Platforms > BigQuery

Google BigQuery Consulting

Alphabyte provides Google BigQuery consulting. We build end-to-end data warehousing solutions on BigQuery, Google's serverless enterprise data warehouse, so your team can analyze large volumes of data quickly and get to the business metrics that matter without waiting on manual reports.

Our data engineers handle design, pipelines, reporting infrastructure and ongoing support, so the warehouse keeps up as your data grows.

Success Stories

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What We Do

BigQuery consulting for data warehousing and reporting that scales with your data.

We use Google BigQuery to build data warehousing solutions shaped around how your organization works. Our data engineers design the datasets and data model, build pipelines from each source system and set up the reporting infrastructure on top, so your dashboards and analysts read from one trusted, current source.

Most organizations call a BigQuery consultant when reporting depends on spreadsheets and manual exports, when data volumes have outgrown an older database, or when BigQuery is already in use but queries are slow and costs are hard to predict. We start with the questions your business needs answered and design the warehouse around them.

We also build warehouses on Snowflake, Amazon Redshift, Microsoft Fabric and Azure SQL. If you are still choosing, our Azure SQL vs Snowflake vs BigQuery comparison sets out the main trade-offs.

01

Scalable Architecture

Warehouses designed to handle growing datasets without redesign or added infrastructure.

02

Query Speed

Tables, partitions and queries tuned so large datasets return results quickly.

03

Ease of Use

Familiar SQL, plus connections to Looker Studio, Jupyter Notebooks and BI tools.

04

Cost Management

Pay-as-you-go usage kept in check with query design and spending controls.

05

Serverless Setup

No servers to run, so your team focuses on data, not infrastructure.

06

Security & Access

Encryption, role-based access and Google Cloud IAM set up for sensitive data.

07

Data Integration

Pipelines from Google Cloud services and third-party tools into one warehouse.

08

Real-Time Analytics

Streaming data loads for dashboards that need current numbers, not yesterday's.

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Our Approach

  • Over 1 to 2 weeks we assess your business needs, data sources and existing infrastructure. We talk to the people who rely on the data, review current databases and reports, and agree on the questions BigQuery must answer. If you already run BigQuery, we also review query patterns, performance and spend.

  • We turn what we learned into a data strategy and roadmap built around your business goals. It sets out which sources come first, how BigQuery fits with your current systems and reporting tools, and how the setup will scale.

  • We design the BigQuery architecture, covering projects and datasets, the data model, partitioning and clustering, access roles and the pipelines that load each source. Queries are planned around how your teams actually report, so performance stays good and costs stay predictable. We review the design with you before any build work starts.

  • Over about 6 to 12 weeks for a first release, we set up the environment, build the pipelines, load historical data and connect your reports. Work runs in phases, so your first reports move to BigQuery early and more sources follow, with monitoring and alerts on every scheduled load from the start.

  • Before anyone relies on a number, we reconcile it against the source systems and test queries under realistic use. Then we refine table design and query patterns based on how people actually use the warehouse, and set up spending controls so usage stays visible to your finance and data teams.

  • We train your analysts and administrators on how the warehouse is organized, how to write efficient queries and how to manage access and costs. Clear documentation covers the data model, pipelines and security setup, so your team can add reports and sources with confidence and without waiting on us.

  • We provide ongoing support with performance monitoring, query optimization and troubleshooting. That includes fixing failed loads, reviewing spend, managing access and adding new sources as your business grows. You can keep us on for day-to-day support or bring us in for specific projects when you need more capacity.

Your BigQuery Consulting Team

Data engineers and BI developers work together on one Alphabyte team, so your BigQuery warehouse is built for the reports and analysis you actually need. We are a Microsoft Solutions Partner with an AWS Certified Solutions Architect on the team, and we have served 100+ organizations since 2015.

BigQuery suits organizations already on Google Cloud. For reporting on BigQuery we work with tools including Power BI and Looker.

We work with mid-market and enterprise organizations. Related services: data warehousing services, data migration services and data analytics & BI consulting.

Get Started
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Frequently Asked Questions

  • Google BigQuery is a fully managed, serverless data warehouse on Google Cloud. It runs fast SQL queries over very large datasets, and you pay for the storage and processing you use. There are no servers to set up or maintain, and it connects with other Google Cloud services and many third-party tools. Organizations use it to bring data from many systems together for reporting and analysis.

  • A BigQuery consultant designs, builds and tunes your BigQuery environment. That covers the architecture and data model, pipelines from each source system, access and security, loading historical data and keeping query costs under control. At Alphabyte the same team also builds the reporting infrastructure on top, so the warehouse is shaped around the reports and questions your business actually needs answered.

  • Most organizations bring in outside help when they are setting up BigQuery for the first time, moving data from an older database, or already using BigQuery with slow queries or rising costs. Help also makes sense when several teams build their own datasets and the numbers stop matching. Getting the design, access and cost controls right early is far easier than fixing them later.

  • It depends on the number of data sources, data volume, how much history you load, how clean the data is and how many reports need to connect. BigQuery usage is billed separately by Google, based on the storage and queries you use. We begin with a short discovery, scope the first release with you before work starts, and design the setup to keep ongoing usage costs predictable.

  • Both are capable cloud data warehouses. BigQuery is serverless and fits well when your systems and data already sit on Google Cloud. Snowflake suits organizations that want multi-cloud flexibility and live data sharing. Platforms such as Microsoft Fabric or Azure SQL fit teams already on Microsoft 365 and Azure. We recommend a platform based on your existing systems, skills, data volume and reporting tools, not a preference of our own.

  • Both are fully managed cloud data warehouses. BigQuery is serverless, so there is no infrastructure to size and you pay for the storage and processing you use. Amazon Redshift runs on AWS and fits organizations whose systems already sit there, with close ties to other AWS services. The better choice usually depends on which cloud you already use, your team's skills and how your workloads behave.

  • Discovery usually takes 1 to 2 weeks. A first release, with your main sources loaded, the data model built and reports connected, usually takes about 6 to 12 weeks after that. Timing depends on the number of sources, data quality and how much history you bring across. We build in phases, so your first reports run on BigQuery early and more sources are added over time.

  • With on-demand billing, BigQuery charges by the amount of data your queries scan, so design matters. We partition and cluster large tables so queries read only what they need, avoid full table scans in routine reports, and set up quotas and spending alerts. Usage reports show where costs come from by team or workload, so you can spot expensive queries early and act before they add up.

  • Yes. Power BI, Looker, Looker Studio and many other BI tools connect to BigQuery directly. We design the data model with reporting in mind, so dashboards stay fast and every report uses the same definitions. Analysts can also query the warehouse from Jupyter Notebooks. One BigQuery warehouse can serve whichever reporting tools your team already uses, without separate copies of the data.

  • Yes. We move on-premises databases and older warehouses to BigQuery, keeping history and reconnecting your reports. Where it fits we use Google's own tools, such as the BigQuery Data Transfer Service, and build custom pipelines for the rest. Test loads and reconciliation against the source let you confirm the numbers before switching over, and the old system can stay available until your team is confident.

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