Building a data warehouse in BigQuery requires precision at every stage - from schema design, to cost optimization, to integration of data from different sources. Many data engineers focus on the advanced features of BigQuery, forgetting the fundamentals: a well-thought-out data architecture, proper SQL query management or ETL process automation.
The result is systems that, instead of acting as powerful analytical tools, generate unforeseen costs, slow down teams and limit an organization's capabilities. Based on completed projects with clients in various industries, we discuss five of the most common BigQuery mistakes. Each is a practical lesson on how to avoid pitfalls and build a data warehouse that provides reliable and timely information, optimizing costs and supporting decision-making.
Mistake 1: Lack of thoughtful data architecture
A poorly designed data architecture is like building a house on an unstable foundation - sooner or later cracks will appear. Lack of data schema design strategy leads to query performance problems, data inconsistencies and unnecessarily high processing costs.
In the case of our client PŚO, the main challenge was to gather data from various transactional databases and create a central data warehouse in BigQuery. Alterdata specialists appropriately designed a star schema-based framework so that the company could efficiently perform analysis and generate reports with a high level of detail. What's more, the data was consistent, so that a single source of truth, provided the basis for making accurate decisions.
Recommendations from our experts:
- When designing a data schema, find a balance between normalization and denormalization. BigQuery handles nested (nested) and repeated (repeated) data well, allowing you to create schemas that are both efficient and easy to use, without strictly adhering to classic relational models.
- It is necessary to implement real-time data quality monitoring to identify potential deficiencies and errors.
Mistake 2: Insufficient cost control
BigQuery is a tool that can quickly generate unanticipated costs in a pay-as-you-go model. When queries scan unnecessary data, and records of marginal value are stored in the warehouse, budgets can get out of control.
In the case of our ecommerce client, backlogged data and suboptimal SQL queries were causing unnecessary scanning of large volumes of data. The problem was solved by systematically optimizing queries and removing unnecessary data sets, such as unused test data. The company gained not only 30% cost savings from BigQuery, but also accelerated analytical operations.
Our recommendations:
- Introduce budget limits in Google Cloud and regular cost audits.
- Monitoring SQL queries and analyzing their execution costs using tools available in BigQuery.
Mistake 3: Ineffective management of integrations
Integrations between systems are often the Achilles' heel of data projects. When data arrives late or is inconsistent, the company loses the ability to respond quickly to market changes. The problem becomes even worse when data from different systems is trapped in silos, making comprehensive analysis difficult.
In the case of a wholesale client, data from two different 3rd party systems was not synchronized in real time, making it difficult to conduct marketing campaigns and sales analysis. Our custom solution built in BigQuery allowed for seamless integration of real-time data, improving the efficiency of operations.
Another example is the implementation of a central data repository in BigQuery for a PŚO company. CRM and ERP data were synchronized, allowing full control over operational processes.
Alterdata engineers' recommendations:
- Implementing tools such as Pub/Sub for real-time data integration significantly improves the flow process and maintains data quality.
- Build a reverse ETL solution to provide bi-directional data flow between systems, enabling better business decisions based on up-to-date data from the warehouse, available directly from operational systems.
Mistake 4: Suboptimal SQL query design
Suboptimal SQL queries not only slow down reporting, but also generate excessive costs. Many companies face problems such as the use of SELECT *, lack of filter conditions or indexes that cause excessive data scanning.
In the case of the previously mentioned ecommerce client, data analysis was significantly slowed down by suboptimal SQL queries. After implementing a query monitoring process and making adjustments, our client's report generation time was reduced from hours to minutes, while operating costs were reduced.
Our recommendations:
- Implement code review processes for SQL queries and monitor their performance.
- Regular training of teams on query optimization and best practices in BigQuery.
Mistake 5: Lack of automation and orchestration of processes
Manually managing ETL processes is time-consuming and error-prone. Lack of automation prevents rapid implementation of changes, and orchestrating multiple processes without dedicated tools leads to operational chaos.
As part of our work with the CSP, we implemented Apache Airflow for ETL process management and Dataform for data modeling. The automation allowed for better control of processes, faster deployment of new solutions and much higher stability of the entire system.
Recommendations:
- Use of tools such as Apache Airflow, dbt or Dataform to automate ETL and data modeling processes.
- Implement CI/CD to automate testing and deployment, minimizing the risk of errors.
Summary
Each of the mistakes described - from lack of thoughtful architecture to insufficient automation - can limit the potential of a data warehouse. Their elimination allows not only to save costs, but also to make full use of data in the organization. Projects completed for our clients show that the right approach to system design in BigQuery can bring tangible operational and business benefits.
Your data can act as a precise mechanism that supports your organization in making quick and accurate decisions. If you want to build or optimize your data warehouse in BigQuery, schedule a free consultation to analyze its architecture and identify potential areas for improvement.
