Sling vs dlt — YAML pipelines vs Python pipelines

Sling and dlt both move data. Both are open source. Beyond that, they’re built for different teams.

dlt is a Python library. You write source connectors as Python classes, call pipeline.run(), and your data lands in Snowflake or Postgres or DuckDB. If your team lives in Python, dlt fits naturally into how you already work.

Sling is a single Go binary. You describe your replication in YAML, run sling run --replication replication.yaml, and the data moves. No interpreter, no library imports, no dependency tree. If you want something a non-engineer can read and a DevOps team can deploy anywhere without touching a virtualenv, Sling is usually the right shape.

TL;DR

Where dlt wins

Code-first pipelines in your existing Python stack

If your data engineers write FastAPI, Airflow DAGs, Prefect flows, and pandas notebooks, dlt drops into that stack without friction. You write a source in Python, you test it in pytest, you run it from the same virtual environment as everything else. The pipeline is just more code.

Sling’s replication.yaml is readable and version-controllable, but it’s a config file, not code. If your team already has Python tooling for scheduling, testing, and deploying, dlt integrates naturally. Sling asks you to run a separate binary.

Nested JSON normalization

Both tools infer column types automatically. Where dlt goes further is with deeply nested JSON: point it at a REST API returning arrays-of-objects and it flattens the structure into related tables, managing foreign keys and schema evolution automatically. For exploratory work and API ingestion where the nested schema isn’t known ahead of time, this saves real setup time.

Sling infers types and handles JSON columns, but nested normalization is manual — you use jmespath-based column transforms to reshape the data. Sling’s output is deterministic and explicit; dlt’s nested normalization is more automatic but can produce surprising column shapes on irregular inputs. If you’re regularly ingesting REST APIs with unknown nested schemas, dlt’s approach fits better.

Python ecosystem fit and in-flight transforms

dlt lets you add transformation logic in Python between the extract and load steps. Read from Salesforce, filter with pandas, normalize with Polars, load to Snowflake, all in one pipeline script. The binary doesn’t run in-flight Python — that’s a deliberate design choice. Sling’s answer is “load first, transform in SQL or dbt after.”

sling-python’s Connection class does let you query any Sling connection and get back a pyarrow Table:

from sling import Connection

conn = Connection("MY_POSTGRES")
table = conn.exec(
  """
  select *
  from orders
  where created_at > '2026-01-01'
  """,
  return_type="arrow",
)
# table is a pyarrow.Table — pass to pandas, Polars, write to Parquet, etc.

This composes well with post-load Python workflows. But if you need to transform data before it lands in the destination — applying business rules mid-pipeline — dlt’s approach is more direct.

DuckDB-first local development

dlt leans hard on DuckDB as a local development destination. Run your pipeline locally against a DuckDB file, inspect the output with the DuckDB shell or a notebook, then swap the destination to Snowflake for production. The iteration loop is tight.

Sling supports DuckDB too, but it’s not the centerpiece of the developer experience the way it is in dlt’s docs and tooling.

Where Sling wins

A single binary with no runtime

In every environment where you run dlt, you need Python installed, a virtual environment, and all of dlt’s dependencies resolved and available. On a clean machine, pip install dlt pulls in pyarrow, pendulum, pydantic, sqlalchemy, and several others. Cold-start latency on Lambda is real.

Sling ships as a single static binary with no external dependencies. Download it, put it on $PATH, run it. On the same Lambda cold start, a Go binary typically initializes 10-50x faster than a Python process with its import chain.

For teams with strict environments (air-gapped deployments, read-only production servers, minimal containers), this matters a lot.

Memory footprint

dltHub ran a direct benchmark in August 2025: loading a ~9.74 GB Postgres dataset into Snowflake. Their headline was query time. What their data also showed:

Sling Pro used 6× less memory than dlt ConnectorX on the same workload. For teams running pipelines on shared infrastructure — t3.medium instances, Lambda functions, k8s pods with memory limits — RAM is often the real constraint. A pipeline that peaks at 8.9 GB requires a dedicated instance or managed service. One that peaks at 1.4 GB runs comfortably on a $12/mo VPS.

The benchmark also showed that dlt PyArrow (1.5 GB peak) was only 49 seconds slower than Sling. If Python ecosystem fit matters and memory is a concern, PyArrow mode is worth knowing about. ConnectorX is faster on raw throughput but at a real infrastructure cost.

Log-based CDC for transactional databases

Both tools support log-based CDC, but the implementation depth differs. dlt’s CDC comes through its pg_replication verified source (PostgreSQL logical decoding) and Debezium integration for MySQL and SQL Server. These are separate source connectors you install and configure on top of the base library.

Sling’s CDC is built into the core binary: native logical replication from PostgreSQL, native binlog reading from MySQL, and SQL Server CDC table support — all configured with the same YAML replication file you use for any other stream. No extra dependencies, no separate connector to maintain.

For teams moving data from transactional databases at high frequency, both tools cover the ground. Sling’s advantage is that CDC is a first-class citizen of the config-file workflow, not a separate integration layer.

Flat pricing vs the credit meter

dltHub Pro is $119/mo. dltHub Scale is $1,190/mo. Both bill in credits, with 100 and 1,000 credits per month respectively, plus overage bundles. The public preview documentation describes credits as “active platform workload execution” but doesn’t publish a per-credit to compute-time mapping.

Sling Platform is flat: $0 / $99 / $249/mo. No credits. No overages. If you run 10 replications a day or 10,000, the price is the same.

Credit-based pricing is a natural growth tax. As your data volumes increase, credit consumption grows, and you either pay more or throttle your pipelines. Flat pricing doesn’t have this problem.

File systems as first-class sources

dlt is strong as a file system destination: write Parquet or JSONL to S3, GCS, or Azure Blob. As a file system source, the story is thinner. Reading from SFTP, processing S3 prefixes, or syncing Azure Blob to Postgres are supported but require more custom Python.

Sling treats file systems as full peers to databases. S3 to Snowflake, SFTP to Postgres, Azure Blob to ClickHouse, GCS to BigQuery: all declarative YAML, same syntax as a database replication. For teams with file-based data sources (vendor drops, SFTP feeds, S3 exports from legacy systems), this is a real operational difference.

Connector quality outside the core tier

Sling’s 40+ connectors are all written in Go by the same team. The ClickHouse connector uses the same connection pool and bulk-copy logic as the Postgres connector. Same architecture, same test suite.

dlt’s core destinations — Postgres, Snowflake, BigQuery, DuckDB, Redshift — are well-maintained. Beyond that tier, connectors are community-contributed Python with varying test coverage.

Two production-breaking issues surfaced in May-June 2026:

• dlt ClickHouse destination (issue #4014): schema-qualified table names with backtick escaping break query execution. Tables load but immediately fail to query. A first-time ClickHouse user would have no way to distinguish this from a permissions problem.
• dlt BigQuery incremental connector (issue #3998): when the extraction returns an empty result set, the destination table is silently truncated. Existing rows get deleted with no error, no warning, no failed run status.

Neither of these affects the destinations where dlt is strongest. But if your destination is outside the Postgres/Snowflake/DuckDB core, the quality picture changes.

Config files non-engineers can read

Sling’s replication.yaml looks like this:

source: MY_POSTGRES
target: MY_SNOWFLAKE

defaults:
  mode: incremental
  object: '{stream_schema}.{stream_table}'
  primary_key: id
  update_key: updated_at

streams:
  public.users:
  public.orders:
  public.payments:
  other_schema.*:

# Python equivalent — same pipeline, same single binary
from sling import Replication

Replication(
  source="MY_POSTGRES",
  target="MY_BIGQUERY",
  defaults={
    "mode": "incremental",
    "object": "{stream_schema}.{stream_table}",
    "primary_key": ["id"],
    "update_key": "updated_at",
  },
  streams={
    "public.orders": {},
    "public.customers": {},
  },
).run()

A data analyst can read this. A DevOps engineer deploying it for the first time can read it. When something breaks at 2am, the on-call engineer can read the config without understanding Python.

dlt’s equivalent is Python code. It’s more powerful, but that means only people comfortable with Python can modify it. For teams where the pipeline owner is different from the pipeline author, which is common in mid-size companies where data engineers build and analysts maintain, the YAML vs code tradeoff matters.

Wildcard stream selection

Sling lets you select entire schemas with a glob pattern:

streams:
  public.*:          # replicate every table in the public schema
  analytics.event_*: # only tables starting with event_

dlt requires you to enumerate sources explicitly in Python. For teams syncing warehouses with dozens of tables, Sling’s wildcard support cuts replication config from hundreds of lines to a handful.

Connection management built in

Sling ships with a full connection management layer. Connections live in ~/.sling/env.yaml (or environment variables), and the CLI has dedicated commands:

sling conns list          # list all configured connections
sling conns test MYCONN   # verify connectivity
sling conns discover MYCONN  # browse tables and schemas
sling conns exec MYCONN -q "select 1"  # run ad-hoc SQL

dlt connections are Python objects you construct per-pipeline. There’s no shared connection registry or interactive discovery built into the library. For data teams managing many environments, Sling’s centralized connection system reduces credential management overhead.

Column-level control and merge strategies

Sling exposes column definitions at the replication level — pin types, add DDL constraints, and annotate columns as part of the YAML config without writing SQL. Column modifiers (available from v1.5.20+) sit directly in the type slot:

streams:
  public.users:
    object: public.users_dim
    columns:
      id:         bigint not_null primary_key
      email:      text not_null unique
      region:     text index
      event_type: text description('normalized event category')
      raw_payload: text

For incremental loads, Sling also exposes the merge behavior itself. Four strategies are available via target_options.merge_strategy — update_insert (upsert), delete_insert, insert (append-only), and update (update-only) — with the optimal default picked per database:

source: MY_POSTGRES
target: MY_SNOWFLAKE

defaults:
  mode: incremental
  primary_key: [id]

streams:
  public.customers:    # uses the database default (update_insert on Snowflake)

  public.audit_log:
    target_options:
      merge_strategy: insert  # append-only, never touch existing rows

  public.product_catalog:
    target_options:
      merge_strategy: update  # enrich existing rows, never insert new ones

dlt’s write dispositions cover similar ground, but the column-constraint and annotation layer is absent — you’d handle DDL shaping outside the pipeline config.

Who should pick which

Pick dlt if your team writes Python and wants pipelines in the same language as the rest of your stack, or if you’re ingesting REST APIs with unknown schemas and want automatic normalization, or if you need in-flight transformation logic. Also the right call if you’re prototyping with DuckDB and want a tight local dev loop, or if you’re already running Dagster, Prefect, or Airflow.

Pick Sling if you want something that installs in one command and runs anywhere without Python, or if your pipeline owners aren’t Python developers, or if you need CDC with no extra integration layer. Also the right fit if you’re selecting streams by wildcard (my_schema.*), if file systems are primary sources in your pipeline (SFTP, S3 prefix scanning, Azure Blob as source), if you’re running on memory-constrained infrastructure (Lambda, k8s pods, small VMs), or if you want flat pricing that doesn’t scale with data volume.

The actual decision

The choice comes down to one thing: does your team want pipelines as code or pipelines as config?

If the answer is code, pick dlt. Your engineers already write Python, you want in-flight transformation, you want DuckDB-first local dev. That’s a real set of requirements and dlt handles them well.

If the answer is config, pick Sling. You want a binary with no runtime, your pipeline owners aren’t Python developers, you need CDC from Postgres or file-system-as-source. Also a real set of requirements, and Sling handles them well.

Both are free to run. Both have managed tiers. The only way to waste time here is to pick the wrong shape for your team and spend months working around it.

Try Sling: The CLI installs in one line:

curl -fsSL https://slingdata.io/install.sh | bash

Or via Homebrew on Mac: brew install slingdata-io/sling/sling