Your modern data stack, engineered end to end.

Transforming Our Data Workflows

What makes dbt so special?

Glad you asked. dbt lets us apply software engineering best practices to your data pipeline. In plain English, that means we treat your analytics code with the same love and attention as application code. Some of dbt’s standout features that we lean on every day include:

• ▸ Modular SQL modeling: We build transformations as bite-sized, reusable SQL models that depend on each other. This makes complex data problems manageable (and far less scary) by breaking them into Lego-like pieces.
• ▸ Built-in testing: We actually test our data models (novel, right?). dbt’s testing framework catches issues early. Better data quality = fewer “oh sh*t” moments.
• ▸ Version control (Git-friendly): All our dbt projects live in Git repos, complete with code review and version history.
• ▸ Auto documentation & lineage: dbt auto-generates documentation for our models and creates a lineage graph of how data flows from source to final metrics.

Reliable, Scalable, and Maintainable Data Transformations

What this means in practice

In practice, dbt helps us ensure that your data is always accurate, consistent, and ready to make an impact. By using dbt, we create a reliable foundation for your data transformations, meaning your team can trust the numbers and move faster with better insights. It unifies data across teams, from CRM and marketing to product, so that everyone is speaking the same language with shared metrics. This eliminates confusion and avoids the chaos of dueling spreadsheets or conflicting reports. Instead of relying on tangled SQL scripts or Airflow DAGs, we use dbt for its modern, scalable, and maintainable approach to building data pipelines. It ensures that your data transformations are done right, every time. In practice, this leads to clean, consistent data and significantly reduces the hassle for everyone involved.

Open-Source Pipelines

ELT pipelines & data engineering

Our team has spent years designing robust ELT workflows to move and transform data reliably. We prefer to leverage dltHub, an open-source Python library, as our implementation backbone for these pipelines. Using dltHub, we can start with battle-tested templates and then tailor them to your needs, rather than reinventing the wheel. dltHub gives us a scalable, cost-effective foundation that we can build upon for any custom requirement, owned in code we can read and version. The result is a data pipeline that you control: optimized for your stack and capable of handling complex or high-volume data with ease.

Flexible, Scalable, and Cost-Effective

Unlocking efficiency with dltHub for custom ELT pipelines

dltHub offers unmatched flexibility for building custom ELT pipelines. With its robust REST API toolkit, we can quickly integrate any API with minimal code. This allows us to create custom sources and connect to virtually any data source, streamlining the process and reducing manual effort. Whether syncing databases from over 100 engines or processing files from cloud storage, dltHub makes it easy to deploy and maintain complex pipelines.

Its declarative configuration allows us to specify the data flow, letting the system handle the heavy lifting. This reduces development time and ensures reliable, transparent pipelines. With features like schema inference, incremental loading, and support for formats like Parquet and Delta tables, dltHub delivers high performance while future-proofing your data architecture. This flexibility and scalability make it our preferred choice for data engineering.

Streamlined, Modern, and Scalable Data Orchestration

Why we choose Prefect

Apache Airflow was a foundational piece of the data ecosystem and is still the right pick for teams already invested in it. Prefect was designed for cloud-native Python workflows from the start, which fits the work we tend to ship: incremental loaders, API-driven syncs, dynamic task graphs. Its modular design means easier setup, less ongoing maintenance, and clean primitives for retries and observability as data grows. The end result is orchestration that’s resilient and scalable without a custom operations stack behind it.

Reliability, Flexibility, and Developer-Friendliness at Scale

The advantages of Prefect for data pipelines

Prefect stands out because it gives us the tools to quickly build and manage data workflows in Python, letting us go from idea to production fast. Its developer-friendly approach enables rapid iteration, allowing us to tweak workflows as new requirements emerge. But speed doesn’t mean sacrificing reliability. Prefect comes with built-in automatic retries, logging, and failure notifications: features that drastically reduce pipeline failures, helping us maintain operational efficiency. Plus, with clear observability and a real-time UI, we can monitor each task in your workflow and catch issues before they become bottlenecks. Whether you’re managing thousands of tasks or scaling up for larger workloads, Prefect ensures that your automation stays robust and flexible over time.

From Setup to Scaling

Why you should trust us with Prefect

At Neon Deer Data Labs, we don’t just deploy Prefect. We build end-to-end data automation solutions around it. Our deep expertise with Prefect ensures that your workflows are reliable, maintainable, and future-proof from day one. Whether you’re a startup needing fast automation or a scaling team looking to evolve, we design systems that grow with your business. Our clients come to us for help with scheduling or automating data tasks, but they stay because we deliver much more: a data infrastructure that works seamlessly, keeps up with rapid growth, and delivers long-term value. Prefect is the backbone of this infrastructure, and we know how to implement it right the first time.