Guest post co-written by Adrian Brudaru from dltHub.
In the modern data stack, the proliferation of tools has led to a fragmented ecosystem where interoperability is more aspiration than reality. This challenge is aptly described in the Dagster blog post on impedance mismatch in data orchestration, which highlights how disparate tools struggle to communicate effectively, leading to inefficiencies and increased complexity.
dlt (Data Load Tool) emerges as an open-source data ingestion standard aiming to bridge these gaps. What sets a standard like dlt apart isn't just its functionality in isolation but its ability to interplay seamlessly with other tools.
This, combined with SQLMesh (our open-source data transformation tool), means you can begin communicating important metadata information across the data pipeline. Making the lives of data practitioners easier and the pipelines more transparent.
The Modern Data Stack doesn't talk to itself
The current landscape of the modern data stack is like a tower of Babel. Each tool operates in its own silo, and while they might read and write to the same databases, they don't share metadata effectively.
Sharing metadata between tools is the future
Most tools interact only through the database layer, treating it as a universal translator. However, this approach is limited because it doesn't capture the rich metadata that could enable more intelligent data processing and integration. Without end-to-end metadata flow, the promise of a cohesive data pipeline remains unfulfilled.
As a result, data engineers repeatedly redefine schemas and semantics across different tools, leading to inefficiencies and increased potential for errors.
A Better Way
Imagine a world where tools within the modern data stack work as one, sharing metadata and orchestrating complex data transformations without manual intervention.
Open-source standards like dlt and SQLMesh make this possible because they are inherently designed to be interoperable and hackable. Why put out a fire with a cup when you could use a hose?
Your pipeline should be more like a hose, passing data and metadata from start to finish without impedance.
By adhering to open standards, tools can expose their internal metadata in a way that other tools can consume and act upon. This fosters an ecosystem where data ingestion, transformation, and analysis tools work in harmony, reducing the burden on data engineers to maintain glue code and manual integrations.
SQLMesh + dlt: Towards an Integrated Data Stack
SQLMesh is a powerful open-source framework that simplifies SQL-based data transformation, making it easier for data platform engineers to build, version, and manage complex data workflows. With features like version control, data lineage tracking, and easy testing, SQLMesh empowers engineers to develop with confidence, speed up iteration, and maintain data quality—all critical for managing and scaling modern data platforms. This makes SQLMesh a favorite among data platform engineers who are looking for a reliable and efficient way to handle transformations without getting bogged down in operational complexity.
The metadata handover in action
The recent pull request on GitHub showcases an integration where SQLMesh can generate project scaffolding by inspecting a dlt pipeline's schema and configuration.
Faster scaffolding
By reading the metadata provided by dlt, SQLMesh can automatically generate the necessary models and configurations required for data transformation tasks. This automation not only accelerates the setup process but also reduces the likelihood of human error. Data engineers no longer need to manually define schemas or write boilerplate code to integrate data from dlt into SQLMesh.
Incremental processing support
One of the key advantages of this integration is the ability to perform incremental data processing and loading. SQLMesh leverages the metadata about data changes captured by dlt to process only new or updated records. This incremental approach is more efficient and scalable, especially with large datasets, as it avoids redundant processing of unchanged data.
Benefits of SQLMesh
Once you ingest data from the dlt pipeline into your SQLMesh project, you can leverage several key benefits:
- Semantic Understanding: Detects SQL issues at compile time, offers column lineage and multi-engine execution.
- Smart Change Categorization: Automatically identifies changes as “breaking” or “non-breaking” to optimize backfill processes.
- Automatic DAG Generation: Generates dependency graphs by parsing SQL or Python scripts.
- Virtual Data Environments: Utilizes views for easy rollbacks/roll-forwards; includes validation tools like unit tests, audits, and data diff.
Why Use SQLMesh and dlt Together
Using SQLMesh and dlt together brings clear advantages for data engineers. Automation of transformation models and configurations boosts efficiency, letting engineers concentrate on complex tasks instead of repetitive coding. Incremental processing ensures pipelines handle growing data volumes smoothly without performance drops.
Data platform engineers prefer SQLMesh and dlt because they uphold true open-source principles, integrate effortlessly into existing systems, leverage metadata-driven approaches to enhance data quality and consistency, benefit from active community support, and offer the flexibility and control needed to manage data infrastructure effectively.
Try It Yourself
For a deeper dive into how SQLMesh integrates with dlt, refer to the pull request on GitHub. To experiment with the generator and see how it can streamline your data engineering workflows, check out the SQLMesh documentation. If you have questions or need assistance, consider joining the SQLMesh Slack community for support from fellow data engineers. If you were interested in learning more about dltHub, checkout their slack community here or their docs.