Livrare gratuită pentru comenzile peste 349,00 lei.

Verifică starea comenzii

Faceți parte dintr-o comunitate de iubitori de cărți din întreaga lume și beneficiați de o mulțime de avantaje Creați-vă un cont gratuit

Transport gratuit la punctele de livrare Pick Up peste 349.00 lei

Packeta 15.00 lei Cargus 28.00 lei Easybox 20.00 lei FAN 20.00 lei Punct FAN 16.00 lei Punct DPD 17.00 lei Curier Sameday 24.00 lei Curier DPD 25.00 lei

Contact

Cum să cumpăr

Ajutor

Contul meu

▸ Gol :-(

Livrare gratuită pentru comenzile peste 349,00 lei.

Designing Cloud Data Platforms

Name: Designing Cloud Data Platforms
Brand: Manning Publications
SKU: 33105720
Price: 351.62 RON
Availability: InStock
Author: Lynda Partner
ISBN: 9781617296444

Lynda Partner

Limba

engleză

Carte Carte broșată

Codul Libristo: 33105720

Editura Manning Publications, aprilie 2021

Centralized data warehouses, the long-time defacto standard for housing data for analytics, are ra... Descrierea completă

Codul Libristo: 33105720

162 b

351.62 lei

șansă 50% Şanse de a obține acest titlu Când primesc cărțile?

30 de zile pentru retur bunuri

Clienții au cumpărat de asemenea

Building AI Applications with Microsoft Semantic Kernel Lucas A. Meyer

Carte broșată

252.55 lei

Monetizing Your Data - A Guide to Turning Data into Profit-Driving Strategies and Solutions Andrew R. Wells

Copertă tare

211.05 lei

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 technology

Access 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 book

In 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 platforms
Best practices for managing structured and unstructured data sets
Machine learning tools that can be used on top of the cloud
Cost optimization techniques

about the reader

For 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.