Learn azure synapse data explorer a guide to building real-time analytics solutions to unlock log and telemetry data
A hands-on guide to working on use cases helping you ingest, analyze, and serve insightful data from IoT as well as telemetry data sources using Azure Synapse Data Explorer Free PDF included with this book Key Features Augment advanced analytics projects with your IoT and application data Expand you...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai :
Packt Publishing
[2023]
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| Edición: | 1st ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009720738306719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credit
- Dedicated
- Contributors
- Table of Contents
- Preface
- Part 1: Introduction to Azure Synapse Data Explorer
- Chapter 1: Introducing Azure Synapse Data Explorer
- Technical requirements
- Understanding the lifecycle of data
- Introducing the Team Data Science Process
- Tooling and infrastructure
- The need for a fast and highly scalable data exploration service
- What is Azure Synapse?
- Data integration
- Enterprise data warehousing
- Exploration on the data lake
- Apache Spark
- Log and telemetry analytics
- Integrated business intelligence
- Data governance
- Broad support for ML
- Security and Managed Virtual Network
- Management interface
- What is Azure Synapse Data Explorer?
- Integrating Data Explorer pools with other Azure Synapse services
- Query experience integrated into Azure Synapse Studio's query editor
- Exploring, preparing, and modeling data with Apache Spark
- Data ingestion made easy with pipelines
- Unified management experience
- Exploring the Data Explorer pool infrastructure and scalability
- Data Explorer pool architecture
- Scalability of compute resources
- Managing data on distributed clusters
- Mission-critical infrastructure
- How much scale can Data Explorer handle?
- What makes Azure Synapse Data Explorer unique?
- When to use Azure Synapse Data Explorer
- Summary
- Chapter 2: Creating Your First Data Explorer Pool
- Technical requirements
- Creating a free Azure account
- Creating an Azure Synapse workspace
- Basics tab
- Security tab
- Networking tab
- Tags tab
- Review + create tab
- Finding your new workspace
- Creating a Data Explorer pool using Azure Synapse Studio
- Basics tab
- Additional settings tab
- Tags tab
- Review + create tab
- Creating a Data Explorer pool using the Azure portal.
- Creating a Data Explorer pool using the Azure CLI
- Summary
- Chapter 3: Exploring Azure Synapse Studio
- Technical requirements
- Exploring the user interface of Azure Synapse Studio
- Running your first query
- Creating a database
- Loading the data
- Verifying whether your data has loaded successfully
- Working with data in Azure Synapse notebooks
- Saving your work and configuring source control
- Managing and monitoring Data Explorer pools
- Scaling Data Explorer pools
- Pausing and resuming pools
- Monitoring Data Explorer pools
- Summary
- Chapter 4: Real-World Usage Scenarios
- Technical requirements
- Building a multi-purpose end-to-end analytics environment
- Sources
- Ingest
- Store
- Process
- Enrich
- Serve
- User
- Summary
- Managing IoT data
- Processing and analyzing geospatial data
- Enabling real-time analytics with big data
- Performing time series analytics
- Summary
- Part 2: Working with Data
- Chapter 5: Ingesting Data into Data Explorer Pools
- Technical requirements
- Understanding the data loading process
- Defining a retention policy
- Choosing a data load strategy
- Streaming ingestion
- Batching ingestion
- Performing data ingestion
- Using KQL control commands
- Building an Azure Synapse pipeline
- Implementing continuous ingestion
- Using other data ingestion mechanisms
- Summary
- Chapter 6: Data Analysis and Exploration with KQL and Python
- Technical requirements
- Analyzing data with KQL
- Selecting data
- Working with calculated columns
- Plotting charts
- Obtaining percentiles
- Creating a time series
- Detecting outliers
- Using linear regression
- Exploring Data Explorer pool data with Python
- Creating an Apache Spark pool
- Working with Azure Synapse notebooks
- Reading data from Data Explorer pools
- Plotting charts.
- Performing data transformation tasks
- Creating a lake database
- Summary
- Chapter 7: Data Visualization with Power BI
- Technical requirements
- Introduction to the Power BI integration
- Creating a Power BI report
- Adding data sources to your Power BI report
- Connecting Power BI with your Azure Synapse workspace
- Authoring Power BI reports from Azure Synapse Studio
- Summary
- Chapter 8: Building Machine Learning Experiments
- Technical requirements
- Understanding the application of ML
- Introducing ML into your projects with AutoML
- Creating an Azure Machine Learning workspace
- Configuring the Azure Machine Learning integration
- Finding the best model with AutoML
- Exploring additional ML capabilities in Azure Synapse
- Using pre-trained models with Cognitive Services
- Finding patterns using KQL
- Training models with Apache Spark MLlib
- Building applications with SynapseML
- Summary
- Chapter 9: Exporting Data from Data Explorer Pools
- Technical requirements
- Understanding data export scenarios
- Exporting data with client tools
- Using server-side export to pull data
- Performing robust exports with server-side data push
- Exporting to cloud storage
- Exporting to SQL tables
- Exporting to external tables
- Configuring continuous data export
- Summary
- Part 3: Managing Azure Synapse Data Explorer
- Chapter 10: System Monitoring and Diagnostics
- Technical requirements
- Monitoring your environment
- Checking your Data Explorer pool capacity
- Monitoring query execution
- Reviewing object metadata and changes
- Setting up alerts
- Creating action groups
- Creating alert rules
- Summary
- Chapter 11: Tuning and Resource Management
- Technical requirements
- Implementing resource governance with workload groups
- Managing workload groups
- Classifying user requests.
- Queuing requests for delayed execution
- Speeding up queries using cache policies
- Summary
- Chapter 12: Securing Your Environment
- Technical requirements
- Security overview
- Managing data encryption
- Configuring data encryption at rest
- Understanding data encryption in transit
- Authenticating users
- Configuring access to resources
- Synapse RBAC roles
- Reviewing role assignments
- Assigning RBAC roles
- Data Explorer database roles
- Implementing network security
- Using a managed virtual network
- Managed private endpoint connection
- Enabling data exfiltration protection
- Controlling public network access
- Protecting against external threats
- Summary
- Advanced Data Management
- Technical requirements
- Managing extents
- Extent tagging
- Moving extents
- Dropping extents
- Purging personal data
- Enabling purge on Data Explorer pools
- Executing data purge operations
- Monitoring data purge operations
- Summary
- Index
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