dlt Alternatives in 2026: 7 Ingestion Tools Compared

TL;DR: Bruin is the best dlt alternative for teams that want ingestion to live inside a real pipeline: ingestr assets for routine source-to-destination loads, Python assets for custom sources, and SQL transforms, materialization, quality checks, lineage, environments, and data diffs in the same project. dlt is a decent Python library for loading data. The problem is that a Python loading library is often the wrong abstraction for production ingestion. Pick ingestr when the job is a direct source-to-destination sync and you want a fast CLI instead of another Python project. Pick Airbyte when you want a connector platform and UI. Pick Sling when you want a focused data movement CLI. Pick Meltano when you want Singer-based ELT. Pick Fivetran when you want managed ELT and are willing to pay for the abstraction. Stay on dlt and add dltHub, its managed platform, when you like the dlt programming model and only want someone else to run it.

The useful distinction is this:

• dlt is an open-source (Apache 2.0) Python library for building data loading pipelines.
• Bruin CLI is an open-source (Apache 2.0) pipeline framework: assets, dependencies, materialization, quality checks, lineage, environments, validation, data diff, and run orchestration.
• ingestr is Bruin's ingestion engine and standalone CLI for moving data from sources to destinations. It is written in Go and source-available under the Functional Source License.
• Airbyte is a connector platform.
• Sling is a data movement CLI and platform.
• Meltano is an open-source ELT platform built around Singer taps and targets.
• Fivetran is a managed automated data movement platform.

That category difference matters. Bruin is not the "no Python" answer to dlt. Bruin supports custom Python assets too. The difference is that Python is not the whole product. In Bruin, custom Python can be one governed asset in a pipeline, with dependencies, secrets, materialization, checks, and lineage around it. That is a better boundary than "here is a Python script, good luck operating it."

We build Bruin, so you know where we stand. The view here is opinionated, but the comparison is meant to be useful, and we say plainly where another tool is the better pick: dlt for a strange API you want to own in pure Python, and Fivetran or Airbyte when nobody on the team should own connector code.

Key takeaways

• dlt is a library; most of its alternatives are a different category. Bruin is a pipeline framework, ingestr and Sling are CLIs, Airbyte and Fivetran are connector platforms, and Meltano runs Singer taps, so compare the job you have rather than feature lists.
• For routine syncs, a compiled CLI can beat a Python library by a wide margin. In the MongoDB to Postgres benchmark published in the ingestr repo, ingestr v1 finished in 2.86 seconds and dlt in 12.55 seconds.
• Custom Python is not a reason to stay on dlt. A Bruin Python asset returns a DataFrame or yields rows, and Bruin loads the result with one of 4 strategies: create+replace, append, merge, or delete+insert.
• Check the licence before you self-host. dlt and the Bruin CLI are Apache 2.0, Meltano is MIT, and Sling's CLI is GPL-3.0, while Airbyte (ELv2) and ingestr (FSL) are source-available and limit offering the software to others as a hosted or competing service.

How we compared

• What you write: Python code, YAML, a single command, or clicks in a UI.
• What surrounds the load: transformations, quality checks, lineage, environments, and scheduling, or nothing at all.
• How it runs: a library inside your process, a CLI, a self-hosted platform, or a managed service.
• How state works: where incremental progress is stored and how easy a run is to reason about.
• Licence, read from each project's repository.

Facts about each tool come from its public documentation, checked in November 2026. Facts about Bruin come from our docs and pricing page.

What dlt gets right

dlt, short for data load tool, is an open-source Python library, licensed Apache 2.0, for loading data from messy sources into structured datasets. It gives Python interfaces for extraction, loading, inspection, schema evolution, and incremental loading.

That is a real use case. If you have a weird API, custom pagination, strange auth, or business-specific extraction logic, writing Python can be the right answer.

dlt is strongest when:

• You want a Python library, not a platform.
• Your source is custom enough that a connector abstraction gets in the way.
• Your team is happy to own the runtime, deployment, scheduling, secrets, monitoring, and downstream model contracts.
• You want to build the ingestion application yourself.

The part I do not like is when dlt becomes the center of the data stack. A Python loading library should not be the place where you explain ownership, asset quality, column contracts, lineage, environments, data diffs, and operational policy. You can bolt those things on, but then the "lightweight" library starts turning into a homegrown platform. dltHub's managed platform is dltHub's own answer to part of that gap: a managed runtime, triggers, observability, and data quality checks for dlt pipelines.

That is where the alternatives matter.

When to look for a dlt alternative

Look for a dlt alternative when one of these is true:

• You do not want a Python application for every routine sync.
• You want custom Python ingestion, but you also want automatic materialization into a warehouse.
• You want ingestion to sit beside SQL models, Python transforms, quality checks, and lineage.
• You want a CLI and Git workflow, not a pile of deployment glue.
• You want AI agents to inspect, change, run, and validate pipelines using documented commands.
• You want fewer hidden state and destination bookkeeping tables to reason about.
• You need a UI or managed connector platform instead of code.

The important correction: "I need custom Python ingestion" is not a reason to rule out Bruin. Bruin supports Python assets and materializes returned data automatically.

dlt alternatives at a glance

Best dlt alternative by need:

• Ingestion plus custom Python, SQL transforms, quality checks, and lineage in one project: Bruin
• A fast command for routine source-to-destination syncs: ingestr
• A UI-driven connector platform with broad SaaS coverage: Airbyte
• A compact CLI for database, file, and API movement: Sling
• Existing Singer taps and targets: Meltano
• Fully managed connectors with no ingestion code to own: Fivetran
• Staying on dlt with a managed runtime: dltHub

The 7 dlt alternatives, one by one

1. Bruin

What it is: Bruin CLI is an open-source (Apache 2.0) pipeline framework. Projects are built from assets: SQL files, Python files, R files, ingestr assets, seeds, sensors, dashboards, and more. You run them with bruin run, validate them with bruin validate, format them with bruin format, inspect lineage with bruin lineage, and compare outputs across environments with bruin data-diff. Bruin Cloud adds managed orchestration, catalog, lineage, access controls, and an AI data analyst on top.

Where it wins: Bruin CLI is the best dlt alternative when you want to keep code-first data engineering but stop treating ingestion as a loose Python app. The unit of work is an asset in a pipeline, not a script someone remembers to deploy. Routine loads are ingestr assets, custom sources are Python assets with automatic materialization, and the SQL models, quality checks, and lineage sit in the same Git project. See Bruin ingestion.

Where it falls short: it is a framework, so you adopt its project layout and asset conventions instead of importing a library into code you already have. Python materialization is also newer than dlt's: Bruin's docs still flag it as recently introduced, and dlt has more Python building blocks for messy APIs, such as its REST API source.

Best for: teams that want ingestion, custom Python, SQL transforms, quality checks, and lineage in one Git project.

2. ingestr

What it is: Bruin's ingestion CLI. Since v1 it is written in Go, and it is source-available under the Functional Source License (FSL-1.1-ALv2): free for internal use, and each release converts to Apache 2.0 two years after it ships. You pass a source URI and a destination URI, and it copies the data with append, merge, delete+insert, or SCD2 strategies, plus change data capture for Postgres, MySQL, SQL Server, MongoDB, PlanetScale, and Vitess.

Where it wins: ingestr is the answer when the ingestion job is not custom. "Copy MongoDB to Postgres" or "sync Postgres to BigQuery" becomes one command or one declarative Bruin asset, not a Python project. In the MongoDB to Postgres benchmark in the ingestr repo it was the fastest of the five runs: 2.86 seconds, against 6.58 for Sling and 12.55 for dlt.

Where it falls short: it does ingestion only; scheduling, transformations, and governance come from the Bruin pipeline around it. Its licence is not an OSI open-source licence, so read FSL's competing-use terms if you plan to offer ingestion to others as a product.

Best for: teams that want a fast command for routine source-to-destination syncs.

3. Airbyte

What it is: a connector platform. Airbyte Core is free to self-host under the Elastic License 2.0, and Airbyte's cloud plans are managed. Syncs are set up in a UI, through an API, or with a Terraform provider, and a no-code Connector Builder covers custom APIs.

Where it wins: Airbyte is the opposite end of the spectrum from dlt: dlt is a Python library, Airbyte is a connector platform. It is the better fit when analysts or analytics engineers need to set up and monitor syncs in a UI without writing code, when you need broad SaaS connector coverage, and when connector management should be a platform concern, not a Python package concern.

Where it falls short: you run Airbyte's infrastructure or buy a plan, which is more platform than a library. When source logic is unusual enough that the connector abstraction gets in the way, dlt gives you code-level extraction control. In the same MongoDB to Postgres benchmark, Airbyte was the slowest of the five runs at 26.09 seconds.

Best for: teams that want a UI-driven connector platform with broad SaaS coverage.

4. Sling

What it is: a data movement CLI written in Go and licensed GPL-3.0, with a web-based Sling Platform for job scheduling and agent-based execution. The CLI supports quick ad hoc runs through flags and repeatable replication through YAML or JSON, across databases, file systems, and APIs.

Where it wins: Sling is closer to ingestr than to dlt. Choose it over dlt when you want a CLI rather than a Python library, your work is mostly database-to-database, file-to-database, or API-to-database movement, and you want a compact movement tool without adopting a broader pipeline framework.

Where it falls short: narrower pipeline scope than Bruin; it moves the data, and the wider pipeline around the sync is yours to build. When you need custom Python extraction logic or library-level control over resource definitions and loading behavior, dlt fits better.

Best for: engineers who want a compact CLI for database, file, and API movement.

5. Meltano

What it is: an open-source (MIT) ELT platform built around Singer taps and targets. The Meltano Singer SDK (Apache 2.0) helps teams build taps and targets that follow the Singer spec, and MeltanoHub lists the available connectors.

Where it wins: choose Meltano over dlt when you want an ELT project structure rather than a Python loading library, when you want to use existing Singer taps and targets, and when your team is comfortable with Singer conventions.

Where it falls short: ecosystem quality. Singer has a lot of surface area, but connector quality varies by connector. If you prefer Python resources directly and do not want taps, targets, and Singer conventions, dlt is the simpler path.

Best for: data teams invested in Singer taps, targets, and open-source ELT projects.

6. Fivetran

What it is: the managed option. Fivetran is a hosted data movement platform with pre-built, fully managed connectors. Its Connector SDK lets you write a custom connector in Python that Fivetran schedules and runs for you.

Where it wins: choose Fivetran over dlt when you want vendor-managed connectors, less internal ownership of extraction logic, and a commercial support path instead of maintaining ingestion code. The Connector SDK covers some custom sources without running your own infrastructure.

Where it falls short: cost and vendor control. Pricing is usage-based, measured in monthly active rows, and connector behavior is defined by the vendor. If you want open-source control or want custom logic to run on your own infrastructure, dlt or Bruin fits better.

Best for: teams that treat ingestion as a buying decision and want managed ELT.

7. dltHub

What it is: dltHub's managed platform for pipelines built with dlt. It adds a managed runtime, cron and event-driven triggers, an observability dashboard, data quality checks, and team collaboration workflows. The Enterprise plan adds role-based access control, audit logs, and tailored support.

Where it wins: you keep your dlt code and stop operating the runtime, scheduling, and monitoring yourself.

Where it falls short: it is still centered on the dlt programming model, so routine syncs remain Python code, and the platform is commercial while dlt itself is Apache 2.0.

Best for: teams already committed to dlt that want managed deployment and operations.

How does custom Python ingestion work in Bruin?

Bruin supports custom Python assets. A Python asset can define a materialize() function and return a pandas dataframe, Polars dataframe, PyArrow table, list of dicts, generator of dicts, or generator of Arrow tables. Bruin then materializes that output into the configured destination.

That is the key difference from dlt:

• dlt gives you a Python loading library.
• Bruin gives you Python as a pipeline asset with materialization, secrets, dependency isolation, quality checks, and lineage.

Bruin uses uv under the hood for Python dependency management. You can use pyproject.toml with uv.lock, or a requirements.txt file. You can also run different Python versions in different isolated assets.

Here is the shape:

"""@bruin
name: raw.my_custom_api
image: python:3.13
connection: bigquery

materialization:
  type: table
  strategy: merge

columns:
  - name: id
    type: integer
    primary_key: true
@bruin"""

import requests

def materialize():
    page = 1
    while True:
        response = requests.get(f"https://api.example.com/items?page={page}")
        rows = response.json()["items"]
        if not rows:
            break
        yield rows
        page += 1

For large API pulls, the generator path is especially important. You do not need to hold the whole dataset in memory. Bruin writes the batches to Arrow and loads them to the destination.

Under the hood, Bruin runs the Python asset, stores returned data as Arrow memory-mapped files, and uses ingestr to load that data into the destination. So Bruin and ingestr are not competing pieces. Bruin is the pipeline layer. ingestr is the ingestion and loading engine.

Is ingestr faster than dlt?

ingestr is the answer when the ingestion job is not custom.

If the task is "copy MongoDB to Postgres", "sync Postgres to BigQuery", "load Google Sheets into Snowflake", or "move this source table incrementally", I do not want to write Python. I want a command or a declarative asset.

Standalone ingestr looks like this:

ingestr ingest \
  --source-uri 'postgresql://...' \
  --source-table 'public.orders' \
  --dest-uri 'bigquery://my-project?credentials_path=/path/to/service-account.json' \
  --dest-table 'analytics.orders'

Inside Bruin, ingestr becomes an asset:

name: raw.transactions
type: ingestr
parameters:
  source_connection: mysql_prod
  source_table: public.transactions
  destination: bigquery
  incremental_strategy: merge
  incremental_key: updated_at

That distinction matters:

• ingestr alone is a fast ingestion CLI.
• ingestr inside Bruin is a pipeline asset with metadata, dependencies, schedules, environments, checks, and lineage around it.
• Bruin Python assets are for custom ingestion logic that still needs automatic materialization and pipeline governance.

In the MongoDB to Postgres benchmark published in the ingestr repo, ingestr v1 completed the run in 2.86 seconds, compared with 6.58 seconds for Sling, 12.55 seconds for dlt, 14.67 seconds for ingestr v0, and 26.09 seconds for Airbyte. Benchmarks are workload-specific, but this is exactly the kind of routine source-to-destination movement where a compiled CLI should beat a Python library or heavier connector platform. The wider set, across more sources, destinations, and row counts, is on the ingestr benchmarks page.

My opinion: for normal ingestion, dlt is too much Python and not enough pipeline. For strange custom ingestion, dlt is reasonable. For production data work, Bruin gives you a better place to put that Python.

Can AI agents run Bruin pipelines instead of dlt code?

This is another place where the abstraction matters.

Bruin MCP exposes Bruin CLI to AI agents. The docs describe it as a way for agents to query data, compare tables, ingest data, and build pipelines using Bruin. That gives an agent a stable set of commands and documentation:

• bruin run to execute assets
• bruin validate to catch definition issues
• bruin lineage to inspect dependencies
• bruin data-diff to compare outputs
• ingestr assets for source-to-destination loading
• Python assets for custom materialized logic

An AI agent can work with a Bruin project because the project has assets, metadata, checks, environments, and commands. With dlt, the agent is mostly editing Python code and hoping the surrounding deployment conventions are discoverable.

That is not a small difference. Agents need structure. A folder of Python ingestion code is flexible, but flexibility is not the same as operability.

Why does dlt's pipeline state matter?

dlt uses state to support incremental loading and resumability. The dlt docs describe incremental loading as requiring tracking which increments have been loaded. The destination can also include internal tables such as _dlt_pipeline_state, which stores pipeline state and checkpoints.

That design is understandable. It is also another operational surface.

Stateful ingestion can become annoying when jobs move between environments, when destination bookkeeping tables confuse downstream users, or when an interrupted run leaves the next run depending on implementation details you did not mean to expose.

Bruin and ingestr are more opinionated. For common syncs, the preference is that the run is understandable from the asset definition: source, destination, strategy, incremental key, interval, columns, and schema. ingestr v1 dropped separate state entirely, which is also what lets one large load be split into parallel runs, for example one run per day (why we rewrote ingestr in Go). For custom Python, Bruin still gives you materialization and pipeline metadata so the output is not just a side effect of a script.

That is the critique of dlt in one sentence: it is flexible, but too much of the production shape still lives outside the library.

Which dlt alternative should you pick?

For a shorter side-by-side of the same tools, see the dlt alternatives comparison.

When Bruin is not the right pick

If your team is productive writing Python sources and only needs ingestion, dlt may already be the right tool, and dltHub can run it for you. If nobody should own connector code, Fivetran or Airbyte is the shorter path. If your policy requires an OSI-approved licence for the ingestion engine itself, note that ingestr is source-available, while dlt, Sling, and Meltano are open source. Bruin earns its place when ingestion is one step in a pipeline you want to own in code, with transforms, quality checks, and lineage around it.

The short version

dlt is not useless. It is a good library for some custom Python ingestion jobs.

But I would not build a data platform around it. It puts too much emphasis on Python loading code and not enough on the things that make pipelines safe to operate: asset boundaries, materialization, checks, lineage, environments, validation, data diff, and agent-readable workflows.

Fivetran is often the right answer when ingestion is a buying decision. dlt is often the right answer when ingestion is an engineering decision. Bruin is for teams that want engineering control without letting every ingestion job turn into a separate little application.

Bruin's bet is different. Use ingestr when ingestion should be a command. Use Bruin Python assets when ingestion truly needs custom Python. In both cases, put the work inside Bruin CLI so it becomes part of a governed pipeline instead of another script with a scheduler attached.

That is the cleaner abstraction.

Frequently asked questions

What is the best alternative to dlt?

Bruin is the best dlt alternative when ingestion should live in a pipeline with SQL transforms, quality checks, and lineage, using ingestr for routine loads and Python assets for custom sources. If you only want a pure Python library with nothing around it, dlt itself is still a good choice.

What is the closest like-for-like replacement for dlt code?

For custom extraction, a Bruin Python asset is the closest match: a materialize() function that returns or yields rows, which Bruin loads into the warehouse for you. For routine syncs, ingestr replaces the dlt code with one command.

Is dlt open source?

Yes, dlt is licensed Apache 2.0, and dltHub sells a managed platform on top of it. Bruin's CLI is also Apache 2.0, and Bruin Cloud, the managed service, starts free on the Team plan.

Is ingestr open source?

ingestr is source-available under the Functional Source License (FSL-1.1-ALv2): free for internal use, and each release converts to Apache 2.0 two years after it ships. The Bruin CLI that runs ingestr assets is open source under Apache 2.0.

Is ingestr faster than dlt?

In the MongoDB to Postgres benchmark published in the ingestr repo, ingestr v1 took 2.86 seconds and dlt took 12.55 seconds. Results depend on the workload, so Bruin publishes the wider benchmark set on its ingestr benchmarks page for you to compare.

Can I write custom Python ingestion in Bruin?

Yes. A Bruin Python asset defines materialize() and returns a pandas or Polars DataFrame, a PyArrow table, a list of dicts, or a generator, and Bruin loads it with create+replace, append, merge, or delete+insert.

Does Bruin keep pipeline state like dlt?

dlt stores state in the destination, in tables such as _dlt_pipeline_state, to support incremental loading and resumable runs. In Bruin, ingestr v1 loads data without separate pipeline state: a run is defined by the asset's source, destination, strategy, incremental key, and interval.

Should I use Airbyte or dlt?

Use Airbyte if you want a UI and a connector platform, and dlt if you need code-level control of extraction. Bruin fits when you want code-first ingestion plus transforms, checks, and lineage in one project.

Is Fivetran a dlt alternative?

Yes, if you would rather buy managed connectors than write code, and its Connector SDK even runs custom Python connectors on Fivetran's platform. Bruin is the code-first alternative when you want engineering control and the transforms and checks in the same project.

Sources

• dlt docs: introduction
• dlt docs: incremental loading
• dlt docs: schema evolution
• dlt docs: destination tables and pipeline state
• dlt on GitHub (Apache 2.0)
• dltHub pricing and plans
• Bruin docs: Python assets
• Bruin docs: materialization
• Bruin docs: ingestr assets
• Bruin docs: ingestion overview
• Bruin docs: MCP
• ingestr on GitHub
• ingestr MongoDB to Postgres benchmark image
• Airbyte documentation
• Airbyte licences
• Sling documentation
• Sling CLI on GitHub
• Meltano Singer SDK documentation
• Fivetran connectors
• Fivetran Connector SDK