Google BigQuery & PostgreSQL : Big Query for Data Analysis

Post published:3 November, 2025
Post category:StudyBullet-22
Reading time:4 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
👥 165,156 students
🔄 September 2025 update

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Course Overview
- Master the strategic use of Google BigQuery, a powerful, serverless cloud data warehouse designed for colossal datasets.
- Gain expertise in PostgreSQL, an industry-standard open-source relational database, complementing cloud analytical skills.
- Understand the synergistic application of BigQuery and PostgreSQL for comprehensive data analysis pipelines.
- Navigate the Google Cloud Platform (GCP) ecosystem, focusing on efficient BigQuery administration and data management.
- Learn to architect and optimize queries for superior performance and cost-effectiveness in diverse analytical scenarios.
- Bridge traditional database knowledge with modern cloud-scale data processing capabilities, becoming a versatile data professional.
- Develop a strong foundation in designing robust data schemas and implementing effective data partitioning strategies for Big Data.
- Prepare to execute full-lifecycle data analysis projects, from raw data ingestion to insightful reporting.
- Cultivate expertise in interpreting complex data, extracting actionable insights, and driving data-driven decision-making.
Requirements / Prerequisites
- A conceptual understanding of databases and data structures is beneficial but not strictly required.
- No prior experience with SQL, Google Cloud Platform, or BigQuery is assumed; the course builds from the ground up.
- A stable internet connection and a modern web browser are essential for accessing cloud resources.
- A Google account will be necessary to engage with BigQuery and related Google Cloud services.
- Eagerness to learn and actively participate in hands-on coding exercises is key to success.
Skills Covered / Tools Used
- Employ advanced SQL constructs for sophisticated data manipulation, aggregation, and transformation in both environments.
- Master Window Functions for intricate analytical operations, including ranking, running totals, and moving averages.
- Utilize Common Table Expressions (CTEs) to structure complex queries for improved readability and modularity.
- Design and implement efficient database schemas, understanding the trade-offs for analytical workloads in BigQuery and PostgreSQL.
- Gain hands-on proficiency with the BigQuery Console, command-line tools, and relevant Google Cloud Storage integrations.
- Explore advanced data types and their optimal application for performance and storage efficiency in Big Data contexts.
- Apply data validation, cleaning, and transformation techniques to ensure high-quality analytical outputs.
- Analyze query execution plans to identify bottlenecks and optimize performance, managing BigQuery costs effectively.
- Develop and deploy User-Defined Functions (UDFs) and stored procedures for custom logic and task automation.
- Interface with PostgreSQL using standard client tools, understanding connectivity and configuration.
- Implement strategies for integrating and querying data across disparate systems, including cloud-to-on-premise scenarios.
- Engage in advanced data exploration and hypothesis testing to uncover deep insights from complex datasets.
Benefits / Outcomes
- Acquire the ability to autonomously design, execute, and debug elaborate SQL queries for Big Data analysis.
- Enhance your marketability with highly demanded skills in both traditional RDBMS and cutting-edge cloud data warehousing.
- Become proficient in extracting profound insights from massive datasets, directly supporting strategic business decisions.
- Open doors to diverse career paths as a Data Analyst, BI Developer, or aspiring Data Engineer in various industries.
- Develop a holistic understanding of the Google Cloud data ecosystem, expanding beyond just BigQuery.
- Cultivate strong problem-solving skills, tackling real-world data challenges with a structured and analytical approach.
- Build a robust portfolio of practical BigQuery and PostgreSQL projects, showcasing your analytical prowess.
- Effectively communicate complex data findings to non-technical stakeholders, translating insights into tangible business value.
PROS
- Comprehensive Dual-Platform Expertise: Uniquely combines deep dives into both PostgreSQL and Google BigQuery, offering unparalleled versatility for diverse data environments.
- Cloud-Native Focus: Provides highly relevant skills for the modern cloud-centric data landscape, particularly within the Google Cloud Platform (GCP) ecosystem.
- Exceptional Student Validation: A high rating (4.56/5) from over 165,000 students underscores the course’s quality, effectiveness, and broad appeal.
- Up-to-Date Curriculum: Regularly updated content, as indicated by the September 2025 update, ensures learners are equipped with the latest tools and best practices.
- Career Accelerating Skills: Delivers in-demand capabilities crucial for thriving in data analysis, business intelligence, and data engineering roles, enhancing career prospects.
- Practical, Project-Oriented Learning: Emphasizes hands-on application, enabling learners to build a strong practical portfolio and apply knowledge immediately.
CONS
- Requires Consistent Dedication: Mastery of the diverse and complex topics, encompassing two distinct database technologies, necessitates sustained practice and independent study beyond the course duration.