BigQuery for Data Science

Analyze Data in an Optimized way at Google Scale

Duration

3 Days

Level

Intermediate Level

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The purpose of this course is to provide participants with knowledge and skills essential to work with BigQuery in an optimal manner. The participants will understand how BigQuery works, its Architecture, Best Practices and how to optimize it for minimizing the cost and reducing the query runtime.

The lab exercises of the course will be performed using Jupyter Lab Environment.

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Data Analysts
Data Engineers
Data Scientists

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BigQuery (BQ) Overview

Features of Big Query
Big Query Architecture
How BigQuery stores data
How does a query execute on BQ?
Various APIs in BQ
Bootstrapping Lab Environment

Working with BigQuery

Ways to interact with BigQuery
BQ Command line interface
Exploring BigQuery Dataset & Tables
BigQuery Table Types
Wildcard Tables & its limitations
Query Execution Plan
Writing Effective Queries
With Clause in BigQuery
BigQuery Core – Python Client API

BigQuery Capabilities

Understanding Query Metadata
Caching in BigQuery
Time Travels and
Table Snapshots
Table Clones
Extending BQ

Structuring the Data Model

Approach of Data Modeling
Denormalized Tables
Understanding Key Data Formats
Solving Data Skew Problem
Working with Views
Using Materialized Views

Tuning BigQuery

Scope of Optimizations
Ways to Optimize Big Query
Understanding Slots & BigQuery Pricing
Diagnose Query Performance Issues
Query Plan Analysis
Understanding Partitioning
Key considerations for Partitioning
Making use of Partitioning
Benefits of Clustering
Key considerations for Clustering
Making use of Clustered Tables
When to prefer Partitioning, Clustering, or Both
Exploring Indexing Capability
Exploring Join and Best Practices
Nested & Repeated Data
Best Practices for BigQuery

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Participants must have knowledge of –

SQL query language
Data Warehousing concepts
Jupyter Notebook Environment
Python Basics

Knowledge of “Basics of BigQuery” will be a plus.

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BigQuery for Data Science

Duration

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