Google BigQuery & PostgreSQL : Big Query for Data Analysis

Post published:1 October, 2025
Post category:StudyBullet-22
Reading time:5 mins read

Become BigQuery expert by mastering Google BigQuery for data analysis. Cover all SQL qureies in PostgeSQL & Big Query
⏱️ Length: 11.7 total hours
⭐ 4.56/5 rating
👥 164,167 students
🔄 September 2025 update

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Course Overview
- Journey into Dual-Platform Mastery: Embark on a comprehensive learning journey to achieve expert-level proficiency in both Google BigQuery and PostgreSQL, two pivotal technologies in modern data analysis.
- Bridge Relational Databases and Cloud Data Warehouses: Gain a unique perspective by understanding foundational principles of traditional relational databases (PostgreSQL) alongside the scalable, high-performance capabilities of a serverless cloud data warehouse (BigQuery).
- Real-World Data Analysis Scenarios: This course constructs practical scenarios and challenges, enabling you to apply theoretical knowledge to solve genuine industry data analysis problems.
- Strategic Data Manipulation for Insights: Learn to transform raw data into coherent, actionable insights, driving informed decisions and strategic business outcomes.
- Performance and Scalability Focus: Understand architectural nuances for petabyte-scale analysis in BigQuery, contrasting it with robust PostgreSQL features for structured data management.
- Future-Proof Your Data Skillset: Master leading cloud computing and open-source database technologies to prepare for evolving demands in data-centric roles.
Requirements / Prerequisites
- Foundational Computer Literacy: Basic understanding of computer operations, comfort in navigating web browsers, and using standard applications.
- Curiosity for Data: An inherent interest in working with data, exploring patterns, and deriving meaningful conclusions, even without prior analytical experience.
- No Prior SQL Experience Necessary: Structured to guide you from the ground up, making it accessible even if you’ve never written SQL code.
- Stable Internet Connection: Required for accessing the cloud-based BigQuery environment and all course materials.
- Dedication to Practice: A willingness to actively engage with coding exercises and practical assignments to solidify understanding and build SQL proficiency.
Skills Covered / Tools Used
- Advanced Data Definition Language (DDL): Master creating, modifying, and deleting databases, schemas, tables, and views in both PostgreSQL and BigQuery, understanding platform-specific syntax and best practices.
- Data Type Management & Transformation: Comprehend various SQL data types (e.g., INT, TEXT, DATE, ARRAY) and learn effective techniques for data type conversion and manipulation for accurate analysis.
- Complex Query Optimization: Delve into strategies for writing efficient queries that minimize execution time and resource consumption, crucial for massive datasets in BigQuery.
- Common Table Expressions (CTEs): Utilize CTEs for structuring complex queries into readable, manageable, and reusable blocks, enhancing clarity and performance.
- Window Functions for Analytical Power: Explore powerful window functions (e.g., RANK, LEAD, LAG) to perform advanced analytical tasks like running totals, moving averages, and percentile analysis.
- Handling NULL Values & Data Integrity: Develop strategies for effectively identifying, managing, and transforming NULL values in your datasets to ensure data integrity and accurate results.
- BigQuery-Specific Functions & Features: Leverage BigQuery’s unique functions for array manipulation, geographical analysis, and working with nested and repeated data.
- PostgreSQL Advanced Features: Explore specific PostgreSQL capabilities like aggregate functions, pattern matching with REGEXP, and user-defined functions (UDFs).
- Data Export and Integration Fundamentals: Learn how to export query results to various formats (CSV, JSON) and understand concepts for integrating BigQuery data with other tools.
- Performance & Cost Management (BigQuery): Gain insights into monitoring BigQuery job performance, understanding slot allocation, and managing query costs effectively.
- Database Normalization Principles: Understand fundamental principles of database normalization to design well-structured, efficient relational schemas in a PostgreSQL context.
- Introduction to Data Security & Access Control: Grasp the basics of managing user roles, permissions, and access controls in both environments to secure sensitive data.
Benefits / Outcomes
- Unlock Actionable Business Intelligence: Transform raw data into compelling narratives and actionable insights that directly influence business strategies and operational improvements.
- Command Over Large-Scale Datasets: Develop the confidence and technical capability to query, analyze, and manage data ranging from gigabytes to petabytes across different platforms.
- Elevated Career Prospects: Position yourself as a highly desirable candidate for roles like Data Analyst, SQL Developer, and Business Intelligence Engineer, with a standout dual skill set.
- Contribute to Data-Driven Decision Making: Play a pivotal role by providing timely, accurate, and insightful data reports and analyses, empowering leadership with informed choices.
- Architectural Understanding of Data Systems: Gain a deeper appreciation for architectural differences and use cases of relational databases versus cloud data warehouses.
- Build a Robust Project Portfolio: Accumulate a collection of practical SQL projects and query solutions to showcase to potential employers, demonstrating hands-on analytical abilities.
- Foundation for Advanced Data Science: Establish a strong SQL foundation essential for progressing into more advanced data science and machine learning disciplines.
- Increased Problem-Solving Aptitude: Enhance your logical reasoning and problem-solving skills by tackling complex data challenges and optimizing query logic.
- Versatility Across Data Ecosystems: Achieve the versatility to work competently with data in both on-premise/self-hosted environments (PostgreSQL) and cutting-edge cloud infrastructure (Google Cloud BigQuery).
PROS
- Highly Relevant Dual Skillset: Master two of the most in-demand data technologies, significantly boosting your marketability in data analytics and engineering.
- Comprehensive Curriculum: Offers an in-depth exploration of SQL, ensuring thorough understanding of core concepts and advanced techniques across both platforms.
- Practical & Hands-On Approach: Emphasizes real-world application, allowing learners to build practical experience and confidence through numerous exercises.
- Accessible to All Levels: Designed to accommodate beginners, yet challenging enough for those with some prior exposure to data concepts.
- Timely Content Update (September 2025): Guarantees that the course material is current with the latest features and best practices for BigQuery and PostgreSQL.
- Strong Community and High Rating: A large student base and high rating reflect the quality and effectiveness of the instruction and content.
CONS
- Significant Time Commitment Required: While comprehensive, mastering these two powerful technologies effectively demands dedicated time and consistent practice beyond the course’s stated duration.