In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors.

Summary

Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it.

About the technology

Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you’ll maximize performance no matter which cloud vendor you use.

About the book

In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors.

What's inside

Best practices for structured and unstructured data sets

Cloud-ready machine learning tools

Metadata and real-time analytics

Defensive architecture, access, and security

About the reader

For data professionals familiar with the basics of cloud computing, and Hadoop or Spark.

Table of Contents

1 Introducing the data platform

2 Why a data platform and not just a data warehouse

3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google

4 Getting data into the platform

5 Organizing and processing data

6 Real-time data processing and analytics

7 Metadata layer architecture

8 Schema management

9 Data access and security

10 Fueling business value with data platforms

About the authors

Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. 

Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years.