Google BigQuery Corporate Training Course

Edstellar's instructor-led Google BigQuery training course equips organizations with the knowledge and expertise to leverage Google BigQuery for enhanced data management and strategic decision-making. Unlock the team's and employee's potential with this powerful data analytics tool and empower your organization to make data-driven decisions.

12 - 24 hrs
Instructor-led (On-site/Virtual)
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Google BigQuery Training

Drive Team Excellence with Google BigQuery Corporate Training

On-site or Online Google BigQuery Training - Get the best Google BigQuery training from top-rated instructors to upskill your teams.

BigQuery is a service provided by Google Cloud Platform, a suite of products & services on Google's scalable infrastructure that includes application hosting, cloud computing, and database services. Empower the workforce with our immersive and dynamic Google BigQuery training course, meticulously designed to turbocharge your corporate team's data prowess. In the age of data-driven decisions, staying ahead demands mastering tools like Google BigQuery, and we've got you covered. Our Google BigQuery instructor-led training goes beyond the basics, diving deep into real-world applications and strategic insights that elevate the team's data-handling capabilities.

Our onsite Google BigQuery training offers a tailor-made experience, fitting seamlessly into the organization's schedule while delivering impactful hands-on learning. Say goodbye to data overwhelm and hello to efficient data processing and analysis. With our expert trainers guiding the team, they'll unlock the power of BigQuery's advanced features and uncover hidden patterns in the data universe.

Google BigQuery Training for Employees: Key Learning Outcomes

Develop essential skills from industry-recognized Google BigQuery training providers. The course includes the following key learning outcomes:

  • Interpret and visualize query results using data visualization tools
  • Utilize Google BigQuery's features for data transformation, filtering, and aggregation
  • Apply data modeling principles to design efficient and scalable Google BigQuery solutions
  • Optimize query performance and resource utilization to enhance data processing efficiency
  • Implement security and access controls in Google BigQuery to ensure data privacy and governance
  • Apply advanced querying techniques in Google BigQuery to extract relevant information from complex datasets

Key Benefits of the Training

  • Improved efficiency in data processing and query time reduction
  • Advanced data analysis capabilities through advanced skills in Google BigQuery
  • Scalable solutions to handle growing data volumes and evolving business needs
  • Empowered employees to make data-driven decisions based on meaningful insights
  • Competitive advantage through deeper customer insights and market trends analysis

Google BigQuery Training Topics and Outline

This Google BigQuery Training curriculum is meticulously designed by industry experts according to the current industry requirements and standards. The program provides an interactive learning experience that focuses on the dynamic demands of the field, ensuring relevance and applicability.

  1. Key features and benefits of Google BigQuery:
    • Scalability: Ability to handle massive datasets and scale as data volumes grow.
    • Speed: Fast query performance and real-time data analysis.
    • Cost-effectiveness: Pay-as-you-go pricing model and automatic resource management.
    • Serverless architecture: No infrastructure management required.
  2. Role of Google BigQuery in modern data analytics and storage:
    • Enabling data-driven decision making.
    • Supporting advanced analytics, machine learning, and AI applications.
    • Centralizing and integrating diverse data sources for analysis.
  1. Data ingestion in Google BigQuery:
    • Batch loading: Uploading data from local files or Cloud Storage.
    • Streaming ingestion: Real-time data ingestion through streaming APIs.
  2. Storage and querying capabilities of Google BigQuery:
    • Columnar storage format for efficient data retrieval.
    • Support for SQL queries, including complex joins and aggregations.
  3. Data visualization options in Google BigQuery:
    • Built-in visualization tools for exploring data insights.
    • Integration with external data visualization platforms.
  1. Understanding datasets, tables, and schemas in Google BigQuery:
    • Creating and managing datasets to organize data.
    • Defining tables and specifying schema for structured data.
  2. Loading and exporting data in Google BigQuery:
    • Uploading data from various sources, such as CSV files or JSON documents.
    • Exporting query results or tables to external storage or services.
  1. Data ingestion methods in Google BigQuery:
    • Batch loading from Cloud Storage or other sources.
    • Streaming ingestion through APIs for real-time data updates.
  2. Data transformation techniques and best practices:
    • Using Google BigQuery's SQL capabilities for data transformations.
    • Applying data cleaning, filtering, and aggregation operations.
  3. Storage options and considerations in Google BigQuery:
    • Choosing between regional or multi-regional storage locations.
    • Understanding storage costs and optimization strategies.
  1. Pricing model and cost considerations for using Google BigQuery:
    • Understanding the pricing based on data storage, queries, and streaming.
    • Estimating costs and optimizing usage to manage expenses.
  2. Quotas and limits associated with Google BigQuery usage:
    • Query quotas and limits for data processing and resource allocation.
    • Managing quotas and understanding the impact on query execution.
  1. Overview of SQL clauses in Google BigQuery:
    • SELECT: Retrieving specific columns or expressions from tables.
    • FROM: Specifying data sources and table references.
    • WHERE: Filtering rows based on conditions.
  2. Commonly used SQL functions in Google BigQuery:
    • Aggregation functions (SUM, COUNT, AVG) for data summarization.
    • String functions (SUBSTR, CONCAT) for manipulating text data.
  1. Understanding nested and repeated fields in Google BigQuery:
    • Handling data structures with nested fields (e.g., JSON or nested records).
    • Dealing with repeated values in arrays or repeated records.
  2. Querying and manipulating data with nested and repeated structures:
    • Extracting specific nested fields or array elements in queries.
    • Performing aggregation or filtering operations on repeated fields.
  1. Techniques for optimizing query performance in Google BigQuery:
    • Query restructuring: Optimizing query structure and avoiding unnecessary operations.
    • Efficient data filtering: Using appropriate WHERE clauses and predicates to reduce data processing.
    • Partitioning tables: Leveraging table partitioning based on logical divisions for faster queries.
    • Clustering tables: Organizing data based on column values to improve query performance.
  2. Analyzing and improving query execution time:
    • Query profiling: Using EXPLAIN and query plan visualization to identify performance bottlenecks.
    • Query tuning: Making adjustments to optimize query execution based on profiling results.
    • Indexes and materialized views: Leveraging indexing and precomputed results for faster queries.
  3. Using query caching and query optimization tools in Google BigQuery:
    • Query caching: Taking advantage of automatic query caching for repeated queries.
    • Query optimization tools: Utilizing tools like Google BigQuery BI Engine and Google BigQuery Query Optimization to improve performance.
  1. Common errors and issues encountered in Google BigQuery:
    • Syntax errors: Identifying and fixing syntax-related errors in SQL queries.
    • Data type errors: Handling issues related to incompatible or mismatched data types.
    • Resource limitations: Managing and resolving errors related to resource usage and quotas.
  2. Understanding error messages and troubleshooting steps:
    • Error code explanations: Understanding the meaning and cause of specific error codes.
    • Troubleshooting techniques: Following step-by-step approaches to identify and resolve errors.
    • Logging and monitoring: Leveraging Google BigQuery logs and monitoring tools to diagnose issues.
  1. Managing access rights and permissions for datasets and tables in Google BigQuery:
    • Dataset access control: Setting permissions for dataset-level access.
    • Table-level access control: Defining fine-grained permissions for individual tables.
    • Roles and groups: Managing access through roles and groups for efficient permission management.
  2. Setting up authentication and authorization in Google BigQuery:
    • Authentication methods: Configuring authentication options like service accounts and OAuth.
    • Authorization policies: Defining authorization rules and policies to control data access.
    • Identity and Access Management (IAM): Utilizing IAM roles and policies for access control.
  1. Exporting query results and tables from Google BigQuery to external systems:
    • Exporting query results: Saving query results to Cloud Storage or other destinations.
    • Table export: Exporting entire tables to external storage or services.
    • Scheduled exports: Automating data export on a recurring basis.
  2. Available file formats and export options in Google BigQuery:
    • CSV, JSON, Avro, and other formats: Choosing the appropriate file format for export.
    • Export configuration options: Specifying export settings such as compression and delimiters.
    • Integration with external services: Exporting data to data warehouses, BI tools, or other systems.
  1. Integrating Google BigQuery with external tools and services (e.g., business intelligence tools, data visualization platforms):
    • Connecting BI tools: Setting up connections to popular BI tools for data analysis and reporting.
    • Data visualization integration: Integrating Google BigQuery with data visualization platforms for interactive dashboards and visual analytics.
    • ETL and data integration: Using connectors and APIs to transfer data between Google BigQuery and other systems.
  2. Leveraging APIs and connectors for data integration:
    • Google BigQuery API: Accessing and manipulating Google BigQuery resources programmatically.
    • Data transfer service: Using the Google BigQuery Data Transfer Service to automate data imports from external sources.
    • Third-party connectors: Exploring available connectors and plugins for seamless data integration.
  1. Importing and analyzing Google Analytics Premium data in Google BigQuery:
    • Setting up Google Analytics data export to Google BigQuery.
    • Configuring data schemas and data import options.
    • Analyzing Google Analytics data using Google BigQuery's querying capabilities.
  2. Extracting insights from Google Analytics data using Google BigQuery:
    • Querying Google Analytics data to generate custom reports and insights.
    • Combining Google Analytics data with other data sources in Google BigQuery.
    • Building advanced analytics and machine learning models using Google Analytics data.
  1. Visualizing data in Google BigQuery using built-in visualization options:
    • Exploring Google BigQuery's native visualization tools and features.
    • Creating charts, graphs, and interactive visualizations directly in Google BigQuery.
    • Customizing visualization settings and options.
  2. Integrating Google BigQuery with external data visualization tools:
    • Connecting Google BigQuery to popular data visualization platforms (e.g., Google Data Studio, Tableau).
    • Leveraging the power of specialized data visualization tools for advanced visual analytics.
    • Sharing and distributing visualizations and dashboards with stakeholders.

This Corporate Training for Google BigQuery is ideal for:

What Sets Us Apart?

Google BigQuery Corporate Training Prices

Elevate your team's Google BigQuery skills with our Google BigQuery corporate training course. Choose from transparent pricing options tailored to your needs. Whether you have a training requirement for a small group or for large groups, our training solutions have you covered.

Request for a quote to know about our Google BigQuery corporate training cost and plan the training initiative for your teams. Our cost-effective Google BigQuery training pricing ensures you receive the highest value on your investment.

Request for a Quote

Our customized corporate training packages offer various benefits. Maximize your organization's training budget and save big on your Google BigQuery training by choosing one of our training packages. This option is best suited for organizations with multiple training requirements. Our training packages are a cost-effective way to scale up your workforce skill transformation efforts..

Starter Package

125 licenses

64 hours of training (includes VILT/In-person On-site)

Tailored for SMBs

Most Popular
Growth Package

350 licenses

160 hours of training (includes VILT/In-person On-site)

Ideal for growing SMBs

Enterprise Package

900 licenses

400 hours of training (includes VILT/In-person On-site)

Designed for large corporations

Custom Package

Unlimited licenses

Unlimited duration

Designed for large corporations

View Corporate Training Packages

This Corporate Training for Google BigQuery is ideal for:

Edstellar's instructor-led Google BigQuery training course is designed for organizations/learning and development departments looking to upskill their data analysts, business analysts, cloud data engineers, business intelligence professionals, other developers, and marketing professionals.

Prerequisites for Google BigQuery Training

Google BigQuery training course requires knowledge of database concepts and Google Cloud Platform Fundamentals (CP100A) or Google Cloud Platform Big Data. Having experience in using a SQL-like query language to analyze data would be an advantage.

Assess the Training Effectiveness

Bringing you the Best Google BigQuery Trainers in the Industry

The instructor-led Google BigQuery Training training is conducted by certified trainers with extensive expertise in the field. Participants will benefit from the instructor's vast knowledge, gaining valuable insights and practical skills essential for success in Google BigQuery practices.

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