LoslegenKostenlos loslegen

Startseite
Kurse
Introduction to NoSQL

Advanced Postgres JSON query techniques

Create Your Free Account

or

Email Address

Password

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Diese Übung ist Teil des Kurses

Introduction to NoSQL

Geringe SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

Explore the basics of NoSQL databases, and how they're used in a modern data architecture. Learn to identify the pros and cons of using column-oriented, document, key-value, and graph databases to make data available to downstream processes and data consumers.

Exercise 1: Introduction to NoSQL Exercise 2: Querying NoSQL databases Exercise 3: Exploring document data Exercise 4: NoSQL use-cases and applications Exercise 5: Tabular NoSQL data stores Exercise 6: Column-oriented databases Exercise 7: Querying a column-oriented database Exercise 8: Filtering a column-oriented database Exercise 9: Non-tabular NoSQL data stores Exercise 10: Identifying tabular and non-tabular NoSQL data stores Exercise 11: Querying document databases Exercise 12: Querying nested documents with Postgres JSON

Dive into column-oriented databases with the help of Snowflake. Learn to populate Snowflake tables using a number of tools, and uncover the fundamentals of micro-partitioning and data clustering to optimize query performance with query pruning. Leverage common table expressions, materialized and non-materialized views to streamline analytics workflows. Step into semi-structured data with Snowflake's VARIANT types.

Exercise 1: Populating column-oriented databases Exercise 2: CREATE TABLE with Snowflake Exercise 3: Populating Snowflake tables Exercise 4: COPY INTO and CREATE TABLE ... AS with Snowflake Exercise 5: Advanced column-oriented database techniques Exercise 6: Micro-partitioning and data clustering with Snowflake Exercise 7: Query pruning with micro-partitioning Exercise 8: Leveraging micro-partitions and data clustering Exercise 9: Analytics workflows for column-oriented databases Exercise 10: Common table expressions with Snowflake Exercise 11: Building an analytics workflow with Snowflake Exercise 12: Materialized vs. non-materialized VIEWs Exercise 13: Working with semi-structured data in Snowflake Exercise 14: Semi-structured Snowflake data types Exercise 15: Querying semi-structured data in Snowflake Exercise 16: Querying nested semi-structured data

Dive deep into the world of document data with Postgres JSON, and explore the intricacies of semi-structured data. Learn how to extract and transform top-level and nested document data using a number of built-in operators and functions.

Exercise 1: Understanding JSON data in Postgres Exercise 2: JSON and JSONB data in Postgres Exercise 3: Executing queries with sqlalchemy and pandas Exercise 4: Querying JSON and JSONB data from Postgres Exercise 5: Storing JSON data in Postgres Exercise 6: Loading Postgres with semi-structured data Exercise 7: Converting tabular data to JSON Exercise 8: Extracting keys from JSON objects with Postgres Exercise 9: Querying JSON data using Postgres Exercise 10: Querying top-level JSON data Exercise 11: Finding the type of JSON data Exercise 12: Working with nested JSON objects Exercise 13: Filtering document databases with Postgres JSON Exercise 14: Advanced Postgres JSON query techniques

Aktuelle Übung

Exercise 15: Arrow and hash arrow operators Exercise 16: #> and #>>Exercise 17: Extracting document data Exercise 18: Manipulating document data

Take your NoSQL skills to the next level by mastering the fundamentals of key-value databases. Explore common use-cases for key-value data, and learn how to use Redis and Python to read and write data. Wrap up with a basic overview of graph databases, and their place in the modern data stack.

Exercise 1: Introduction to key-value databases Exercise 2: Key-value databases Exercise 3: Key-value vs. document databases Exercise 4: Connecting to a Redis cluster Exercise 5: Storing and retrieving key-value data Exercise 6: Storing key-value data with Redis Exercise 7: Retrieving key-value data with Redis Exercise 8: Storing Python dictionaries with Redis Exercise 9: Graph databases Exercise 10: Understanding graph databases Exercise 11: Graph database providers Exercise 12: Using graph databases Exercise 13: Wrapping up!