BigQuery Transformations

What Are BigQuery Transformations in Keboola?

BigQuery transformations in Keboola allow you to easily manage, transform, and optimize your data using powerful SQL queries directly within Keboola's platform. Leveraging Google's BigQuery backend, Keboola ensures high performance, scalability, and ease of use. Whether you’re a seasoned data engineer or just starting with data analytics, Keboola simplifies your transformation workflow.

Step-by-Step Guide to Creating a BigQuery Transformation

1. Create a New Transformation: Log into Keboola and navigate to the transformations section. Click "Create New Transformation" and select "BigQuery" as your backend. Provide a descriptive name and a clear markdown-supported description to keep your transformation organized and documented.
2. Configure Input Mapping: Setup your transformation by defining inputs. Click "New Table Input" and select one or multiple tables from storage you want to work with. This step is crucial for bringing your data into the transformation workspace.
3. Write Your SQL Queries: Now you’re ready to start writing your SQL queries. Click "Add New Code", give your code block a meaningful name, and write your query. Keboola places no restrictions on the length of your SQL block, allowing you to write extensive and complex SQL statements to transform your data effectively.
4. Best practice tip: Keep your SQL code organized and easy to debug by dividing logical steps into separate blocks. Remember, code blocks run sequentially exactly in the order they are placed.
5. Configure Output Mapping: After writing your SQL queries, you need to specify where your transformed tables will go. Click "Output" and select the tables you wish to export back to storage. Ensure that the names match your SQL CREATE TABLE statements. Choose existing storage buckets or create new ones as needed. Keboola also allows you to rename tables and define primary keys if your table contains a unique column.
6. Execute the Transformation: Finally, click the "Run" button to execute your transformation. Keboola initiates the job, and you’ll see real-time status updates. Once the transformation completes, your newly created tables will appear in storage, ready for further analysis, reporting, or export.

Advanced Features for BigQuery Transformations in Keboola

• Detailed Job Logs: Keboola provides comprehensive job logs for each transformation run. These logs are invaluable tools for debugging and performance optimization, offering insights into execution times, table loading durations, and detailed error messages. Quickly identify bottlenecks and resolve issues efficiently.
• Version Control and Auditability: Every change made to your transformations is tracked within Keboola’s intuitive Versions section. Easily see who made changes, exactly what changed, and when it happened. Compare different versions side-by-side, highlighting modifications such as updates to SQL clauses. If needed, effortlessly revert back to previous versions to restore functionality or debug issues.
• Parameterize Your Transformations with Variables:
• Increase flexibility and efficiency by parameterizing your SQL code. Create variables using simple syntax, such as mustache notation. For instance, if you want your transformation to include only "Closed Won" opportunities, create a variable called StageName and assign it the value "Closed Won". Your SQL query can then dynamically reuse this variable, making it easy to adjust your transformations without rewriting your code each time.
• When checking your job log, you’ll see the executed SQL query with actual values in place of the variables, ensuring transparency and simplifying troubleshooting and audits.
• Markdown Supported Descriptions: Document your transformations clearly and neatly using markdown-supported descriptions. This feature enhances readability and allows effective communication within your team, facilitating collaboration among analysts, data engineers, and stakeholders.

Real-world Example of BigQuery Transformation

Consider a scenario where your sales team needs detailed analysis of regional performance. You have multiple sales data tables in Keboola storage. By configuring input mapping, you select all required tables. You then write SQL queries to aggregate sales by region, calculate profit margins, and identify top-performing products. After running the transformation, the output mapping will export the results to Keboola storage, ready for visualization tools or further analysis.

Keboola simplifies such complex analytical processes, making it straightforward to translate business requirements into actionable insights.

Benefits of Using Keboola for BigQuery Transformations

• Ease of Use: User-friendly interface makes SQL transformations accessible to everyone.
• Scalability: Leverage BigQuery’s robust infrastructure to handle large datasets seamlessly.
• Flexibility: Parameterize queries and version transformations to adapt quickly to changing requirements.
• Time Savings: Automate routine data preparation tasks to focus on insights and strategy.
• Enhanced Collaboration: Markdown descriptions, detailed logs, and version controls facilitate effective teamwork.