20+ TB of Replicated Data to BigQuery

In 70+ data warehouse projects

100% Satisfied Customers

Avg. rating of 5★ on Clutch

Top BI & Big Data Company in Poland

According to the Clutch 2023 ranking

Top BI & Big Data Company in Poland

Build Your Future-Proof Data Warehouse

Our certified Google Cloud experts help you build a scalable, secure, and cost-effective data foundation ready for advanced analytics and business intelligence.

Ready for a scalable and cost-effective solution?

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Our BigQuery Services

Your Partner for Building BigQuery Success

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Implementation

We design and build scalable BigQuery data warehouses from the ground up, handling everything from optimal schema design and data modeling to efficient data loading pipelines, tailored to your analytical requirements.

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Consulting

Get expert guidance on BigQuery best practices, including query performance optimization, effective cost management, data governance, security configurations, and data warehouse architecture.

Training

We deliver customized BigQuery training workshops tailored to your team's current skill level and your company's unique use cases, ensuring practical, directly applicable learning outcomes.

Why Us

Data Partners Who Deliver Results, Not Just Reports

Choosing the right analytics partner is crucial. We offer proven experience and certified expertise specifically within the Google Cloud ecosystem. Our track record demonstrates our commitment to delivering reliable data solutions that help businesses like yours grow.

Data Replicated to BQ

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Completed Projects

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Years of Experience

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Case Studies

Success Stories of Our Clients

How did we cut analytics and data warehouse maintenance costs by 80% with Google Cloud?

How did we implement a fraud detection system for transactions and reduce them by 90%?

Why BigQuery?

The Power of Serverless Data Warehousing

The serverless foundation of Google BigQuery offers fundamental advantages over older data warehouse technologies by removing operational bottlenecks.

Your organization gains the ability to query massive datasets performantly and scale resources instantly without complex manual intervention or extensive capacity planning.

This allows technical teams to concentrate fully on strategic data initiatives instead of time-consuming infrastructure maintenance.

Serverless Operation

Focus on analysis, not server management.

Optimized Cost

Optimize spending with usage-based pricing.

Integrated ML

Create and run machine learning models using SQL.

Fast SQL Queries

Analyze massive datasets within seconds.

About bigQuery

Frequently Asked Questions

Google BigQuery is Google Cloud's fully managed, serverless enterprise data warehouse service, specifically designed for high-speed analysis of very large datasets using standard SQL.

Serverless means you don't need to provision, manage, or scale any underlying server infrastructure yourself; BigQuery automatically handles resource allocation based on your actual usage.

BigQuery typically charges based on data storage volume and the amount of data processed by your queries (compute), offering pay-as-you-go or flat-rate options. Its serverless nature and optimization techniques can make it very cost-effective compared to traditional data warehouses, but actual costs depend heavily on usage patterns and optimization.

Key differentiators often include BigQuery's truly serverless architecture (others may require cluster management), its deep integration within the Google Cloud ecosystem (including BigQuery ML), and its specific pricing models. The best choice depends on your specific technical requirements and existing infrastructure.

Yes, BigQuery leverages Google Cloud's robust, multi-layered security infrastructure. It provides strong features like data encryption at rest and in transit, fine-grained Identity and Access Management (IAM), and options for row-level and column-level security to protect your data assets.

While SQL is standard, experts are crucial for designing optimal data architectures within BigQuery (schemas, partitioning, clustering), building efficient data pipelines, writing highly performant and cost-effective queries, and correctly implementing security and governance best practices to maximize value.

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