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.
What you'll learn
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.
Table of contents
- Module Overview 1m
- Enabling APIs 4m
- Creating Our First Entity 5m
- Creating Entities with Ancestor Information 5m
- Running Queries on the Web Console Using GQL 6m
- Querying Multiple Namespaces in GQL 3m
- Using Python Client Libraries to Work with Datastore 5m
- Creating Entities Using Python 4m
- Running Queries Using Python 6m
- Implementing Transactions in Datastore 4m
- Composite Indexing to Run Queries for Projecting Multiple Properties 5m
- Summary 1m
- Module Overview 1m
- View Datastore Statistics Using the Web Console 3m
- Exporting Entities to Cloud Storage Buckets 5m
- Importing Data into Big Query for Analytical Processing 5m
- Implementing Code to Export Datastore Entities 5m
- Running an App Engine Cron Job to Export Entities 3m
- Using Dataflow Templates to Bulk Delete Entities 4m
- Summary 2m
About the author
Vitthal has spent a lot of his life studying - he holds Masters Degrees in Math and Electrical Engineering from Stanford, an MBA from INSEAD, and a Bachelors Degree in Computer Engineering from Mumbai. He has also spent a lot of his life working - as a derivatives quant at Credit Suisse in New York, then as a quant trader, first with a hedge fund in Greenwich and then on his own, and finally at Google in Singapore and Flipkart in Bangalore. In all these roles, he has written a lot of code, and b... more