Google BigQuery Training Course
Google BigQuery training courses focus on equipping learners with the skills needed to use BigQuery, Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse. These courses cater to various roles, including data analysts, data engineers, and data scientists.
Why should you choose Nisa For Google BigQuery Training?
Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.
Job Assistance
If you face any problem while working on Google BigQuery Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.
The Process we follow for our Online Job Support Service:
- We receive your inquiry for Online Job
- We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
- If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
- We will fix the timing for Online Job Support as mutually agreed by you and our consultant.
Course Information
Google BigQuery Training
Duration: 25 Hours
Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
Training Method: Instructor Led Online One-on-One Live Interactive
Sessions.
COURSE CONTENT :
1. Introduction to Google BigQuery
- Overview of BigQuery
- What is Google BigQuery?
- Key features: serverless, scalable, and high-performance
- Use cases: data warehousing, analytics, and machine learning
- BigQuery Architecture
- Columnar storage
- Query execution and optimization
- Comparing BigQuery to Other Data Warehouses
- Advantages and limitations
2. Setting Up BigQuery
- Getting Started
- Setting up a Google Cloud account
- Enabling BigQuery API
- Understanding Projects and Datasets
- Project structure in Google Cloud
- Creating and managing datasets
- User Interface and Tools
- Using the BigQuery web UI
- Integrating with command-line tools and APIs
3. Loading and Managing Data
- Loading Data into BigQuery
- Importing data from Google Cloud Storage, Google Sheets, and external sources
- Using batch and streaming data ingestion
- Data Formats
- Supported formats: CSV, JSON, Parquet, and Avro
- Best practices for data ingestion
- Managing Tables
- Creating tables and defining schemas
- Partitioned and clustered tables for performance optimization
4. Querying Data with SQL
- Basics of BigQuery SQL
- Writing SELECT statements
- Filtering, grouping, and sorting data
- Advanced SQL Features
- Subqueries, joins, and window functions
- User-defined functions (UDFs)
- Recursive queries
- Query Performance Optimization
- Understanding query execution plans
- Reducing query costs and improving speed
5. Performance Optimization
- Best Practices for Query Optimization
- Using partitions and clustering effectively
- Avoiding costly operations
- Caching and Materialized Views
- Leveraging BigQuery’s caching for faster results
- Creating and using materialized views for efficiency
- Cost Optimization
- Estimating query costs and reducing expenses
- Using BigQuery pricing tools
6. Data Security and Governance
- Managing Access Control
- Role-based access control (RBAC)
- Dataset, table, and view-level permissions
- Encryption and Compliance
- Ensuring data security with encryption at rest and in transit
- Monitoring and Auditing
- Using Cloud Audit Logs for BigQuery
7. Integration with Other Tools
- BigQuery and Google Cloud Ecosystem
- Integrating with Dataflow, Dataproc, and Cloud Storage
- Using BigQuery with AI/ML tools like Vertex AI
- BI Tool Integration
- Connecting BigQuery to Looker, Tableau, and Power BI
- APIs and SDKs
- Using Python, Java, or other languages to interact with BigQuery
8. Advanced Topics
- Real-Time Analytics
- Streaming data ingestion with Cloud Pub/Sub
- Analyzing IoT or event-driven data
- BigQuery ML
- Building and deploying machine learning models directly in BigQuery
- Use cases for BigQuery ML
- Federated Queries
- Querying external data sources without moving data
- Using BigQuery Omni for multi-cloud environments
9. Administration and Monitoring
- Monitoring BigQuery Usage
- Using Cloud Monitoring and Query Statistics
- Troubleshooting
- Common issues and solutions
- Automation and Scripting
- Scheduling queries with Cloud Scheduler