Optimizing data management: a strategic challenge for modern businesses
Today, a company’s ability to efficiently leverage its data is a key driver of competitiveness. Poor database management can lead to financial losses, delayed analytics, and an unnecessary burden on technical teams.
Google BigQuery, one of the most powerful cloud tools for data analytics, has just introduced a new feature designed to enhance performance: "UNION ALL BY NAME."
This innovation speeds up data consolidation processes while reducing the risk of errors.
The result? Faster decision-making and optimized costs.
Let’s explore how this new feature can improve your ROI and optimize the KPIs related to your data management strategy.
The Hidden Costs of Data Transformation
Wasted time and manual errors: a drag on performance
Companies are processing increasing volumes of data, often coming from diverse systems (CRM, ERP, sales platforms, etc.).
Every time teams attempt to merge these datasets, they encounter the same recurring problem: the database structures are rarely aligned.
Until now, combining datasets required manual column alignment — a process that was:
- Time-consuming: Any structural change meant rewriting SQL code.
- Error-prone: A mismatched column could skew your analysis and mislead strategic decisions. In previous versions, such issues often remained invisible, silently producing false results.
- Resource-intensive : Data engineers and practitioners spent more time fixing queries than delivering real business value.
How BigQuery "UNION ALL BY NAME" Improves Your ROI
Google BigQuery now simplifies the entire issue with "UNION ALL BY NAME", a feature that automatically aligns columns based on their names, not their order.
Before this feature:
Merging tables meant manual column matching — a major source of hidden costs and potential errors.
Now, with "UNION ALL BY NAME":
The process becomes smooth, faster, and reliable.
Concrete Example:
Before:
Here, manual column alignment was required.
With "UNION ALL BY NAME":
BigQuery handles column alignment automatically — no manual intervention needed.
This requires a column renaming step beforehand. While it increases the number of rows slightly, we believe the trade-off is worth it, as it makes the code clearer and improves overall quality.
How This Update Directly Impacts Your KPIs
Reduced time-to-insight
Your teams spend less time manipulating data and more time analyzing it.
Result: faster, more reliable decision-making.
Fewer errors & lower financial risk
Fewer mistakes during data merges means fewer decisions based on incorrect insights.
Improved data governance leads to stronger compliance and risk management.
Optimized costs & smarter resource allocation
Less time fixing SQL queries means greater productivity from your technical teams.
Simplified database maintenance reduces your IT operations budget.
Effortless scalability
As your datasets evolve, adding or removing columns no longer affects your queries.
Your infrastructure becomes more agile and future-ready without heavy rework.
Conclusion: A New Strategic Lever for Your Data Transformation
With "UNION ALL BY NAME," Google BigQuery offers a practical solution for organizations looking to streamline their data workflows.
By reducing consolidation time, minimizing errors, and cutting hidden costs, this new feature is a game-changer for both analytical and operational performance.
Need help optimizing your data flows or BigQuery architecture? Get in touch with our experts for a tailored audit.
🛠️ Note: Support for this new feature in SQLFluff — an open-source library that enables its deployment into production — was developed by the author of this article, an Analytics Engineer at Lenstra.
This ensures a smooth and rapid adoption for all technical teams who will use it in the future.
