Data platform
BigQuery
BigQuery is a data warehouse that Google runs for you. You load your data into it and ask questions in a familiar query language, without managing servers. We design the warehouse, load it, keep its cost in check, and connect it to the reports people use.
- 1,200+ clients helped
- Building since 2010
- 15 services, one team
- 50+ technologies
At a glance
What it is
BigQuery is Google Cloud's data warehouse: a place to gather large amounts of business data and ask questions of it quickly.
Problems it solves
Data sits in many systems, reports are built by hand, and questions that span several sources take days to answer.
Who it suits
Teams with growing data in several systems who want one trusted place to analyze it, and organizations already on Google Cloud.
How to choose
Choose BigQuery when you already use Google tools or have large, growing data and want little infrastructure to run. Compare it with Snowflake and a plain database first.
What it means
A warehouse you do not have to build
An ordinary business database is built to run the business: take an order, update a record. A data warehouse is built to examine the business: how did sales by region change over three years. The two jobs strain each other, so serious reporting usually moves to a warehouse.
BigQuery is a warehouse where Google handles the machines. There is no server to size, patch, or restart. You store data, run queries, and pay for what you use. That removes a great deal of set-up and is its main attraction.
Options
BigQuery compared with the alternatives
Suits
Large or fast-growing data, teams on Google Cloud, and those who want little infrastructure to manage.
- Trade-off
- Costs follow how much data each query scans, so design matters.
Suits
Organizations that want a cloud-neutral warehouse with strong data sharing.
- Trade-off
- A separate platform and bill; the skills do not transfer automatically.
Suits
Smaller data, where one well-tuned database can serve reports too.
- Trade-off
- Reporting load can slow the system that runs the business.
Suits
Early stages, with a few sources and one or two people.
- Trade-off
- Breaks down as sources, people, and versions multiply.
Worth knowing
Where BigQuery costs surprise people
Cost follows how much data each query reads, not how long it runs. That makes a few habits matter more than any setting.
- Asking for every column when two would do
- Querying a very large table without a date filter
- A dashboard that refreshes a heavy query every few minutes
- Repeating the same expensive calculation instead of saving its result
What it looks like
What the warehouse delivers to people
A warehouse matters because of what people see from it.
How we set it up
Building a warehouse people trust
Start from the questions
We list the ten questions the business most wants answered and the data each needs.
What it produces
- Question list
- Source list
Design the tables
Raw data is kept as it arrived. Clean, named tables sit on top, with one definition for each measure.
What it produces
- Data model
- Measure definitions
Load and check
Automated loads bring data in on a schedule, with checks that flag missing or odd values.
What it produces
- Load jobs
- Quality checks
Control cost and access
Partitioning, limits, and budgets keep spending predictable. Access follows roles.
What it produces
- Cost guardrails
- Access rules
What DECHY can do
Working with BigQuery
Design
- A warehouse model built around your questions.
- A single definition for each number leaders rely on.
Load
- Pipelines from your applications, spreadsheets, and web services.
- Checks that catch bad data before it reaches a report.
Deliver
- Dashboards and scheduled reports on top of the warehouse.
- Alerts when a number moves outside its range.
Add intelligence
- Forecasts and models using data already in the warehouse.
- A route into AI features that use your own data.
Common questions
Questions about BigQuery
Do we need BigQuery, or will our database do?
If your data is modest and one database already serves reports quickly, you may not need a warehouse yet. When reports slow the live system or you need data from many sources together, a warehouse earns its place.
How do we keep the bill predictable?
By partitioning tables, limiting what each query scans, and setting budgets with alerts. We also review the heaviest queries regularly.
Can we use BigQuery if we are not on Google Cloud?
Yes. Data can be loaded from systems anywhere. Teams already on other clouds should still compare it with the warehouse options on their own platform.
Can you connect our existing dashboards?
Usually, yes. Most reporting tools can read from BigQuery. We check yours during the first review.
Have a different question?
Tell us what you are weighing up and we will answer it plainly.
Ask a questionAlready a client? Email support@dechytech.com
Technical depth
How we build with BigQuery
For your developers and IT lead: our BigQuery choices on storage and structure, cost control, access and governance, and loading, and the reasoning behind each.
- Tables are partitioned by date and clustered by common filters, so queries read less data.
- Raw, cleaned, and reporting layers are kept in separate datasets with clear ownership.
- Transformations are written as version-controlled SQL and run on a schedule.
- Queries select only the columns they need and filter on the partition.
- Budgets and alerts warn before spending drifts.
- We choose between pay-per-query and reserved capacity once usage is known.
Access is granted by role through Google Cloud identity. Sensitive columns can be hidden from people who do not need them.
Batch loads cover most needs. Where freshness matters, streaming inserts or change capture from the source database keep the warehouse close to current.
Tell us which questions you cannot answer today
Describe where your data lives and what you want to know. We will outline a warehouse, how it would be loaded, and what it should cost to run.