Making complex data in the data warehouse easy to process and understand

Different data sources, such as CRM systems, GPS trackers, or sensors, provide data in their own unique structures. Certain types of data, like satellite imagery or financial transactions, inherently involve large volumes. A data warehouse helps curate, clean, and structure such complex data, making subsequent analysis and reporting easier. The more complex the data, the greater the challenge for the data warehouse.

BigQuery: The All-Rounder for Complex Data

For the analysis and processing of complex data, BigQuery, Google Cloud’s powerful data warehouse solution, is particularly well-suited. BigQuery stands out for its ability to quickly and efficiently process large and diverse datasets without the need for maintenance. The solution automatically scales to handle vast amounts of data. Additionally, BigQuery can manage complex SQL queries in real time, providing deeper insights and enabling the monitoring of business processes.

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A Special Case: Working with Geospatial Data

From our extensive experience working with geospatial data, we understand how crucial it is to have complex data well-structured and cleaned for accurate processing. Geospatial data often contains both spatial and temporal information and comes in various formats. When processing this data, it is often necessary to combine different data types and meaningfully integrate them—ranging from traffic and weather data to sensor data—so they can be related to one another, enabling comprehensive analyses.

Common challenges with geospatial data include:

• Spatial Referencing: Geospatial data must be accurately positioned on the Earth's surface, often requiring conversions between different coordinate systems.
• Data Volume: Geospatial data, particularly satellite imagery, typically involves massive datasets, which can overwhelm traditional systems.
• Integration: Geospatial data frequently needs to be combined with other data types, such as demographic or weather data, to enable comprehensive analyses.

Geospatial Data and BigQuery: A Perfect Symbiosis

BigQuery offers native support for geospatial data, enabling businesses to analyze it directly using SQL. Even extremely large datasets, such as satellite imagery, are processed quickly and efficiently by BigQuery. This makes BigQuery the ideal choice for companies that regularly work with geospatial data, such as those in logistics, urban planning, or environmental research.

• GIS Extension (Geographic Information System): Enables SQL queries on polygons, lines, and points without the need for additional tools or learning new languages.
• Seamless Integration: As part of the Google Maps Platform, BigQuery allows seamless integration with common Google Cloud services for geospatial data, such as Google Maps or Google Earth Engine.
• Handling Structured and Unstructured Data: Different data sources and formats can easily be integrated and connected in BigQuery, ensuring seamless analysis of heterogeneous datasets.
• BigQuery ML: Machine learning models can be written and trained directly in BigQuery, making it especially valuable for geographic prediction models and real-time analyses.

Case Study: Supply Chain Optimization

Optimizing supply chains is one of the most complex tasks for modern companies. It requires the integration and analysis of various data sources, such as supplier data, traffic data, and weather data, to make the entire logistics process more efficient. This is where both complex data and geospatial data come into play, as they must be used together to monitor and adjust the supply chain in real time. In the data warehouse, these datasets are structured and cleaned to facilitate analysis and reporting for supply chain optimization. By integrating ML tools, companies can train machine learning models to predict future supply shortages, transport delays, or weather events

Our solutions for optimizing your supply chain