free download! Databricks Data Insights: Getting Started to Practice

Introduction: This book helps readers get started with the data lake Lakehouse and some spark-related applications from the introduction of technical foundations to the application practice of scenarios.

As we all know, Databricks dominates many popular technologies such as Apache Spark, Delta Lake, and ML Flow in the open source big data community, and Delta Lake, as the core storage engine solution for data lakes, brings many advantages to enterprises.

From the introduction of technical basics to the application practice of scenarios, this book helps readers get started with the data lake Lakehouse and some spark-related applications.

Basics

Analyze the application advantages of Lakehouse architecture and Delta Lake from the evolution of big data platform architecture, key features and implementation principles of Delta Lake, as well as the advantages and disadvantages of data warehouses and data lakes, and the application of integrated lake and warehouse architecture. It also introduces the core features of the community version of Delta Lake, the design idea of the Lakehouse search engine, and discusses how it achieves superior processing performance.

Application

For stream-batch integrated data warehouse, real-time data entry and analysis, retail demand forecasting, marketing effect attribution analysis, machine learning model training and deployment and other scenarios, explain in detail how to apply Delta Lake, spark, and MLflow to actual usage scenarios , generate business value.

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Databricks Data Insights: Getting Started to Practice

Great sneak peek:

Basics

1. Databricks Data Insights - Enterprise-level fully managed Spark big data analysis platform

2. The evolution history and status quo of Delta Lake

3. In-depth analysis of the data lake storage solution Lakehouse architecture

4. Basic introduction to Delta Lake data lake (open source version)

5. Introduction to Delta Lake Data Lake Basics (Commercial Edition)

Application

6. How to use Delta Lake to build a batch-stream integrated data warehouse

7. Use DDI+Confluent for real-time data collection and analysis

8. Application practice of retail demand forecasting using Databricks

9. Application practice of marketing effect attribution analysis using Databricks

10. Application practice of machine learning model training and deployment using Databricks and MLflow

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