Associate Data Practitioner

Define and execute SQL queries in BigQuery to generate reports and extract key insights

BigQuery is a fully managed, serverless data warehouse that lets you run fast SQL queries over large data sets. It uses standard SQL to help you explore and uncover trends and patterns. With BigQuery, you don’t have to manage servers or storage, making it easier to focus on your analysis.

Writing queries in BigQuery involves using familiar clauses like SELECT, WHERE, GROUP BY, and ORDER BY. You can:

• Filter rows to focus on relevant data
• Aggregate values to calculate sums or averages
• Sort results to identify top performers

Once your SQL is ready, you can run it in the BigQuery console, the bq command-line tool, or client libraries in languages like Python and Java. BigQuery also offers scheduled queries so you can automatically generate reports at regular intervals and keep your stakeholders informed.

To extract key insights, use features such as window functions and array operations. These tools help you spot moving averages, running totals, and other advanced metrics. By combining these functions, you can reveal hidden trends and make data-driven recommendations.

Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise)

Jupyter notebooks provide an interactive computing environment where you can write code, visualize results, and explain your process in one place. Colab Enterprise is Google Cloud’s hosted solution, making it easy to collaborate in the browser without installing anything locally. This setup is perfect for both data exploration and sharing findings.

In a notebook, you can connect to BigQuery using the BigQuery Python client library or the %bigquery magic command. Common methods include:

• Authenticating with your Google Cloud project
• Running SQL directly and loading results into a pandas DataFrame
• Using pandas-gbq to read and write tables

Once data is loaded into a DataFrame, libraries like pandas, matplotlib, and seaborn let you clean, transform, and visualize data. You can create bar charts, line plots, and heatmaps to highlight key patterns and anomalies. These visuals make your analysis more engaging and easier to understand.

Notebooks also support markdown cells for clear documentation. By mixing narrative text with code and charts, you build a reproducible record of your work. This approach helps teams review your methods and ensures everyone can follow the steps to arrive at the same insights.

Analyze data to answer business questions

Successful data analysis starts with clear, well-defined business questions. Before you jump into BigQuery or notebooks, take time to understand what stakeholders need. This might involve formulating a hypothesis or specifying target metrics like customer churn rate or average order value.

Next, gather and prepare your data. Typical steps include:

• Ingesting data from different sources into BigQuery
• Cleaning and transforming tables for consistency
• Creating views or derived tables for recurring analyses

With your data ready, use SQL in BigQuery for large-scale processing and notebooks for more detailed exploration. BigQuery handles heavy lifting on massive data, while notebooks offer flexibility for custom calculations and iterative testing. Combining both tools ensures your analysis is both efficient and thorough.

Finally, present your findings in a clear, concise format. You can build dashboards in Data Studio or embed charts from notebooks into slide decks. The goal is to turn raw numbers into a compelling story that guides smart business decisions.

Conclusion

In this section, you learned how to use BigQuery to write and run SQL queries that generate reports and reveal insights. You explored how Jupyter notebooks like Colab Enterprise let you interactively analyze data and create visualizations. Finally, you saw the importance of framing business questions and following a structured process to move from data to actionable recommendations. Together, these skills form a strong foundation for identifying trends, spotting patterns, and driving decisions with data.