Google BigQuery is a fully managed, serverless data warehouse and analytics engine from Google Cloud that helps companies quickly analyze large volumes of information and integrate machine learning capabilities. BigQuery allows you to work with structured, semi-structured, and some types of unstructured data while ensuring a high level of security and scalability.
High Performance: Process terabytes of data in seconds and petabytes in minutes.
Scalability: Architecture with separated storage and compute.
Serverless: No need to manage infrastructure.
Flexible Pricing: A pay-as-you-go model where you only pay for the resources you use.
AI Integration: Support for BigQuery ML and Gemini to generate and analyze data directly within SQL.
BigQuery separates storage and compute: data is stored in a distributed storage system, while computational tasks are executed on separate clusters connected via Google’s Jupiter network. This provides high speed, reliability, and cost-effectiveness.
Working with BigQuery typically involves several stages:
Connecting Data Sources — Integration with cloud and on-premises storage, analytics systems (GA4, CRM, ERP), and streaming services.
Loading and Storage — Data is saved in BigQuery’s distributed, highly reliable storage, which supports various formats (CSV, JSON, Avro, Parquet, ORC).
Processing and Transformation — Executing SQL queries, transforming and cleaning data, and joining it from various sources.
Analytics and Modeling — Using BigQuery ML, built-in functions, and integrations with AI tools for forecasting, clustering, and classification.
Visualization and Export — Connecting BI tools (Looker, Data Studio, Power BI) or exporting data to other systems.
HSBC — analyzes billions of transactions to detect financial crimes. Analytics speed has been increased tenfold.
Deutsche Börse Group — uses BigQuery in its D7 digital platform for analytics and AI implementation.
CME Group — scales its infrastructure and implements real-time analytics as part of a 10-year partnership with Google Cloud.
BigQuery ML — create and run ML models directly within the BigQuery environment.
BigQuery Studio — a unified workspace for analytics and code development, including generative AI.
BigLake — access data across different clouds and formats (S3, Azure Storage, Iceberg, Delta Lake, Hudi).
BigQuery Omni — multi-cloud analytics without moving data.
Data Governance — integration with Dataplex, Analytics Hub, and Data Clean Rooms for secure data sharing and control over exports.
Google BigQuery is used by companies worldwide to solve a wide range of challenges:
Customer Data Analysis: Identifying patterns in behavior and preferences to personalize offers and improve customer service. It enables the creation of a complete 360° customer profile.
Marketing Campaign Optimization: Increasing ROI through precise targeting and strategy adjustments. It integrates with Google Analytics 4 (GA4) for real-time audience segmentation.
Fraud Prevention: Analyzing large volumes of transactions to detect suspicious activities in real-time.
Risk Management: Using predictive analytics to assess and mitigate business risks.
New Product Development: Creating services and solutions based on identified customer needs.
Modern companies face a number of challenges:
Unstructured Data: Up to 90% of corporate data exists as text, images, videos, documents, or audio recordings. BigQuery supports working with such data, including through Google’s AI tools.
The Need for Real-Time Analytics: Gaining quick access to key metrics and events without waiting for lengthy calculations.
Infrastructure Fragmentation: Data is often distributed across different systems, which slows down analytics. BigQuery unifies it in a single environment.
Growing Security Demands: Built-in encryption, column-level access control, and integration with Dataplex help meet high security standards.
Difficulties with Scaling AI: BigQuery simplifies the implementation of machine learning and the integration of generative AI.
Taking your first step with BigQuery is surprisingly simple thanks to its SQL interface. For example, you can query public datasets to see its capabilities in action.
SELECT
state,
SUM(total_amount) AS total_sales
FROM
`bigquery-public-data.samples.chicago_taxi_trips`
WHERE
EXTRACT(YEAR FROM trip_start_timestamp) = 2023
GROUP BY
state
ORDER BY
total_sales DESC
LIMIT 10;
This query will analyze Chicago taxi trip data from 2023 to show the total sales amount by state. In a real-world scenario, you would replace
bigquery-public-data.samples.chicago_taxi_trips
with your own data table.
As an experienced Google Cloud partner, Elcore Group is ready to help your business unlock the full potential of BigQuery. We offer comprehensive services that cover the entire data lifecycle, from strategy to implementation and optimization.
Pilot Projects (POC) and BigQuery ML Implementation
We will help you quickly test and implement BigQuery and BigQuery ML to solve specific business challenges.
This includes:
Assessing your current data environment and identifying key use cases.
Building a reliable foundational Google Cloud Data Foundation (IAM, BigQuery, Cloud Storage).
Migrating or creating ETL/ELT pipelines to move selected data (e.g., from Google Analytics 4 / Universal Analytics) into BigQuery.
Developing and deploying training and prediction pipelines using BigQuery ML.
Configuring security and connecting business intelligence tools like Looker/Looker Studio.
Expert Support
Our team includes certified architects, data engineers, and project managers who will provide technical leadership and deep expertise at every stage of the project.
Support and Training Programs
We can help you access various Google Cloud programs, such as:
Migration and Testing Credits: To fund migrations, proofs of concept (POCs), and new product testing.
Partner Funding: To support projects through professional services.
Google Cloud Skills Boost Team Trial: Free access to Google Cloud training to upskill your team.
Google BigQuery is not just a data warehouse; it is a unified, serverless, and highly scalable platform that serves as the cornerstone for implementing AI in your business. It allows you to not only work efficiently with all types of data but also to gain real-time insights, build complex ML models, and make informed decisions, all while significantly reducing the Total Cost of Ownership (TCO).
If you are looking to transform your business, increase operational efficiency, and get ahead of the competition by leveraging the power of big data and artificial intelligence, BigQuery is the solution you need.
