BigQuery Consulting
Drive high-performance BigQuery analytics with optimized architecture.
Transform data into actionable intelligence for unmatched scale and cost-efficiency.
Improve data integrity
Resolve silent pipeline failures and implement advanced partitioning to reduce data processing by up to 90%, and achieve predictable monthly spend.
Gain limitless cloud elasticity
Transition from rigid legacy to elastic architecture, and automate converting complex legacy code into high-performance, cloud-native SQL.
Establish ongoing governance
Ensure your data is continuously governed, free up your team to focus on insights rather than infrastructure.
How we work with you
Gain complete visibility into data lineage and warehouse spend.
Audit all datasets and query patterns to uncover hidden inefficiencies and map how data moves through the organization. Receive a prioritized action plan to stop budget leaks and fix performance bottlenecks immediately.
Build a right-sized foundation for enterprise-scale performance.
Refactor schemas through partitioning and clustering aligns storage with specific query patterns, ensuring critical dashboards always receive priority slots. Integration with BigLake creates a high-performance environment that scales automatically without manual intervention.
Make governed insights accessible to every business team.
Connect BigQuery to preferred BI platforms like Looker or Tableau and build semantic layers that empower self-service analytics. The implementation of materialized views ensures teams receive fast, trusted reports without waiting for engineering support.
Maintain ongoing cost governance and performance reliability.
24/7 monitoring and financial guardrails proactively stop runaway queries before they impact the budget. Continuously tune reservations and pipeline health to ensure data is fresh, secure, and cost-effective every single day.
Unlock real-time insights and limitless scale with a serverless BigQuery architecture.
Modernize your data estate with seamless
BigQuery migration and expert architectural design.
Unlock BigQuery’s serverless speed, performance, and cost control.
Modern cloud platform expedites customer analytics for a global telecommunications provider
Pythian migrated the company to a BigQuery environment, enabling near real-time analytics in minutes.
40%
Reduction in costs
10x
Faster time-to-insight
<15
Seconds to query terabytes
Frequently asked questions (FAQ) about BigQuery consulting services
BigQuery's on-demand model charges $6.25 per TiB of data scanned. One bad SELECT * can cost hundreds of dollars. We implement partitioning and clustering to cut scan costs by up to 40 percent, then refactor queries to reduce data processed by up to 90 percent. We recommend the right edition (Standard at $0.04 per slot-hour, Enterprise at $0.06, or Enterprise Plus at $0.10) vs. on-demand pricing for predictable monthly spend. BigQuery ML lets your team deploy models in SQL without separate infrastructure. You see returns on high-value workloads in weeks.
Through BigLake and Object Tables, BigQuery can now perform analytics on unstructured data like images, PDFs, and audio files stored in Google Cloud Storage. Pythian helps you set up these "Lakehouse" features so you can use SQL to call AI models that summarize or categorize these files.
We configure Google Cloud IAM with least-privilege access, column-level security, dynamic data masking, and audit logging through Cloud Audit Logs. For regulated industries, we align BigQuery with HIPAA, SOC 2, PCI DSS, and GDPR using Google's compliance frameworks. VPC Service Controls and DLP scanning protect sensitive data at rest and in transit. Dual-run validation during migration confirms zero security gaps.
Complexity depends on your source platform, data volumes, and proprietary code. We translate legacy SQL (BTEQ, PL/SQL, Netezza stored procedures) to BigQuery Standard SQL with automated tools. Complex logic that automation misses gets manual engineering from teams who know both platforms. We configure BigLake for lakehouse workloads and BigQuery Omni for querying AWS/Azure data without egress fees. Both environments run in parallel during validation.
BigQuery handles both. The streaming ingestion API and Pub/Sub integration let you query data within seconds of arrival. Change data capture (CDC) through Storage Write API keeps dashboards current with live operational data. Pythian designs architectures that combine batch and streaming in a single BigQuery environment.
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