Introduction
Building a BigQuery data warehouse requires precision at every stage – from data modeling and designing a flexible data warehouse architecture, through optimizing data storage costs (including long-term storage), effective storage of both raw and historical data, to integrating data from various sources and easy data transfer between Google tools. Unlike traditional relational databases, the scalable BigQuery data warehouse eliminates the need to manage servers, allowing flexible adjustment of resources to current business needs and focusing on analyzing large datasets. A key aspect is also loading data and creating data structures using SQL queries. BigQuery operates as a serverless platform, automatically scaling resources and processing SQL queries, enabling the use of advanced analytical tools in a cloud environment.
The BigQuery data warehouse plays a vital role in business intelligence, enabling advanced analysis and reporting. Thanks to the data warehouse, centralization and integration of data from multiple sources are possible, which translates into faster and more reliable business decision-making. Its significance for business strategy is invaluable – BigQuery makes data analysis and visualization simpler, and access to up-to-date information instantaneous. BigQuery is used in digital marketing, logistics, industry, e-commerce, and many other sectors. Typical use cases include large dataset analysis, real-time reporting, and business process optimization. Below we present practical examples of BigQuery use and the five most common mistakes to know to build an efficient and scalable data warehouse.
Mistake 1: Lack of thoughtful data architecture
A poorly designed data warehouse architecture is like building a house on an unstable foundation – cracks will appear sooner or later. Data modeling is a key stage in designing the data warehouse architecture because it allows creating consistent and efficient structures that facilitate analysis and integration of information from various sources. It is worth emphasizing that a data warehouse differs from traditional databases – its task is to aggregate and provide data for analytical purposes, while databases mainly serve current transactional operations.
In the case of our client PŚO, the main challenge was collecting data from various transactional databases and creating a central data warehouse in BigQuery (https://alterdata.com/case-studies/pso/). This process involved integrating data from multiple databases, which is a crucial stage in building a modern data warehouse. Alterdata specialists appropriately designed a schema based on the star schema so the company could efficiently perform analyses and generate highly detailed reports. Moreover, the data was consistent, providing a single source of truth as a basis for making accurate decisions in analytical processes and optimizing marketing activities.
Our experts' recommendations:
- When designing the data schema, find a balance between normalization and denormalization. BigQuery handles nested and repeated data well, which allows creating schemas that are both efficient and user-friendly, without strictly adhering to classical relational models.
- It is essential to implement real-time data quality monitoring to identify potential gaps and errors and ensure data security in the cloud.
Mistake 2: Insufficient Ccontrol of data storage costs
Google BigQuery is a tool that in a pay-as-you-go model can quickly generate unexpected costs. The cost of storing data in BigQuery is charged based on the volume of data stored, and after 90 days of inactivity, data is automatically moved to a cheaper long-term storage model, which helps optimize expenses. How much does Google BigQuery cost? The pricing structure includes fees for storage, query processing, and free limits and cost calculators that help plan budgets and control expenses. When queries scan unnecessary data and the warehouse stores records of marginal value, the budget can get out of control.
In the case of our client from the e-commerce sector, obsolete data and suboptimal SQL queries caused unnecessary scanning of large data volumes. The problem was solved by systematic query optimization and removing unnecessary datasets such as unused test data. The company gained not only savings of about 30% in BigQuery costs but also acceleration of analytical operations.
Our recommendations:
- Implement budget limits in Google Cloud Platform and conduct regular cost audits.
- Monitor SQL queries and analyze their execution costs using tools available in BigQuery.
- Optimize SQL queries by using standard queries and avoiding excessive data scanning, which allows paying only for actually used resources.
Mistake 3: Inefficient management of integrations from various sources
Data integration between systems is often the Achilles' heel of data projects. A key process here is data integration, i.e., combining different sources of information such as Small Data, Thick Data, and Big Data to get a complete picture of company operations. An important stage is also loading data from various systems into the data warehouse, enabling further analytical processes. When data arrives with delays or is inconsistent, the company loses the ability to respond quickly to market changes. The problem becomes even bigger when data from different systems is trapped in silos, making comprehensive analysis difficult.
In the case of a client from wholesale trade, data from two different third-party systems was not synchronized in real-time, which hindered marketing campaigns and sales analysis. Our custom solution built in BigQuery enabled seamless real-time data integration, improving marketing efficiency.
Another example is implementing a central data repository in BigQuery for the PŚO company. Data from CRM and ERP were synchronized, allowing full control over operational processes.
Alterdata engineers' recommendations: Check out Alterdata's data engineering services that support company growth and optimize data management.
- Implement tools such as Pub/Sub for real-time data ingestion to significantly streamline data flow and maintain high data quality.
- Build a reverse ETL solution to ensure two-way data flow between systems, enabling better business decisions based on current data from the warehouse, directly accessible in operational systems.
- Use easy integration with other Google tools and external tools, allowing aggregation of data from distributed sources and effective use. BigQuery also enables easy data transfer and loading between Google tools, significantly simplifying implementation and data management in the cloud environment.
Mistake 4: Suboptimal SQL query design
Suboptimal SQL queries not only slow down reporting but also generate excessive costs. In BigQuery, you can create tables and views using SQL queries, allowing flexible data structure management. Optimizing SQL queries improves analytical processes, enabling faster and more efficient data processing. Many companies struggle with problems such as using SELECT *, lack of filtering conditions, or indexes, causing excessive data scanning.
In the case of the previously mentioned e-commerce client, data analysis was significantly slowed by suboptimal SQL queries. After implementing query monitoring and making corrections, the report generation time shortened from hours to minutes, while reducing operational costs.
Our recommendations:
- Implement code review processes for SQL queries and monitor their performance.
- Conduct regular training for teams on SQL query optimization and best practices in BigQuery.
- Use advanced tools for SQL query optimization that allow fast execution of complex queries and effective extraction of data insights from large datasets, thus streamlining analytical processes.
Mistake 5: Lack of automation and orchestration of processes
Manual management of ETL processes is time-consuming and error-prone. Lack of automation prevents quick implementation of changes, and orchestrating multiple processes without dedicated tools leads to operational chaos. Tools like Dataform and dbt support data modeling in ETL processes, allowing the design of consistent and efficient data warehouse structures.
In cooperation with PŚO, we implemented Apache Airflow to manage ETL processes and Dataform for data modeling. Automation allowed better process control, faster implementation of new solutions, and much higher system stability.
Recommendations:
- Use tools such as Apache Airflow, dbt, or Dataform for automating ETL processes and data modeling.
- Implement CI/CD to automate testing and deployments, minimizing error risks.
- Automating ETL processes improves analytical workflows, enables advanced data analysis, and allows effective management of large data volumes, supporting decision-making processes and enabling quick response to changes in a dynamically changing business environment.
After discussing the five most common mistakes when building a BigQuery data warehouse, it is worth supplementing this list with additional aspects that are crucial for effective and secure use of this tool. These points help understand how BigQuery works in practice, what are the best practices related to data governance, data security, real-time processing, and the use of advanced techniques such as machine learning. Thanks to this, the article becomes more comprehensive and provides readers with fuller knowledge necessary for informed decisions and optimization of marketing activities and business processes.
Data Security in BigQuery
Data security in BigQuery is the foundation on which trust in every data warehouse is built, especially when it comes to storing and analyzing large datasets from various sources. BigQuery offers advanced protection mechanisms that allow organizations to effectively secure their enterprise data in the cloud. Encryption of data both during transmission and at rest ensures that even in the event of unauthorized access, information remains unreadable to third parties.
Additionally, BigQuery enables precise permission management through role-based access control (RBAC), limiting access to sensitive data only to selected users or teams. Audit and activity monitoring features provide full transparency of operations performed on large datasets, and tools such as Data Loss Prevention (DLP) help identify and protect sensitive data from leaks.
Thanks to these solutions, BigQuery offers not only scalability and performance but also a high level of data security, which is crucial for companies that want to build a competitive advantage based on a trusted and secure analytical environment.
Real-time data processing
In a dynamically changing business environment, access to real-time data becomes essential for companies that want to make accurate decisions and respond quickly to market changes. BigQuery enables processing and analyzing data in real time, opening new possibilities for mobile applications, websites, and business decision support systems.
Thanks to integration with tools like Google Cloud Pub/Sub or Dataflow, BigQuery allows smooth loading and aggregation of data coming from various data sources in real time. This solution works especially well for analyzing user behavior, monitoring marketing campaign effectiveness, or managing operational processes where every second counts.
The ability to instantly process and visualize data allows organizations not only to draw conclusions faster but also to implement strategies based on the most current information. As a result, companies using BigQuery can build flexible and scalable data warehouses that support business decision-making based on the most up-to-date data available in real time.
BigQuery ML – Machine Learning in the Data Warehouse
BigQuery ML is a breakthrough feature that allows using machine learning directly in the data warehouse without the need to export large datasets to external tools. Thanks to this, data analysis and building predictive models become accessible to analytical and business teams, even without advanced programming knowledge.
BigQuery ML allows training machine learning models using standard SQL queries, significantly accelerating the implementation of advanced analytics. The use of technologies such as TensorFlow or scikit-learn enables analyzing large datasets, predicting trends, customer segmentation, or optimizing marketing activities on an unprecedented scale.
Thanks to BigQuery ML, companies can not only discover hidden patterns in historical and raw data but also automate decision-making processes and implement strategies based on prediction. This tool opens new opportunities for organizations that want to leverage the potential of machine learning to build a competitive advantage and effectively manage large datasets.
Summary
Each of the described mistakes – from lack of thoughtful architecture to insufficient automation – can limit the potential of a BigQuery data warehouse. Eliminating them allows not only cost savings but also full utilization of data in the organization. Projects carried out for our clients show that the right approach to designing a system in BigQuery can bring measurable operational and business benefits. Practical examples of BigQuery use include large dataset analysis, real-time SQL queries, and applications in various industries such as logistics or digital marketing. Its significance lies in making data analysis more accessible and efficient, and as a scalable data warehouse, it enables flexible management of both historical and raw data.
Your data can act as a precise mechanism that supports the organization in making quick and accurate decisions. Using machine learning and artificial intelligence combined with BigQuery opens new possibilities for advanced analytics and discovering hidden patterns in historical and raw data. The importance of a data warehouse lies in enabling efficient storage, integration, and analysis of large datasets, which translates into competitive advantage. Typical BigQuery use cases include demand forecasting, business process optimization, or customer behavior analysis. If you want to build or optimize your BigQuery data warehouse, schedule a free consultation to analyze its architecture and identify potential areas for improvement.
