Fivetran or dlt: when each fits your data team
Choose Fivetran when your team wants maintained, managed connectors and has little appetite for writing and operating pipeline code. Choose dlt when your engineers prefer pipelines as Python, want them versioned and reviewed like any other service, and can run them inside tools they already operate. This choice usually surfaces when a MAR-based bill grows faster than the data it moves, or when a team that already writes Python loaders asks why it still pays for connectors it could write in an afternoon. dlt is a library, not a platform, so the decision is about who owns the code path, not about a product you must buy. Fivetran fits teams where a connector failure should be someone else's ticket. Its catalog of managed connectors, schema handling and vendor-side maintenance remove work that dlt leaves with you. dlt fits teams that already run Airflow, AWS Lambda functions or notebooks and want the extraction logic, schema inference and incremental loading in their own repository. The library installs into existing code, and its README names environments such as an AWS Lambda function, an Airflow DAG, notebooks or a local machine. Confirm that your orchestrator and runtime limits, such as function timeouts for large backfills, match the volumes you move.
The decision turns on three checks. First, list your sources and confirm that each has a maintained Fivetran connector or a dlt source, including REST APIs with custom authentication, which you would write and test yourself. Second, decide who fixes a broken connector at two in the morning, because with dlt that person is on your team. Third, measure how much engineering time you can give to pipelines each sprint. A small set of stable database and API sources is a good fit for dlt. A long tail of SaaS sources with frequent API changes often is not, unless you accept ongoing maintenance as a recurring cost.
Which scenarios favor dlt over Fivetran
This table compares common team situations and names the option that tends to fit each one better, dlt or Fivetran. Read each row as a conditional: the advantage holds only under the condition listed in the last column, so check that condition against your own sources, staffing and residency rules before deciding.
| Scenario | Better fit and reason | Condition the advantage depends on |
|---|---|---|
| Strong Python team already running Airflow | dlt, because pipelines live as code next to existing DAGs and need no separate sync platform to operate | Engineers are willing to own connector code and tests, and Airflow already has capacity for extra scheduled tasks |
| Mostly REST APIs and file sources | dlt, because custom REST and file sources are written in Python and loaded with built-in schema inference and incremental loading | The APIs are documented and stable enough that someone on the team can maintain each source over time |
| Data residency rule keeping extraction in your own cloud account | dlt, because it runs wherever Python runs, including inside your own account, so extraction never needs a vendor-hosted worker | Your compliance team accepts a self-run library and you can confirm where logs and credentials are stored |
| Few sources and no dedicated pipeline engineers | Fivetran, because managed connectors and vendor-run maintenance reduce the number of people who must watch pipelines | Your sources are covered by Fivetran's catalog and the MAR volume stays predictable for your budget |
| Many SaaS connectors the team does not want to maintain | Fivetran, because its catalog and managed maintenance absorb API changes that would otherwise fall to your engineers | Each SaaS source you need exists in Fivetran's catalog and you accept its pricing model for those connections |
| Need for a broad, vendor-supported connector catalog | Fivetran, because its maintained connectors cover more sources out of the box than a library of hand-written sources | Vendor support response and connector coverage are higher priorities than code ownership |
How dlt loads data without a backend
dlt is a library, not a platform, so there is no server to install and no control plane to run. It is an open source Python library released under Apache 2.0, and it installs into the code you already have. The dlt README names environments such as an AWS Lambda function, an Airflow DAG, a notebook or a local machine as places it runs. That means the team decides where a pipeline executes, how it is scheduled and where its logs go. The version to pin matters for reproducibility: dlt 1.31.0 was released on October 7, 2026, according to its PyPI project page. Check the release notes for any version you intend to adopt before you pin it in a requirements file.
Sources cover REST APIs, SQL databases, and files in local or cloud storage on S3, GCS and Azure, plus pandas, Polars and Arrow DataFrames. The PyPI page lists more than twenty destinations, including DuckDB, Snowflake, BigQuery, Postgres, Databricks and filesystem storage on S3, GCS and Azure, along with custom destinations. Because the destination list changes between releases, confirm the exact sources and destinations you need against the current dlt documentation before you commit to a design. Pay particular attention to any Fivetran connector whose destination you plan to load into and to whether the destination you use is listed as supported in your target dlt version.
Because no vendor holds the state, the team owns scheduling, pipeline state and credentials. Pipeline state, including incremental cursors, lives where your code and destination can reach it, so decide early where it is stored and how it is backed up. Credentials for source APIs and warehouses move into your own secret store rather than a vendor connection screen. Each of these is a recurring operational responsibility: someone must rotate keys, watch failed runs and handle a source API change. Ask your team to name the owner for each of these before you migrate a single connection.
Fivetran and dlt side by side on capabilities
This table sets Fivetran's managed ELT model against the Apache 2.0 dlt Python library across licensing, connectors, loading behavior and operations. Read the dlt column as what you build and run yourself, and the Fivetran column as what you rent, then confirm the Fivetran items with the vendor before deciding.
| Capability | Fivetran | dlt |
|---|---|---|
| Licensing | Commercial SaaS subscription; no open-source license for the hosted service | Apache 2.0 open-source Python library |
| Delivery model | Managed cloud service that Fivetran operates, with Hybrid Deployment options on certain plans | Library installed into your own Python code, with no backend required to run |
| Connector source | Catalog of managed connectors maintained by Fivetran; confirm current coverage for your sources | REST APIs, SQL databases and files in local or cloud storage (S3, GCS, Azure), plus pandas, Polars and Arrow DataFrames |
| Schema inference | Managed by the service; confirm how it handles source schema changes for your connectors | Built-in schema inference and nested data normalization |
| Incremental loading | Managed by the service per connector; confirm the method used for each source | Built-in incremental loading that you configure in the pipeline code |
| Merge strategies | Confirm which load modes each destination and connector supports | Built-in merge strategies declared in code |
| Schema contracts | Confirm what contract or change-control options your plan includes | Schema contracts declared through Python decorators, versioned with your code |
| Destinations | Confirm the destinations available on your plan and region | More than twenty listed, including DuckDB, Snowflake, BigQuery, Postgres, Databricks and filesystem storage, plus custom destinations |
| Scheduling | Scheduled by Fivetran's service; confirm sync frequency options for your plan | Runs wherever you schedule Python, such as an Airflow DAG, AWS Lambda or a cron job you operate |
| Monitoring and alerting | Confirm dashboards, logs and alert channels with Fivetran for your plan | Not provided by the library itself; you build logging and alerts with your existing tooling |
| Where the pipeline runs | Fivetran's cloud, or your infrastructure under Hybrid Deployment where your plan allows it | Anywhere Python runs, including your own cloud accounts, data centers or laptops |
| Optional dlt[hub] extra | Not applicable | Feature to verify for your needs: the README associates it with data quality, transformations and AI; confirm scope and license terms before adopting |
How dlt handles schema inference, incremental loads and contracts
dlt covers much of the schema and load logic that Fivetran manages for you, but it does so through code your team writes and owns. The four behaviors below are where that ownership shows up, and each one needs checks against your own source data before you rely on it.
Schema inference and nested normalization
You write a resource that yields Python dictionaries or DataFrames, and dlt infers table and column types, then flattens nested objects and lists into child tables. Fivetran performs the equivalent step on its side of the connector. With dlt, the inferred schema is visible in your repository and the pipeline state, so you can review what it produced. Check the flattened output against a sample of real API responses, especially fields that are sometimes null or change type between records.
Incremental loading and merge strategies
You declare a cursor field or primary key on the resource, and dlt tracks the last loaded state so later runs fetch only new or changed rows. Choose the write disposition deliberately: append suits large append-only event tables, while merge suits mutable entities such as customers or orders. Test late-arriving updates and deletes, because a cursor that moves forward will miss records that change without updating the cursor field.
Schema contracts declared in Python
You attach a contract to a resource to state whether new tables, new columns or changed data types are allowed, warned about or rejected. This matters most when an upstream API changes without notice and downstream dbt models depend on fixed column names. Set the policy per entity, not once for the whole pipeline. Assign a named owner who receives alerts when a contract is violated, because a rejected load that no one sees is a silent outage.
Schema evolution over time
When a source adds columns or nested fields, dlt alters destination tables to match the new schema on the next run, without a manual migration step. This reduces routine maintenance when a SaaS vendor ships frequent API changes. It does not decide whether a change is safe for your warehouse consumers, so pair it with the contract policy above. Confirm the behavior for each destination you use, since DDL support and type mapping differ between warehouses and filesystem targets.
Where the data and credentials sit with dlt
With dlt, the extraction code and its runtime run inside your environment, so the location of your data is set by where your orchestrator, workers and destination run. The cards below cover the decisions that follow from that: where data and credentials sit, how traffic reaches sources, who can reach destinations, and what evidence your team must produce.
Data location follows the runtime
Because dlt is a library rather than a hosted service, records pass through whatever machine runs the pipeline, such as an AWS Lambda function, an Airflow worker or a laptop, before they reach the destination. Confirm which regions and accounts those components use, including temporary staging files written to S3, GCS or Azure during loads. Your residency position is only as strong as the weakest component in that path.
Credential storage and rotation
dlt reads source and destination credentials from its configuration and secrets files or from environment variables, so the storage choice is yours to make. Use a dedicated secrets manager with access logging, keep credentials out of Git and rotate them on a schedule your team documents. Check how your orchestrator injects secrets into task logs, since a failed run can expose them there.
Network paths to sources
Each pipeline opens outbound connections to REST APIs, SQL databases and file storage, and Fivetran's managed connectors use network paths that Fivetran operates. With dlt, you decide the egress route, whether through a NAT gateway, a private link or a bastion, and whether databases accept connections only from the worker subnet. Document these paths, because auditors will ask who can reach each source system.
Identity and access to destinations
dlt writes to destinations such as Snowflake, BigQuery, Postgres or Databricks using the credentials you give it. Create a dedicated service identity per pipeline with write access only to its target schemas, and keep separate identities for loading and for analysts reading the output. Confirm whether your warehouse supports short-lived tokens or workload identity so long-lived passwords are not needed.
Audit logging across the pipeline
Run history from dlt is only as complete as the logs you keep. Decide where load traces, orchestrator run records, warehouse query history and cloud audit logs are stored, how long they are retained and who can alter them. Without this, you cannot show which pipeline loaded which records, or when a schema change happened, after a Fivetran connection has been removed.
Controls, reviews and evidence
Access reviews, change approvals and control descriptions for this stack must be designed and documented by your team. DeployOpen can help plan and prepare these controls and the evidence that supports them, but it does not issue audit reports, attestations or certifications, and independent auditors make that determination. Separately, check each source's own terms for how its data may be stored, processed and retained.
What a move from Fivetran to dlt involves
The sequence turns each Fivetran connection into a dlt pipeline you can version, schedule and reconcile against the data your downstream models already read. Each step closes with a check, so you switch off Fivetran only after the new loads match.
Inventory connections and downstream dependencies
List every Fivetran connection, its source, its destination schema and its sync frequency, then map which dbt models, dashboards and reverse-ETL jobs read each table. Data engineering owns this step, with analytics engineers confirming the consumers. It is done when every table has a named owner and a list of dependent models. Ask your Fivetran administrator for read access to the connection list before you start, since the inventory is the basis for the rest of the plan.
Export the configuration as a migration plan
Pull connection, destination and schema settings through Fivetran's REST API or Terraform provider, then strip credentials and tenant identifiers before committing the snapshot to version control. The export becomes the reference for rebuilding each pipeline. It is done when the snapshot is reviewed and no secrets are present in the repository.
Match table and column names before coding
Decide the target schema, table names and column names for every source, and make dlt's destination naming match what Fivetran wrote. Downstream dbt models depend on these names, so a renamed column breaks a model even when the data is correct. Analytics engineers and data engineers agree the mapping together. It is done when a written naming map covers every table your models reference.
Choose a dlt source and configure incremental loading
For each connection, select a dlt source, such as a REST API, SQL database or file source, or write a custom resource where none exists. Configure incremental cursors, primary keys, merge strategy and schema contracts to reproduce how Fivetran handled deletes, updates and new columns. Data engineers own this work and test it against a sandbox destination. It is done when a test run produces the expected state and schema on a sample of source data.
Schedule runs in your existing orchestrator
Deploy each dlt pipeline into the scheduler you already operate, such as Airflow, a serverless function or a CI job, with secrets held in your existing secrets store. Set retries, alerting on failure and concurrency limits so loads do not collide with transformations. It is done when a scheduled run completes end to end and a failure triggers the alert path you defined.
Reconcile row counts and key fields
Compare row counts, primary key uniqueness, null rates and sampled values between dlt output and Fivetran output for the same tables, and document any difference with its cause. Expect some differences from timing and from how each tool handles deletes. The check is complete when every table either matches or has a signed-off explanation for each variance.
Run both pipelines in parallel for an agreed window
Keep Fivetran syncing while dlt writes to a separate schema, and run the dbt models against both to confirm dashboards and downstream outputs agree. Agree the length of the window and the sign-off criteria with the data owners before starting, not during. It is done when the agreed window closes with the reconciliation results accepted in writing.
Switch off Fivetran after sign-off
Repoint dbt sources and dashboards to the dlt-loaded schemas, pause the Fivetran connections, and then delete them once the rollback plan is no longer needed. Confirm the billing change with your Fivetran account team and record the final state in the runbook. It is done when no production model reads from Fivetran-managed schemas and the runbook names the owner of each dlt pipeline.
Questions teams ask before choosing dlt over Fivetran
How much engineering time does a dlt pipeline take to build and maintain?
The build effort depends mostly on how many sources you load, how complex their APIs are, and how much of the pipeline your team already has in place. dlt handles schema inference, nested data normalization, incremental loading and schema evolution in the library, so the engineering work shifts toward writing and reviewing source code, tests and orchestration. Maintenance grows with the number of sources, because every upstream API change, pagination quirk or authentication update lands on your team. Ask any implementation partner to break the written scope into per-source work, including testing and handover, so you can see where the hours go before you commit.
Can a team without Python engineers run dlt?
Not comfortably. dlt is a Python library that installs into existing code and runs in places such as an AWS Lambda function, an Airflow DAG, notebooks or a local machine, so someone must write, test and deploy Python pipelines. Analysts who are strong in SQL can contribute to destination modeling, but source code, credentials handling and failure diagnosis need Python skills. If your team lacks that capacity, plan for either hiring, training or a fixed-scope engagement that delivers the pipelines and documents how to run them. Fivetran remains the option that asks less of your team in code.
How long does a migration from Fivetran to dlt take?
The duration depends on the number of connections, how many are standard REST or database sources, the volume of historical data you must backfill, and how many downstream dbt models or dashboards depend on the exact table and column names Fivetran produces. Reconciling row counts between the old and new pipelines, planning initial loads and running both in parallel before cutover are the steps that most often extend the schedule. Ask for a written plan that lists each connection, its load strategy, the reconciliation method and the cutover criteria before agreeing on a timeline.
What skills and on-call coverage does running dlt pipelines require?
You need Python for the pipeline code, SQL and data modeling for the destination, and enough knowledge of your orchestrator to schedule, retry and alert on runs. Operationally, failed loads, rate limits and upstream schema changes need a named owner, a runbook and an alerting path. Because dlt runs wherever Python runs, the hosting, secrets management, logging and monitoring are your responsibility. Ask the partner or your own team to document who receives alerts, how credentials rotate and how a failed backfill is restarted without duplicating rows.
Is a consultant or implementation partner worth hiring for a dlt move?
A partner is worth considering when your team lacks spare Python capacity, when the migration covers many connections, or when compliance evidence for the new pipelines is required. DeployOpen plans, implements, integrates, hardens, documents and supports self-hosted open-source platforms in your own infrastructure. Most engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts, and ongoing support is available as a retainer. DeployOpen does not publish a price list, so a quote follows from the written scope. Ask for the list of connections, the handover documentation and the testing approach to be included.
Can dlt replace a Fivetran connector that has no REST API or database source?
It depends on what the source exposes. dlt's documented sources include REST APIs, SQL databases, files in local or cloud storage such as S3, GCS and Azure, and pandas, Polars and Arrow DataFrames. If your Fivetran connector reads from an application that offers only a proprietary protocol, a webhook, a vendor SDK or a bulk export, you will likely write a custom source in Python, which adds build and test effort. Confirm the exact source access method for each connector before assuming a like-for-like replacement, and check whether an existing dlt source or a community source already covers it.
When is Fivetran still the better choice over dlt?
Fivetran is usually the better fit when you want managed connectors maintained by the vendor, when the team has no capacity to fix broken pipelines, or when the source list is long and changes often. It also makes sense when your priority is fast time to first data over control of the code. Weigh that against the recurring Fivetran charges and any residency or lock-in concerns that drove the search. Write down the connectors, their change frequency and the people who would own fixes, then compare that list with the dlt build and maintenance plan before deciding.
How DeployOpen helps evaluate and implement dlt pipelines
DeployOpen is a Chainscore Labs company that plans, implements, integrates, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For Fivetran vs dlt, we start with a written scope that maps your current connectors to dlt sources and destinations, then build and test the pipelines in your environment.
How we scope
- Inventory every Fivetran connection, its destination, sync schedule and downstream consumers before any build work starts.
- Confirm which sources have supported dlt sources and which would need custom Python code or a REST API source.
- Agree the destinations, orchestrator, secrets handling and data residency constraints in a written, fixed-scope document.
- Define what counts as done, including row-count reconciliation and schema contract expectations for each table.
How we build
- Implement dlt pipelines with incremental loading, merge strategies and schema contracts declared in version-controlled Python code.
- Run pipelines inside your existing Airflow DAGs or serverless functions, so scheduling follows your current operating model.
- Test each pipeline against the Fivetran output in parallel, comparing row counts, keys and column types before any cutover.
- Harden credentials, logging and access controls to match your internal security baseline and documented standards.
How we hand over
- Deliver the pipeline repository, runbooks for reruns and backfills, and a record of the reconciliation results for each table.
- Document how to add a new source, change a schema contract and respond when an upstream API changes its response shape.
- Walk your data engineers through operating the pipelines, so the team owns connector maintenance from the handover date.
- Provide an inventory of what was retired from Fivetran, so finance and procurement can confirm the change against the contract.
How we support
- Ongoing support, upgrades to new dlt releases and continuous testing are available as retainers, agreed in writing.
- Upgrades are tested against your pipelines and reconciliation checks before they reach production.
- Ask any provider which connector fixes and dlt version upgrades are included in the retainer and which are billed separately.
