Technologies / Data & Analytics

Practical data analysis in Google Cloud with BigQuery and Looker

Szkolenie koncentruje się na praktycznym wykorzystaniu najważniejszych narzędzi analitycznych Google Cloud Platform do przetwarzania i wizualizacji dużych zbiorów danych. Program łączy zaawansowane techniki pracy z BigQuery jako hurtownią danych z tworzeniem interaktywnych dashboardów w Looker. Zajęcia prowadzone są w formie warsztatów praktycznych, podczas których uczestnicy realizują rzeczywiste projekty analityczne. Szkolenie przygotowuje do efektywnego wykorzystania ekosystemu Google Cloud w projektach Business Intelligence.

Issues

BigQuery as a data warehouse and analytics engine
SQL standard and advanced analytics
Query optimization: partitioning and clustering
Working with semi-structured data (JSON, ARRAY, STRUCT)
User-Defined Functions (UDFs) in BigQuery
ETL/ELT processes and data transformation
Looker and the LookML modeling language
Interactive dashboards and data visualizations
Security and data access control
BigQuery ML and machine learning models
Integration with Vertex AI and Gemini models
Cost management and performance monitoring

Benefits

Applying advanced BigQuery techniques including partitioning, clustering, window functions, and UDFs for large-scale data analysis
Creating interactive dashboards and reports in Looker using LookML dimensions, measures, and explores
Transforming and preparing data using BigQuery ETL/ELT patterns, Dataform, and dbt integration
Optimizing query performance and managing costs through BigQuery best practices
Integrating BigQuery with Cloud Storage, Dataflow, and other Google Cloud Platform services
Implementing security and access control in analytical projects using IAM and authorized views
Using BigQuery ML and Gemini models to accelerate data analysis workflows
Delivering end-to-end Business Intelligence projects on the Google Cloud analytics stack

Who is this training for?

Data analysts looking for modern tools to work with large data sets

Data engineers (data engineers) developing competencies in the Google Cloud ecosystem

Business intelligence specialists moving to cloud platforms

Developers interested in integrating applications with GCP analytics services

Product managers in need of advanced analytical tools

IT consultants specializing in analytical solutions

Database administrators learning about modern data warehouses

Those preparing for Google Cloud Data Analytics certification

Prerequisites

Solid knowledge of SQL and basic database operations
Experience working with analytical or business intelligence tools
Basic knowledge of data warehouse concepts and ETL processes
Ability to read and interpret relational database diagrams
Knowledge of basic principles of data visualization and report generation

Training program

Google Cloud data ecosystem and interconnectedness of services
BigQuery as a serverless data warehouse and analytics engine
Looker as a BI and data visualization platform
BigQuery architecture: projects, datasets, tables, tasks
Methods for loading data into BigQuery from various sources

SQL standard in BigQuery: Syntax, data types, operators
Analytical functions (window functions) and complex joins
Query optimization: partitioning, clustering, best practices
Working with semi-structured data: JSON, ARRAY, STRUCT
User-Defined Functions (UDFs) in SQL and JavaScript.
Data engineering and ETL with BigQuery
Data transformation techniques: CREATE TABLE AS SELECT, MERGE

Introduction to Dataform and dbt in integration with BigQuery
Integration with Cloud Storage for raw and processed data
Dataflow basics as a tool for building ETL/ELT pipelines
Introduction to Looker and the LookML language
Looker platform architecture and connection setup with BigQuery
LookML language basics: dimensions, measures, views, models
Creating and managing data models in LookML
Exploring (explores) and defining relationships between tables
Data visualization and dashboard creation in Looker
Available visualization types and best practices for their use
Building interactive dashboards: tiles, filters, parameters
Data drill-down, drill-through functionalities

Management and security of analytical data
Access control mechanisms in BigQuery (IAM, authorized views)
Access control and permissions in Looker
Data masking techniques and security at the line level

Introduction to Dataplex as a metadata management tool

BigQuery ML: creating machine learning models in SQL
BigQuery integration with Vertex AI for advanced ML scenarios
Using Gemini models in BigQuery to increase productivity
Generate SQL queries, summarize data, create explanations
Practical applications of AI in the daily work of a data analyst

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For Practical data analysis in Google Cloud with BigQuery and Looker we recommend: Solid knowledge of SQL and basic database operations; Experience working with analytical or business intelligence tools; Basic knowledge of data warehouse concepts and ETL processes.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data analysts looking for modern tools to work with large data sets; Data engineers (data engineers) developing competencies in the Google Cloud ecosystem; Business intelligence specialists moving to cloud platforms.