BigQuery explained: Blog series recap

BigQuery BigQuery is Google Cloud's enterprise data warehouse designed for business agility. It's serverless architecture allows you to operate at scale and run fast SQL queries over large datasets.  We started a new blog series—BigQuery Explained—to uncover and explain BigQuery's concepts, features and improvements. This blog post is the home page to the series with links to the existing and upcoming posts for the readers to refer. Here are links to the blog posts in this series:

1. Overview: This post dives into how data warehouses change business decision making, how BigQuery solves problems with traditional data warehouses, and dives into a high-level overview of BigQuery architecture and how to quickly get started with BigQuery.

2. Storage Overview: This post dives into BigQuery storage organization, storage format and introduces partitioning and clustering data for optimal performance.

3. Data Ingestion: In this post, we cover options to load data into BigQuery. This post dives into batch ingestion and introduces streaming, data transfer service and query materialization.

4. Querying your Data: This post covers querying data with BigQuery, lifecycle of a SQL query, standard & materialized views, saving and sharing queries.

5. Working with Joins, Nested & Repeated Data: This post looks into joins with BigQuery, optimizing join patterns and  nested and repeated fields for denormalizing data.

6. Data Manipulation (DML):  This post shows you how to run data manipulation statements in BigQuery to add, modify and delete data stored in BigQuery.

We have more articles coming soon covering BigQuery's features and concepts. 

Stay tuned. Thank you for reading! Have a question or want to chat? Find me on Twitter or LinkedIn.