Data platform
Snowflake
Snowflake is a platform for storing and analyzing large amounts of business data in the cloud. Its signature idea is that many teams can use the same data at once without getting in each other's way. We design, load, govern, and tune Snowflake, and we keep its cost visible.
- 1,200+ clients helped
- Building since 2010
- 15 services, one team
- 50+ technologies
At a glance
What it is
Snowflake is a cloud data platform. It holds a business's data in one place and lets many teams analyze it at the same time without slowing each other down.
Problems it solves
Different teams need the same data for different jobs, reports slow each other, and nobody is sure what the platform costs or who can see what.
Who it suits
Organizations with many data users and sources, on any major cloud, and teams already using Snowflake.
How to choose
Choose Snowflake for many teams sharing governed data across clouds, or for sharing data with partners. Choose a simpler warehouse or a database when your needs are smaller.
What it means
Separating where data lives from who computes on it
In a traditional warehouse, storing data and working on it share the same machines. When finance runs a month-end report and data science runs a large experiment, they compete for the same capacity, and everyone slows down.
Snowflake separates the two. Data is stored once. Each team gets its own compute that starts when needed and stops when idle. Heavy jobs run in their own space, and the bill follows the compute actually used.
That design suits organizations where many groups share data, or where data must be shared with partners without sending copies. It also moves a skill from technology to management: the platform is easy to start, so spending needs rules from the first day.
Options
Snowflake compared with the alternatives
Suits
Many teams sharing governed data, work across more than one cloud, and sharing data with partners.
- Trade-off
- Spending needs active management; a separate platform to learn.
Suits
Organizations on Google Cloud, and very large queries with little infrastructure to run.
- Trade-off
- Tied more closely to Google Cloud, and costs follow what each query scans.
Suits
Modest data volumes where one database can serve both applications and reports.
- Trade-off
- Reporting load can slow the application, and it does not scale out as easily.
Suits
Early stages with a few sources and a few people.
- Trade-off
- Version and trust problems grow with every added person.
What it looks like
A governed dashboard built on shared data
Shared data pays off when many teams read one governed source.
How we set it up
A Snowflake platform you can govern
Map the users and the data
We list who needs which data, how fresh it must be, and what is sensitive.
What it produces
- User and data map
Design the layers
Raw data, cleaned data, and reporting data live in separate areas, each with an owner.
What it produces
- Data layers
- Naming rules
Set access and cost rules
Roles decide who sees which columns. Budgets, limits, and automatic suspension keep compute in check.
What it produces
- Role design
- Cost guardrails
Load, check, and hand over
Automated loads, quality checks, and a runbook so your team can operate it.
What it produces
- Load jobs
- Runbook
What DECHY can do
Working with Snowflake
Design
- A platform layout built around your teams and questions.
- One definition for each key measure.
Load
- Pipelines from your applications, files, and cloud services.
- Quality checks that stop bad data early.
Govern
- Roles, masking of sensitive fields, and audit trails.
- Cost budgets with alerts that someone owns.
Deliver
- Reports and dashboards on top of the platform.
- Data shared with partners without copying it.
Common questions
Questions about Snowflake
Snowflake or BigQuery?
Both are strong. BigQuery suits teams on Google Cloud who want little to manage. Snowflake suits many teams sharing data, work across clouds, and data sharing with partners. We compare them on your facts.
How do we control the cost?
With separate compute for each job, automatic suspension, budgets with alerts, and a regular review of the heaviest queries. Someone should own the number.
Do we need a warehouse at all?
Not always. If one database serves your reports without slowing the business, a warehouse can wait. We tell you honestly when it is premature.
Can you work with the platform we already have?
Yes. We start with a review of design, access, and cost, and then recommend what to keep and what to change.
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 Snowflake
For your developers and IT lead: our Snowflake choices on compute, storage and structure, governance, and pipelines, and the reasoning behind each.
- Separate warehouses for loading, transformation, and reporting, sized to the work and set to suspend when idle.
- Resource monitors cap spending per team and alert before the cap.
- Queries are reviewed for scans that read more than they need.
- Tables are organized to match common filters, and very large ones are clustered where it helps.
- Historical versions allow recovery from mistakes for a defined period.
- Role-based access with least privilege, and masking policies on sensitive columns.
- Access is logged, and sharing with partners is limited to the named data.
Transformations are written as version-controlled SQL, tested, and run on a schedule. Heavier processing can be written in Python close to the data.
Tell us who needs which data
Describe your teams, your sources, and what the current reports cost in time. We will outline a platform and a cost model.