Designing Cloud Data Platforms by Zburivsky Danil (23 results)

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Paperback. Condition: New. 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 an 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 analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations.   Designing Cloud Data Platforms  lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn   Designing Cloud Data Platforms, you'll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you'll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you'll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more.   what's inside The tools of different public cloud for implementing data platformsBest practices for managing structured and unstructured data setsMachine learning tools that can be used on top of the cloudCost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky  has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe.   Lynda Partner  is the VP.

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Paperback. Condition: New. 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 an 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 analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations.   Designing Cloud Data Platforms  lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn   Designing Cloud Data Platforms, you'll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you'll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you'll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more.   what's inside The tools of different public cloud for implementing data platformsBest practices for managing structured and unstructured data setsMachine learning tools that can be used on top of the cloudCost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky  has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe.   Lynda Partner  is the VP.

Condition: New. 2021. 1st Edition. Paperback. . . . . .

Paperback. Condition: Brand New. 400 pages. 9.50x7.50x0.75 inches. In Stock.

Taschenbuch. Condition: Neu. Neuware - '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. 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'.

Condition: New. 2021. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.

Condition: New. &Uumlber den AutorrnrnDanil Zburivsky has over 10 years 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 t.

Paperback. Condition: Brand New. 400 pages. 9.50x7.50x0.75 inches. In Stock.

Paperback. Condition: New. 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 an 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 analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations.   Designing Cloud Data Platforms  lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn   Designing Cloud Data Platforms, you'll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you'll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you'll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more.   what's inside The tools of different public cloud for implementing data platformsBest practices for managing structured and unstructured data setsMachine learning tools that can be used on top of the cloudCost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky  has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe.   Lynda Partner  is the VP.

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Paperback. Condition: new. Paperback. 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 an 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, youll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. Youll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn Designing Cloud Data Platforms, youll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, youll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics youll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what's inside The tools of different public cloud for implementing data platformsBest practices for managing structured and unstructured data setsMachine learning tools that can be used on top of the cloudCost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. 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 an 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, youll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. Youll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn Designing Cloud Data Platforms, youll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, youll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics youll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what's inside The tools of different public cloud for implementing data platformsBest practices for managing structured and unstructured data setsMachine learning tools that can be used on top of the cloudCost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condition: New. 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 an 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 analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations.   Designing Cloud Data Platforms  lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn   Designing Cloud Data Platforms, you'll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you'll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you'll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more.   what's inside The tools of different public cloud for implementing data platformsBest practices for managing structured and unstructured data setsMachine learning tools that can be used on top of the cloudCost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky  has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe.   Lynda Partner  is the VP.

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