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Introductie tot NoSQL

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Introductie tot NoSQL

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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 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: Introductie tot key-value-databases Exercise 2: Key-value-databases Exercise 3: Key-value versus documentdatabases Exercise 4: Verbinding maken met een Redis-cluster Exercise 5: Sleutel-waardegegevens opslaan en opvragen Exercise 6: Key-value-gegevens opslaan met Redis Exercise 7: Key-valuegegevens ophalen met Redis Exercise 8: Python-dictionaries opslaan met Redis Exercise 9: Graphdatabases Exercise 10: Graphdatabases begrijpen Exercise 11: Aanbieders van graafdatabases Exercise 12: Grafendatabases gebruiken Exercise 13: Afronding!

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