keys, and Secrets Across Different Data Platform Technologies

Implement Access Control for Azure Data Lake Storage Gen2

Implement Secure Endpoints (Private and Public)

Implement Resource Tokens in Azure Databricks

Load a DataFrame with Sensitive Information

Write Encrypted Data to Tables or Parquet Files

Develop a Batch Processing Solution

Browse and Search Metadata in Microsoft Purview Data Catalog

Push New or Updated Data Lineage to Microsoft Purview

Chapter 9 Monitoring Azure Data Storage and Processing

Monitoring Data Storage and Data Processing.

Implement Logging Used by Azure Monitor.

Notes:

Description based on publisher supplied metadata and other sources.

Includes index.

ISBN:

9781119885436

1119885434

9781119885443

1119885442

OCLC:

1392402601