Mastering Azure Analytics Architecting in the Cloud with Azure Data Lake HDInsight and Spark 1st Edition by Zoiner Tejada ISBN 1491956607 9781491956601

Mastering Azure Analytics Architecting in the Cloud with Azure Data Lake HDInsight and Spark 1st Edition by Zoiner Tejada – Ebook PDF Instant Download/Delivery: 1491956607, 978-1491956601
Full download Mastering Azure Analytics Architecting in the Cloud with Azure Data Lake HDInsight and Spark 1st Edition after payment

Product details:

ISBN 10:    1491956607
ISBN 13: 978-1491956601
Author:      Zoiner Tejada

Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution.

You’ll not only be able to determine which service best fits the job, but also learn how to implement a complete solution that scales, provides human fault tolerance, and supports future needs.

• Understand the fundamental patterns of the data lake and lambda architecture
• Recognize the canonical steps in the analytics data pipeline and learn how to use Azure Data Factory to orchestrate them
• Implement data lakes and lambda architectures, using Azure Data Lake Store, Data Lake Analytics, HDInsight (including Spark), Stream Analytics, SQL Data Warehouse, and Event Hubs
• Understand where Azure Machine Learning fits into your analytics pipeline
• Gain experience using these services on real-world data that has real-world problems, with scenarios ranging from aviation to Internet of Things (IoT)

Mastering Azure Analytics Architecting in the Cloud with Azure Data Lake HDInsight and Spark 1st Table of contents:

1. Enterprise Analytics Fundamentals

• The Analytics Data Pipeline: Understanding the steps of getting data from collection to insight.
• Data Lakes, Lambda, and Kappa Architectures: Exploring data storage and processing approaches in real-time and batch environments.
• Choosing Between Lambda and Kappa: Decision criteria for different architectures.
• The Azure Analytics Pipeline: Understanding Azure’s ecosystem for handling large-scale data.
• Example Code and Data: Practical examples for hands-on learning.

2. Getting Data into Azure

• Ingesting Data: Different methods like bulk data loading, stream loading, and event-driven ingestion using Event Hubs.
• Network-Oriented and End User Tools: Different tools for accessing and loading data into Azure.

3. Storing Ingested Data in Azure

• Storage Options: Blob Storage, Azure Data Lake Store, HDFS, and queue-oriented storage (e.g., Event Hubs and IoT Hub).
• Blue Yonder Scenario (Smart Buildings): Real-world example for data storage in Azure.

4. Real-Time Processing in Azure

• Stream Processing: Handling real-time data from Event Hubs using tools like HDInsight (Apache Storm) and Azure Machine Learning.
• Tuple-at-a-Time Processing: Processing data as it arrives.

5. Real-Time Micro-Batch Processing in Azure

• Micro-Batch Processing: Processing data in small, frequent intervals using technologies like Spark Streaming and Azure Stream Analytics.

6. Batch Processing in Azure

• Batch Processing with HDInsight: Using MapReduce, Hive, Pig, and Spark on HDInsight for large-scale batch processing.
• Using Data Lake Analytics: Utilizing U-SQL to process large data sets.
• Azure Data Factory: Orchestrating batch processing pipelines.

7. Interactive Querying in Azure

• Interactive Querying: Exploring data with SQL Data Warehouse, Hive with Tez, Spark SQL, and U-SQL.
• Indexes, Partitions: Key techniques for optimizing data queries.

8. Hot and Cold Path Serving Layer in Azure

• Data Serving Layers: Using technologies like Redis, Document DB, SQL Database, SQL Data Warehouse, and HBase for serving data in both real-time and batch contexts.

9. Intelligence and Machine Learning

• Azure Machine Learning: Integrating machine learning models in the pipeline.
• R Services: Using R on HDInsight and SQL for advanced analytics.
• Microsoft Cognitive Services: Applying AI capabilities to your data.

10. Managing Metadata in Azure

• Azure Data Catalog: Organizing and managing metadata to make data assets discoverable.
• Adding Assets to the Data Catalog: Practical tasks like adding Azure Data Lake Store assets, blobs, or SQL Data Warehouse.

11. Protecting Your Data in Azure

• Identity and Access Management: Controlling access to your data and services.
• Data Protection and Auditing: Ensuring data security and compliance.

12. Performing Analytics

• Analytics with Power BI: Using Power BI for real-time and batch reporting.
• Real-Time Analytics: Power BI integration with live data streams in scenarios like Blue Yonder.
• Batch Analytics: Reporting with Power BI on batch data processing results.

People also search for Mastering Azure Analytics Architecting in the Cloud with Azure Data Lake HDInsight and Spark 1st:

mastering azure synapse analytics pdf
what is azure text analytics
mastering azure security
microsoft azure master data management
ozone mastering assistant

Tags:

Zoiner Tejada,Mastering,Azure,Analytics,Architecting,Cloud,Azure,Data,Lake,HDInsight,Spark 1st