Beginning Azure Synapse Analytics: Transition from Data Warehouse to Data Lakehouse
Bhadresh Shiyal
Résumé
The book begins with an introduction to core data and analytics concepts followed by an understanding of traditional/legacy data warehouse, modern data warehouse, and the most modern data lakehouse. You will go through the introduction and background of Azure Synapse Analytics along with its main features and key service capabilities. Core architecture is discussed, along with Synapse SQL. You will learn its main features and how to create a dedicated Synapse SQL pool and analyze your big data using Serverless Synapse SQL Pool. You also will learn Synapse Spark and Synapse Pipelines, with examples. And you will learn Synapse Workspace and Synapse Studio followed by Synapse Link and its features. You will go through use cases in Azure Synapse and understand the reference architecture for Synapse Analytics.
After reading this book, you will be able to work with Azure Synapse Analytics and understand its architecture, main components, features, and capabilities.
What You Will Learn
- Understand core data and analytics concepts and data lakehouse concepts
- Be familiar with overall Azure Synapse architecture and its main components
- Be familiar with Synapse SQL and Synapse Spark architecture components
- Work with integrated Apache Spark (aka Synapse Spark) and Synapse SQL engines
- Understand Synapse Workspace, Synapse Studio, and Synapse Pipeline
- Study reference architecture and use cases
Who This Book Is For
Azure data analysts, data engineers, data scientists, and solutions architects
Chapter Goal: Introducing readers to some of the important core data and analytics concepts as a foundation
No of pages : 15
Sub -Topics
1. Introduction/Background
2. Core Data Concepts
1. Structured Data
2. Unstructured Data
3. Semi-Structured Data
4. Batch Data
5. Streaming Data
6. Difference between Streaming Data and Batch Data
7. Relational Data and its characteristics
3. Core Analytics Concepts
1. Data Ingestion
1. ELT
2. ETL
2. Data Processing
3. Data Exploration
4. Data Visualization
5. Analytics Techniques
1. Descriptive
2. Diagnostic
3. Predictive
4. Prescriptive
5. Cognitive
4. Summary
Chapter 2: Modern Data Warehouse and Data LakehouseChapter Goal: Providing conceptual understanding about traditional / legacy Data Warehouse, Modern Data Warehouse and finally the most modern Data Lakehouse.
No of pages : 25
Sub - Topics
1. Introduction/Background
2. What is Data Warehouse?
3. Why do we need a Data Warehouse?
4. What is Modern Data Warehouse?
5. Comparison between Traditional Data Warehouse and Modern Data Warehouse?
6. What is Data Lakehouse?
7. Comparison between Data Warehouse and Data Lakehouse
8. Benefits of Data Lakehouse
9. Examples of Data Lakehouse
10. Summary
Chapter 3: Introduction to Azure Synapse AnalyticsChapter Goal: Building foundational knowledge by introducing Azure Synapse Analytics, its main features and its key services capabilities
No of pages : 20
Sub - Topics:
1. Introduction/Background
2. What is Azure Synapse Analytics?
3. Azure Synapse Analytics vs Azure SQL Datawarehouse
4. Why should you learn Azure Synapse Analytics?
5. Main Features
1. Unified Experience
2. Powerful Insights
3. Limitless Scale
4. Instant Clarity
5. Security and Privacy
6. Key Services Capabilities
1. EDW
2. Data Lake Exploration
3. Multiple Language Support
4. Low-Code or Code-Free Data Orchestration
5. Integrated Apache Spark and SQL Engines
6. Stream Analytics
7. AI Integration
8. BI Integration
9. Management and Security
7. Summary
Chapter 4: Architecture and its Main ComponentsChapter Goal: Explaining Azure Synapse Analytics Core Architecture and its main components as it is very different from traditional Data Warehouse Architecture and its components.
No of pages : 15
Sub - Topics:
1. Introduction/Background
2. High Level Architecture
3. Main Components of Architecture
1. Synapse SQL
2. Synapse Spark
3. Synapse Pipelines
4. Synapse Studio
5. Synapse Link
4. Summary
Chapter 5: Synapse SQLChapter Goal: Exploring Synapse SQL in detail including its architecture, its main features with some How-Tos to make the readers familiar with some important activities which can be carried out for Synapse SQL
No of pages : 25
Sub - Topics:
1. Background / Introduction
2. Synapse SQL Architecture Components
1. Azure Storage
2. Control Node
3. Compute Node
4. Data Movement Service
5. Distributions
3. Synapse SQL Pool
4. Synapse SQL On-Demand
5. Synapse SQL Features
6. Resource Consumption Models
7. Synapse SQL - Best Practices
8. How-Tos
1. Create an Azure Synapse SQL Pool
2. Create an Azure Synapse SQL On-Demand
3. Load Data using COPY Statement
4. Load data from Azure Data Lake Storage for Synapse SQL
5. Load data by using Azure Data Factory
6. Ingest data into Azure Data Lake Storage Gen2
9. Summary
Chapter 6: Synapse Spark
Chapter Goal: Explaining Synapse Sparks and its main components including Delta Lake along with some How-Tos to make the readers familiar with important tasks pertaining to Synapse Spark.
No of pages : 30
Sub - Topics:
1. Introduction/Background
2. What is Apache Spark
3. Synapse Spark Capabilities
4. What is Delta Lake and its importance in Spark?
5. Synapse Spark Job Optimization
6. Development Libraries
7. Apache Spark Machine Learning
8. How-Tos
1. Create Synapse Spark Cluster
2. Load Data using Synapse Spark Cluster
3. Export / Import Data with Apache Spark
9. Summary
Chapter 7: Synapse PipelinesChapter Goal: Introducing Azure Synapse Pipelines and how it integrates with Azure Data Factory. Detailed explanation to various types of Pipeline activities with examples.
No of pages: 20
Sub - Topics:
1. Introduction / Background
2. Overview of Azure Data Factory
3. Data Movement Activities
4. Data Transformation Activities
5. Control Flow Activities
6. Copy Pipeline Example
7. Transformation Pipeline Example
8. Scheduling Pipelines
9. SummaryChapter 8: Synapse Workspace and Synapse Studio
Chapter Goal: To make readers familiar with Synapse Workspace and Synapse Studio including its main features and its capabilities and to give understanding about how to accomplish some important tasks using workspace and studio.
No of pages : 25
Sub - Topics:
1. What is Synapse Workspace?
2. Workspace Components and Features
3. What is Synapse Studio?
4. Main Features
5. Capabilities (What it can do?)
6. Linking Power BI to Synapse Studio
7. How-To carry out important activities using Studio
8. Summary
Chapter 9: Synapse Link
Chapter Goal: To explain differences between OLTP and OLAP, why HTAP is required and its benefits and then introducing Synapse Link along with its Cosmos DB integration, its features and use cases.
No of pages : 20
Sub - Topics:
1. Introduction / Background
2. OLTP vs OLAP
3. What is HTAP?
4. HTAP Benefits
5. What is Azure Synapse Link?
6. Azure Cosmos DB Analytical Store
7. Synapse Link Features
8. Synapse Link Use Cases
9. Summary
Chapter 10: Azure Synapse Use Cases and Reference Architectures
Chapter Goal: To make readers familiar with Synapse Workspace and Synapse Studio including its main features and its capabilities and to give understanding about how to accomplish some important tasks using workspace and studio.
No of pages : 15
Sub - Topics:
1. Introduction / Background
2. Where you should use Synapse Analytics?
3. Where it should not be used?
4. Few Examples of Use cases of Synapse Analytics
5. Reference Architecture for Synapse Analytics
6. Summary
He has worked on various technologies, including Visual Basic, SQL Server, SharePoint Technologies, .NET MVC, O365, Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage Gen1/Gen2, Azure SQL Data Warehouse, Power BI, Spark SQL, Scala, Delta Lake, Azure Machine Learning, Azure Information Protection, Azure .NET SDK, Azure DevOps, and more.
He holds multiple Azure certifications that include Microsoft Certified Azure Solutions Architect Expert, Microsoft Certified Azure Data Engineer Associate, Microsoft Certified Azure Data Scientist Associate, and Microsoft Certified Azure Data Analyst Associate.
Bhadresh has worked as Solutions Architect on a large-scale Azure Data Lake implementation project as well as on a data transformation project and on large-scale customized content management systems. He has also worked as Technical Reviewer for the book Data Science using Azure , prior to authoring this book.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Apress |
| Auteur(s) | Bhadresh Shiyal |
| Parution | 16/06/2021 |
| Nb. de pages | 249 |
| EAN13 | 9781484270608 |
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