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Architecting Schemaless Scalable NoSQL Databases Using Google Datastore

      Author: mitsumi   |   11 January 2019   |   comments: 0

Architecting Schemaless Scalable NoSQL Databases Using Google Datastore

Architecting Schemaless Scalable NoSQL Databases Using Google Datastore
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 48M | 188 MB
Genre: eLearning | Language: English

This course is about Datastore, a schemaless, serverless NoSQL service that fills a specific niche on the GCP. Datastore offers fast lookups virtually independent of the dataset size and is optimized for hierarchical queries on document data

A suite of big data technologies is considered incomplete unless it includes a solution optimized for document-oriented data and hierarchical queries, and that can provide the blazingly fast lookup that web serving applications need to perform on such data. In this course, Architecting Schemaless Scalable NoSQL Databases Using Google Datastore, you will gain the ability to identify situations when Datastore is right for you, and query it both interactively and programmatically. First, you will learn exactly how Datastore contrasts with other GCP technologies such as BigQuery, BigTable and Firestore. Datastore is all about fast reads; Datastore only supports queries whose runtime depends only the size of the result set, and not on the size of the total data set. This is a remarkable guarantee, and it is achieved via a combination of heavy usage of indices, and of constraints on the types of queries that are supported. Next, you will discover Datastore's unique data model, which users often find hard to navigate. Datastore organizes documents into categories called kinds; each individual document is called an entity and belongs to a kind. Finally, you will explore how to perform administrative and backup operations and work with Datastore pro-grammatically. When you're finished with this course, you will have the skills and knowledge of Google Datastore needed to design and implement a storage solution optimized for fast querying of hierarchical, document-oriented data.

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