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dlt
TransformationEvery data platform has sources that managed connectors do not reach. dlt fills those gaps without building from scratch.
We use dlt to build reliable, maintainable ingestion pipelines for the APIs, databases, and file formats that Fivetran and Estuary cannot cover.
dltManaged ELT tools like Fivetran and Estuary cover the most common data sources. But every organization has data that lives in places those tools do not reach: internal APIs with custom authentication, legacy databases with proprietary schemas, partner data feeds in non-standard formats, or SaaS applications without pre-built connectors. Historically, filling those gaps meant writing custom Python scripts from scratch, managing state, handling retries, and dealing with schema changes manually.
Python-native
Pipeline development language
50+
Custom connectors built across clients
Type-safe
Schema evolution with automatic handling
How We Use dlt
Proven approaches from real client engagements.
01What dlt Gives You That Raw Python Does Not
dlt is a Python library that provides the unglamorous but critical scaffolding every ingestion pipeline needs. Schema inference, automatic type handling, incremental state tracking, error handling with retries, and destination-agnostic loading. You write the code specific to your data source (the API calls, the authentication, the parsing), and dlt handles everything between extraction and landing the data in your warehouse.
This distinction matters because custom pipelines fail in predictable ways. The API pagination breaks. The schema changes upstream. The state file gets corrupted. A network timeout happens at 3 AM. dlt handles these failure modes so you do not have to re-learn them for every new source. We have built 50+ custom connectors with dlt across clients, and the reliability of dlt-based pipelines is comparable to what you get from managed tools.
02Where dlt Fits in the Stack
We deploy dlt alongside managed tools, not instead of them. A typical data platform might use Estuary for Shopify and Postgres CDC, Fivetran for Salesforce and HubSpot, and dlt for the client's proprietary inventory system API and a partner's weekly SFTP data dump. The orchestration layer (Dagster or Airflow) treats all three the same.
dlt loads data into any warehouse that dbt supports: Snowflake, BigQuery, MotherDuck, Databricks, or Postgres. The pipelines are warehouse-agnostic, so if you migrate your warehouse, the ingestion layer moves with you.
03When to Use dlt vs. a Managed Tool
Use dlt when the source is niche, when you need custom logic during extraction (filtering, pre-processing, joining data from multiple endpoints), or when managed connectors exist but do not support the specific tables or fields you need. Use a managed tool when the connector is well-maintained, when you prefer to keep Python engineering effort focused elsewhere, or when the time-to-production matters more than per-connector cost.
The decision is per-source, not per-platform. Most of our clients use a mix of managed and custom ingestion. The goal is complete data coverage with the least total maintenance burden.
Related Services
CorrDyn services where we use dlt.
Data Engineering
Build reliable, cost-effective data pipelines on AWS, GCP, and Azure. CorrDyn designs and implements data infrastructure that scales.
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Systems Integration
Connect CRMs, ERPs, e-commerce platforms, and legacy systems so data flows automatically. CorrDyn builds integrations that run reliably at scale.
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Managed Data Team
Get a full data team without the hiring timeline. CorrDyn embeds the specific skill sets you need and owns outcomes, not just hours.
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Frequently Asked
Questions
What is dlt and when should I use it?
dlt (data load tool) is a Python library for building data pipelines. Use it when you need to ingest data from APIs, databases, or file systems that managed ELT tools like Fivetran do not support, or when you need custom transformation logic during ingestion. dlt handles schema inference, type conversion, and incremental loading so you do not have to build that logic from scratch.
How does dlt compare to writing custom Python scripts?
dlt provides the boilerplate that every custom pipeline needs: connection handling, schema evolution, incremental state management, error handling, and retry logic. You write the source-specific extraction code, and dlt handles the rest. The result is custom pipelines that are as reliable as managed tools.
Can dlt replace Fivetran?
For specific connectors, yes. dlt is not a managed service, so you need engineering capacity to build and maintain the pipelines. For well-supported sources, Fivetran or Estuary may be more efficient. For niche APIs, internal databases, or custom data formats, dlt is often the better choice.
Need help with dlt?
Whether you need a new deployment, an optimization audit, or a migration plan, we will start with what you have and tell you what makes sense.
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