BigQuery Consulting Services for Cost Control, Performance and Scale
MinervaDB delivers vendor-neutral BigQuery consulting and 24×7 BigQuery support: migration from Redshift, Snowflake, Teradata and Hadoop, data modelling for BigQuery's storage and slot economics, query performance engineering, Editions and reservation sizing, and governance that keeps the bill predictable. Senior engineers only, measured outcomes, no licences sold.
The BigQuery Consulting Firm That Starts From Your Job History
BigQuery is easy to start and hard to run well at scale. On-demand pricing charges per TiB scanned, Editions pricing charges per slot-hour, and both punish tables that were designed like an OLTP schema. Most of the BigQuery estates we are asked to fix are not slow because BigQuery is slow; they are slow and expensive because partitioning, clustering and reservations were never designed from the actual query patterns.
Our BigQuery consulting begins with ninety days of your own INFORMATION_SCHEMA.JOBS history and billing export, ranks every finding by measured cost or latency impact, and only then proposes changes. Every recommendation names the figure that justifies it, and every outcome is reported as the measured delta on your workload, never as a generic multiple. We run BigQuery alongside Snowflake, Databricks, ClickHouse and Redshift for our customers, so when BigQuery is the wrong engine for a workload, we say so.
Vendor Neutral
We sell no licences and take no referral fees. BigQuery, Snowflake, Databricks, Redshift or ClickHouse: the recommendation follows the workload.
Senior Engineers Only
Every engagement is led by engineers with a database-internals background across PostgreSQL, MySQL, ClickHouse and the cloud warehouses.
True 24×7 Support
Named engineers on a follow-the-sun rota. S1 acknowledged in 15 minutes, root-cause analysis after every incident.
Measured, Not Promised
Bytes scanned, slot time, latency and cost are measured before and after every change from your own job history.
Comprehensive BigQuery Consulting Services
From migration and data modelling to slot economics, performance engineering and 24×7 operations, one team covers the whole BigQuery lifecycle.
BigQuery Architecture & Data Modelling
Dataset, table and partitioning layout designed from query patterns rather than the source system.
- Partition columns chosen for the predicates the workload uses
- Clustering keys ordered by cardinality and filter frequency
- Nested and repeated fields where they remove joins
- Materialised views and BI Engine for the dashboard tier
- Before-and-after bytes-scanned and slot-time evidence
BigQuery Performance Engineering
Slow queries diagnosed from execution plans and stage statistics, not from the SQL text.
- Shuffle bytes, slot milliseconds and skew per stage
- Partition pruning that is silently not happening
- Joins on high-cardinality strings and unbounded windows
- UDFs that block parallelism
- Reservations too small for the concurrency
BigQuery Cost Optimisation & FinOps
Ninety days of jobs modelled against on-demand, Standard, Enterprise and Enterprise Plus pricing.
- On-demand for spiky work, committed slots for the steady base
- Autoscaling ceilings and reservation assignments per workload
- Logical versus physical storage billing per dataset
- Table and partition expiration, orphaned datasets
- Quotas, labels, billing export and bytes-scanned alerts
Migration into BigQuery
Phased migrations from Amazon Redshift, Snowflake, Teradata, Greenplum, Oracle, SQL Server, Hadoop and Hive.
- Assessment and SQL translation inventory
- Schema redesign for BigQuery, not a lift-and-shift
- Bulk load through Cloud Storage or Data Transfer Service
- Dual-running with reconciliation queries
- Cutover with a rehearsed rollback
Pipelines & Lakehouse Integration
Ingestion and transformation designed for idempotent loads and reconciliation.
- Storage Write API and Pub/Sub streaming
- Datastream CDC from PostgreSQL, MySQL and Oracle
- Dataflow and Dataform transformations
- BigLake and Apache Iceberg tables for Spark and Trino
- BigQuery behind an operational PostgreSQL or ClickHouse system
Slot & Reservation Engineering
Reservations sized from measured slot usage, split by workload, reviewed against utilisation each quarter.
- Slot utilisation by hour from
JOBS_TIMELINE - Separate reservations for ELT, BI and ad-hoc analysts
- Autoscaling for peaks, commitments for the base
- Fluid scaling and flex slots where they fit
- Quarterly commitment review
Security, Governance & Compliance
Designed for the reality that BigQuery is usually the most widely shared data store in the company.
- IAM at dataset, table, row and column level
- Policy tags and dynamic data masking
- Customer-managed encryption keys
- VPC Service Controls and audit log routing
- GDPR, HIPAA, SOC 2 and DPDP evidence packs
BigQuery Health Check
Fixed-scope review of schema design, job history, slot utilisation, storage billing and security.
- Prioritised findings ranked by cost and latency impact
- The measurement behind every recommendation
- Estimated effect on the monthly bill, labelled as an estimate
- Delivered remotely in one to two weeks
- Usually the first step before a retainer
24×7 BigQuery Support
A retainer with named engineers, not a ticket queue.
- Failed or runaway jobs, slot exhaustion, quota errors
- Pipeline breakages and cost spikes
- Proactive job-history review for regressions
- Monthly report with measured metrics
- Advisory hours for what your team is planning
How BigQuery Consulting Engagements Work at MinervaDB
Four phases, sized to the estate, published here so you know exactly what you are buying.
A read-only role and ninety days of INFORMATION_SCHEMA job, storage and reservation views plus the billing export. The workload profile and a findings report ranked by measured impact.
Partitioning and clustering per table, materialised views and BI Engine, reservation topology, storage billing model and expiration policies. Each decision cites the phase-one measurement.
New layouts built alongside the old, backfilled and reconciled, consumers switched through views. Reservation changes staged with monitoring windows. Nothing dropped until a snapshot exists.
The phase-one analysis re-run on post-change job history, actual bytes-scanned, slot-time and cost deltas reported, runbook and dashboards handed over, analysts trained.
Why Engineering Leaders Choose MinervaDB for BigQuery Consulting
Most BigQuery cost and performance problems are data-modelling problems in disguise, and data modelling is what we have done for two decades.
Database Internals Background
Engineers who understand query planners, storage formats and statistics, applied to BigQuery's execution model.
Inside Your Project
We work through your IAM roles with least privilege; every action is visible in your Cloud Audit Logs.
Runbooks, Not Slide Decks
Every change is documented as a runbook your team can operate from after we leave.
The Right Engine
ClickHouse for sub-second serving, PostgreSQL or AlloyDB for transactions, Snowflake or Databricks when the platform decision is made. We support all of them.
BigQuery Technology Stack & Expertise Matrix
What we measure, where we measure it, and the engagement types that apply.
| Technology / Area | BigQuery Expertise Scope | Engagement Types |
|---|---|---|
| Query performance | Execution plans, stage statistics, shuffle bytes, slot milliseconds, skew, repartitioning, UDF impact | Health Check, Consulting, Support |
| Table design | Partitioning (ingestion time, date, integer range), clustering, nested and repeated fields, materialised views | Consulting, Migration |
| Cost and slots | On-demand vs Editions modelling, reservations, autoscaling, commitments, storage billing model, expiration | Health Check, FinOps, Support |
| Ingestion | Storage Write API, Pub/Sub, Datastream CDC, Dataflow, Dataform, Data Transfer Service | Consulting, Data Engineering |
| Lakehouse | BigLake, Apache Iceberg tables, Spark and Trino interoperability | Consulting, Platform Engineering |
| Migration sources | Redshift, Snowflake, Teradata, Greenplum, Oracle, SQL Server, Hadoop, Hive | Migration |
| Security and governance | IAM, policy tags, masking, CMEK, VPC Service Controls, audit logs, compliance evidence | Audit, Consulting |
| Adjacent Google Cloud | Cloud SQL, AlloyDB, Spanner, Bigtable, Memorystore | Consulting, Support |
BigQuery Consulting Across Data-Intensive Industries
The same evidence discipline, applied to the workloads that make BigQuery bills grow fastest.
SaaS & Product Analytics
Event streams at scale, per-tenant reporting, and the partition design that keeps customer-facing dashboards cheap.
Retail & E-commerce
Order, inventory and clickstream models with the seasonality that breaks fixed reservations.
AdTech & Marketing
Impression-scale ingestion, attribution joins, and the storage billing choices that matter at petabyte volumes.
Financial Services
Governed analytics with column-level policy tags, CMEK and audit evidence for regulators.
BigQuery Health Check & Cost Review
Fixed-price, fixed-scope, delivered remotely. Most customers start here.
Ninety days of job history, schema design, slot utilisation, storage billing and security reviewed; findings ranked by measured impact with estimated savings. Typical turnaround one to two weeks. Credited against a support retainer signed within 30 days.
Source inventory, SQL translation scope, schema redesign plan, load and cutover approach, cost model against your current warehouse, and a written recommendation, including "stay where you are" when that is the honest answer.
Transparent BigQuery Consulting Rates
Priced to the market for senior cloud data-warehouse expertise. No minimum block of hours for remote consulting; retainers include named engineers and severity SLAs.
Architecture, data modelling, performance and cost engineering by senior engineers, billed by the hour.
- Available on short notice worldwide
- Partitioning, clustering and reservation design
- Query performance engineering
- Cost modelling across on-demand and Editions
- No minimum hours
Named engineers, S1 in 15 minutes, monthly health report with measured metrics, advisory hours that roll into project work.
- S1 15 min · S2 12 h · S3 24 h · S4 48 h
- Incident response on jobs, slots, quotas and pipelines
- Proactive job-history review for regressions
- Monthly cost and performance report
- Onboarding assessment and runbook included
Workshops, migration cutovers and on-site delivery for teams that need an engineer in the room.
- Architecture and modelling workshops
- Migration cutover supervision
- Analyst training on cost-aware query patterns
- Executive cost reviews
- Travel billed at cost
All engagements start with a written scope. Test every change in a non-production project first, keep table snapshots before schema changes, and maintain a robust disaster-recovery posture with cross-region dataset replication where the data warrants it.
BigQuery Consulting — Frequently Asked Questions
Both. The 24×7 support retainer can be taken on its own for an existing BigQuery estate. Most customers start with the health check, fix the top findings as a short consulting engagement, then move to the retainer.
Remote consulting is US $195 per hour with no minimum, on-site consulting US $350 per hour plus travel, the health check and cost review US $6,500 per project, and 24×7 support retainers start at US $3,500 per quarter. Migrations are quoted on a fixed scope after the assessment.
Usually, and we prove it from your own job history before recommending anything. The common levers are partition and clustering fixes that cut bytes scanned, committed slots for steady workloads, physical storage billing for compressible data, and expiration policies. We do not publish generic savings percentages.
Amazon Redshift, Snowflake, Teradata, Greenplum, Oracle, SQL Server, Hadoop and Hive, and on-premises PostgreSQL and MySQL reporting databases, with SQL translation, schema redesign, dual-running with reconciliation and a rehearsed cutover.
Yes. Reservations are sized from measured slot usage, split by workload with assignments, autoscaled for peaks, and reviewed against actual utilisation each quarter.
Remote engagements typically start within a few business days of a signed scope. Production incidents on the support retainer are handled immediately under the S1 target.
Through your own IAM roles and service accounts with the least privilege the work needs. Every action is visible in your Cloud Audit Logs, and access is removed when the engagement ends.
Yes. BigLake, Iceberg, Datastream, Dataflow, Dataform, Pub/Sub, Cloud SQL, AlloyDB and Spanner are within the same practice, alongside our Google Cloud data platform engineering and data analytics platform engineering service lines.