Understanding data transformation platforms

In large and complex data systems, it’s easy to lose track of what is going on. Many teams struggle to find data, understand its purpose, or figure out who changed what. If that sounds like your team, you can benefit from a data transformation platform. In this article, we’ll look at what a data transformation platform is, when it helps data teams, and how to get started building one.

What is a data transformation platform?

A data transformation platform is a system that handles the movement and structure of data within a data warehouse. It loads data from databases into useful tables, transforms them into new tables, and connects the resulting objects to tools that drive data use cases. The connections between tables and transformations are documented on the data platform so that the structure and dependencies of pipelines are accessible and transparent.

Data transformation platforms sit in between data storage and users in an organization. Analytics engineers work on the platform, managing and optimizing queries for tables, cleaning data sets, preparing meaningful data objects, and more. The platform is like a data hosting service that also organizes and automates the transformations that form the backbone of data systems.

Problems with the modern data stack

The modern data stack is a cloud-based architecture that has emerged as a set of solutions to natural developments in data architecture as an organization grows. Small organizations begin with ad hoc data pipelines to manage the limited amounts of data they produce and access. Analysts download raw data into CSVs and develop analytical reports with spreadsheet tools.

A spreadsheet-based data pipeline can work well enough in the beginning. However, as data volume grows, the process's limitations become apparent.

Investigating, cleaning, and transforming datasets is laborious in spreadsheet systems. Directly transferring large datasets is also a drag on network bandwidth. Data becomes fragmented across different local systems, and row limits put a hard cap on scalability.

Such a system is also susceptible to stale and outdated data. All of this downloading and processing is performed manually by an individual. It takes time out of their schedule every month or every week to download, transform, and analyze fresh data. If they get swamped with other duties, this data and the associated reports on which the business relies could lapse into obsolescence.

To get past these limitations, many organizations move their data operations onto the cloud. Cloud storage is centralized, easily scalable, and manages network usage with connection pooling. These benefits have led to an explosion in the popularity of cloud storage. Alongside this shift in storage, tools for on-cloud development emerged to streamline cloud-based data development.

The network of cloud-based data development tools – Looker, Redshift, Fivetran, etc. – that emerged is the “modern data stack.” These analytics products are designed to take advantage of cloud computing and connect with each other via SQL queries. The leverage that the modern data stack provides to data teams has made it dominant among data development architectures.

However, the modern data stack comes with its own host of problems. Data is hosted but not organized or discoverable due to a lack of data governance. Query structures are developed and deployed, but documentation is poor, dependencies are opaque, and updates are chaotic due to a lack of version control. The stack is valuable, but complexity at scale gets in the way.

How does a data transformation platform help?

Data transformation platforms support the modern data stack by organizing and automating data development. The transformations that form the backbone of data systems happen on the platform rather than being attached to individual projects. The tools that utilize the developed transformations connect to the platform, where they process and store meaningful data.

This centralized platform standardizes data operations since everyone works from the same baseline of well-developed datasets. Standardization aligns metric definitions, preventing conflicts between different definitions, usages, and calculations of similarly-named metrics. Governance systems can hook into a single platform, maintaining privacy and security standards across the organization.

Data transformation platforms also manage query pipelines, enforcing access rules and tracking dependencies. Data engineers and analytics engineers can schedule queries so that developed data sets are always kept up-to-date. They can also automate tests for data quality, consistency, and formatting, guaranteeing stability while streamlining development timelines.

This approach yields numerous benefits:

• Having a data transformation platform saves time on repetitive and routine tasks, opening up data teams' schedules for more value-driven projects.
• Having a standardized, documented baseline of transformation and tables improves trust, allowing for effective collaboration and avoiding blame games when problems arise.
• Accessible and prepared datasets allow non-data teams to self-serve answers to their most burning data-driven questions, enabling data teams to focus on building out useful infrastructure rather than struggling under a backlog of data pipeline requests.

How dbt does data transformation

dbt Cloud is a data transformation platform built for cloud systems, designed to enable safe, trusted collaboration. Over 100,000 data engineers use dbt to support their organizations' data development.

For example, the digital customer experience platform HubSpot uses dbt to organize transformation development, build dependency documentation, and improve its troubleshooting process. Media company Condé Nast uses dbt to increase self-service usage by 30%, reduce reliance on data engineers, and improve the efficiency of their data operations.

dbt allows transformation logic to be defined as code, enabling more readable, modular logic. When pipelines are built as code bases, teams can manage documentation and version control through standard systems like GitHub. This enables applying the same rigorous controls to data that software engineering teams have applied for years to software.

dbt Cloud connects dbt pipelines to powerful cloud-based tools like dbt Mesh for project organization, a semantic layer for standardized metrics and data access, and much more.

dbt’s design philosophy allows organizations to treat data as a product, accelerating engineering cycles with support for CI/CD in data development. With dbt’s support, data teams can keep up with the accelerated product cycles of cloud-based software development. Data teams become proactive contributors to projects rather than bottlenecks in update cycles.

Creating a data transformation platform

Data transformation platforms are a natural evolution of the so-called modern data stack, which itself emerged as a natural extension of small-scale data operations. They connect data sources and data products, manage transformation logic, and govern data access while streamlining and organizing development processes. With a data transformation platform, your organization’s data development can proactively contribute to project value.

dbt Cloud is a premier data transformation platform used by hundreds of thousands of developers worldwide. Its cutting-edge features let you treat data as a product, enabling data teams to keep up with modern development cycles.

Check out this whitepaper to learn about dbt in more detail. To experience dbt for yourself, book a demo to see if your organization is ready to take its data stack to the next level.