Why you need a data control plane

Despite recent advancements in analytics—AI, cloud, self-service—we’ve still yet to solve core data problems plaguing our teams: data quality, data velocity, and keeping costs in check in the process. One reason is that today’s modern data ecosystem has created data and compute silos that need to be centralized in a data control plane so organizations can build the holistic context needed to confidently embrace data at scale.

dbt Cloud is the data control plane that centralizes your analytics workflow metadata and makes it actionable, so your teams can ship and use trusted data, faster. Here’s how.

The problem: Data doesn’t scale

The cloud and AI have made data more accessible than ever. Organizations are prioritizing ways to translate raw data into trusted insights.

However, as your data usage scales, the stakes get higher to ensure that data is accurate, timely, and well-governed. Without a standardized approach (control plane) to managing data at scale, your organization will face a few self-perpetuating problems:

Data quality issues abound

No visibility/ lineage

You need a way to visualize data dependencies and trace lineage as data moves from source to model to metric. Without this, you’re flying blind. Your teams end up shipping analytics code into production that accrues data analytics debt exponentially. Stakeholders also lack the signals they need to trust that data is fresh and accurate.

No testing or version control

Without a built-in ability to test analytics code, teams push code into production and simply hope for the best. Or perhaps they wait for an angry stakeholder to surface an issue. Without version control, there’s no way to roll production code back to its prior state while a data engineer investigates the issue.

Slow debugging

Finding the root cause of an issue is toilsome without the ability to trace lineage (including column-level).

No documentation

Without a standardized way to document analytics code metadata (freshness, owner, column names, etc.), there’s no continuity as new or other team members try to understand lineage, what code had been built, etc.

No support for mesh architectures

Bringing governance logic and rules to disparate business domains helps improve and enforce data quality at scale. But it’s not enough for your organization to adopt a mesh architecture. The services that sit on top of it need to as well.

Data pipelines get bogged down

Starting from scratch

Undocumented data analytics code offers zero visibility into what’s been built before and how various models interconnect. As a result, many teams end up building new solutions from scratch. They create stored procedures in a database, locally run analytics code, or other unmanaged and ungoverned solutions.

Lack of automation

AI co-pilots have proven effective at accelerating the productivity of knowledge workers. Without native AI copilots, data practitioners must conduct manual, tedious work by hand.

No CI/CD

In software engineering, Continuous Integration and Continuous Deployment (CI/CD) pipelines deploy changes to production through stages that subject them to rigorous automated and manual verification procedures. Sadly, most analytics code still doesn’t follow this approach. Without a safe or automated way to build and merge analytics code into production, deploying changes to models becomes very manual and doesn’t scale.

Limited number of developers

Even if they had SQL skills, not enough data practitioners (analysts, for example) had the deep expertise to work with legacy transformation tools. This led to over-burdened data engineering teams who should have been spending more time on data architecture and platforming.

No way to promote self-serve in a governed, scalable way

Allowing end users to tap into trusted data models and metrics is the holy grail. However, without a way to centralize business logic and without first-class integrations into BI platforms and LLMs, you risk end-users accessing incorrect data. That further sabotages trust in data and data teams.

Costs spiral out of control

Vendor lock-in

It’s more important than ever to avoid lock-in with any one vendor or approach. With tense dynamics across cloud and data platform vendors, most organizations don’t want to be price-gouged.

Inefficient compute

Getting data from its source to its consumer involves many hops between various models and tables. It’s easy to inadvertently and unnecessarily drive compute costs up without the proper context or controls into how these jobs are run.

Optimization requires context

Data estates are large, complex, and dynamic. You need intuitive ways of understanding how efficiently your models are performing and which models are most (and least) utilized so that you can take this information and assign resources to fine-tune and optimize your pipelines.

The solution: A data control plane

The solution to these problems: a data control plane powered by dbt Cloud.

Business runs on data. And data runs on dbt. For years, customers have trusted dbt as their one-stop shop for transforming data into high-quality and accurate data sets. Now, you can leverage dbt Cloud to deliver high-quality data, faster, and at the lowest cost possible.

dbt Cloud is your control plane for data:

• It’s natively interoperable across various cloud and data platforms so you’re never locked-in
• Its platform features support data developers and their stakeholders across various stages of the analytics development lifecycle to make data analytics a team sport
• It provides the trust signals and observability features required to ensure all data outputs are accurate, governed, and trustworthy.

With dbt Cloud as your data control plane, your data teams have a standardized and cost-efficient way to build, test, deploy, and discover analytics code. Meanwhile, data consumers have purpose-built interfaces and integrations to self-serve data that is governed and actionable.

Do more with data

With a data control plane powered by dbt Cloud, your organization can transform how it does data. With dbt Cloud, you can:

Reliably deliver high-quality data to the business

Testing and version control

Improve the integrity of the SQL in each model by making assertions about the model logic (unit tests) or the expected results generated by a model. Embrace version control with deep git integrations to track all code changes across dev, staging, and prod. Work collaboratively, safely, and simultaneously on a single project. Safely roll back to prior states when issues arise.

Spot and fix issues quickly

Monitor your dbt jobs and set up proactive alerts to keep pipelines smooth. Use column-level lineage to trace dependencies and debug issues quickly. Build trust with downstream teams by embedding health status tiles in analytics tools so everyone is aligned on data freshness and quality checks. Use audit logs to understand and troubleshoot user and system events quickly.

Automated lineage and documentation

Auto-generate the documentation when your dbt project runs. dbt provides a mechanism to write, version-control, and share documentation for your dbt models. You can write descriptions (in plain text or markdown) for each model and field and navigate your entire detailed lineage in dbt Explorer.

Governance with guardrails

dbt Cloud supports data mesh architectures with dbt Mesh. Divide projects into data domains to reduce complexity and maintain data governance. Role-based access controls make it easy to configure who has access to what data.

Build, test, and deploy data faster

Reuse, don’t rebuild

Create reusable (modular) data models that can be referenced in future work instead of starting at the raw data with every analysis. This “DRY” (don’t repeat yourself) approach to code makes it maintainable by people other than yourself and scalable as the system load increases. Reuse models across projects to accelerate data delivery without unnecessary complexity.

Automated end-to-end lineage

Get an automated and holistic view of how data moves through your organization—where it comes from, how it’s transformed, and who consumes it. With this visual graph and data catalog, developers can build, troubleshoot, and analyze data workflows more efficiently and accelerate cross-org data literacy.

Standardize and accelerate data development workflows

Use embedded AI-copilot experiences to generate SQL on demand, and auto-generate tests, documentation, and metrics. Lean on custom rules for SQL formatting to standardize and optimize code development and trust those guidelines are natively enforced.

Built-in CI/CD

Run CI jobs to test your code before it’s merged to production to ensure it’s behaving as expected and won’t break anything downstream. Trigger jobs to run when a pull request (PR) is merged and defer to production to optimize compute cycles and velocity. Do all of this from the flow of your development and git environment.

Democratize data development

Get more data-literate stakeholders to participate in data development. With dbt, they don’t need to write boilerplate DML and DDL by managing transactions, dropping tables, and managing schema changes. They can write business logic with just a SQL select statement, or a Python DataFrame, that returns the dataset they need. dbt takes care of materialization.

The new Visual Editor democratizes data development to even less technical users with a visual drag-and-drop interface that’s all powered by version-controlled, governed SQL under the hood.

Semantic layer integrations foster self-serve

With dbt Semantic Layer, data teams can define business logic centrally, alongside their dbt models, and ship them to any endpoint…whether a BI tool, an embedded application, or LLM. This means that the people who rely on data to make decisions have it readily available at their fingertips in self-serve, accessible interfaces and everyone is confident that that data is accurate, governed, and consistent.

Optimize data platform costs free from lock-in

Flexible interoperability between cloud vendors

With rapidly changing market dynamics, it’s more important than ever that teams don’t get locked-in to any one vendor or approach.

There’s no need to hardcode logic at the platform level. dbt Cloud is an abstraction layer that’s interoperable across a variety of cloud data platforms. Use dbt Cloud to:

• Enable cross-department mesh
• Support centralized governance for complex projects running on multiple data platforms
• Dynamically distribute workloads across platforms

dbt Cloud provides unparalleled flexibility fueled by SQL and backed by a passionate open core community.

Inject intelligence into your data builds

Reduce data platform spend by only building models that have changed with defer to production. dbt Cloud supports auto-cancellation for stale CI builds. You can also use unit tests to validate model logic before the model is materialized.

Quickly identify and resolve performance bottlenecks

Pinpoint long-running or often-failing models and quickly identify opportunities to reduce infrastructure costs and save data team time. Discover and improve popular models, and divest from unused models.

Conclusion

dbt Cloud is your control plane for data. But it’s not just for data developers. With its purpose-built user interfaces and native integrations, stakeholders of all technical stripes can participate in the data workflow and have the trusted insights needed to translate data into strategic decisions.

Learn more about how dbt Cloud can deliver data at speed and scale—contact us for a demo today.