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Data Lakehouse in Action

You're reading from   Data Lakehouse in Action Architecting a modern and scalable data analytics platform

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801815932
Length 206 pages
Edition 1st Edition
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Author (1):
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Pradeep Menon Pradeep Menon
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Pradeep Menon
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Table of Contents (14) Chapters Close

Preface 1. PART 1: Architectural Patterns for Analytics
2. Chapter 1: Introducing the Evolution of Data Analytics Patterns FREE CHAPTER 3. Chapter 2: The Data Lakehouse Architecture Overview 4. PART 2: Data Lakehouse Component Deep Dive
5. Chapter 3: Ingesting and Processing Data in a Data Lakehouse 6. Chapter 4: Storing and Serving Data in a Data Lakehouse 7. Chapter 5: Deriving Insights from a Data Lakehouse 8. Chapter 6: Applying Data Governance in the Data Lakehouse 9. Chapter 7: Applying Data Security in a Data Lakehouse 10. PART 3: Implementing and Governing a Data Lakehouse
11. Chapter 8: Implementing a Data Lakehouse on Microsoft Azure 12. Chapter 9: Scaling the Data Lakehouse Architecture 13. Other Books You May Enjoy

Bringing it all together

So far, we have covered the essential elements of batch and stream ingestion and processing. Now, let's bring these two types of processing together to define the Lambda architecture pattern.

Figure 3.12 – Lambda architecture pattern

The preceding diagram depicts a Lambda architecture pattern. A Lambda architecture pattern has three layers: the batch layer, the speed layer, and the serving layer.

The batch layer

The following diagram illustrates batch layer processing in a Lambda architecture:

Figure 3.13 – The batch layer in a Lambda architecture

Batch layer processing consists of ingesting the data into the raw data store of the data lake using pull or push methodologies through a batch data ingestion service. Once the data has been ingested in the raw data store, a batch processing service is initiated. The batch processing service employs a distributed computing engine for faster...

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