Architecting Schemaless Scalable NoSQL Databases Using Google Datastore

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.
Course info
Level
Beginner
Updated
Jan 10, 2019
Duration
1h 48m
Table of contents
Description
Course info
Level
Beginner
Updated
Jan 10, 2019
Duration
1h 48m
Description

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.

About the author
About the author

An engineer and tinkerer, Vitthal has worked at Google, Credit Suisse, and Flipkart and studied at Stanford and INSEAD. He has worn many hats, each of which has involved writing code and building models. He is passionately devoted to his hobby of laughing at his own jokes.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Vitthal Srinivasan, and I'd like to welcome you to this course on Architecting Schemaless Scalable NoSQL Databases Using Google Datastore. A little bit about myself, I have a masters' degrees Financial Math and Electrical Engineering from Stanford University. I have previously worked in companies such as Google in Singapore and Credit Suisse in New York. I am now a co-founder at Loonycorn, a studio for high-quality video content based in Bangalore, India. A suite of big data technologies can be considered incomplete unless it includes a solution optimized for document-oriented data and hierarchical queries. In this course, you will gain the ability to identify situations when a Datastore is right for you, and use it to query 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; it only supports queries whose runtimes depends purely on the size of the result set and is independent of 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 allowed. Datastore and BigTable are the two NoSQL offerings on the GCP. Datastore effectively indexes every column, unlike BigTable, which only indexes a single column, that's the row-key. Datastore does not allow joins or subqueries and is optimized for hierarchical queries on document data. We will study all of these aspects of Datastore. 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 must belong to one kind. Individual fields within an entity are called properties. Datastore is schemaless and there are virtually no rules enforcing uniformity in different entities of the same kind. This data model is so complex and so unintuitive to use that Datastore adoption has definitely suffered in consequence. Once you've mastered the intricacies of this data model, you will explore how to perform administrative and backup operations and work with Datastore programmatically. 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.