diff --git a/framework/dlt/.dlt/config.toml b/framework/dlt/.dlt/config.toml
new file mode 100644
index 00000000..2db5fac8
--- /dev/null
+++ b/framework/dlt/.dlt/config.toml
@@ -0,0 +1,7 @@
+# put your configuration values here
+#add_dlt_load_id = false
+
+[runtime]
+log_level="WARNING"  # the system log level of dlt
+# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/reference/telemetry
+dlthub_telemetry = false
diff --git a/framework/dlt/.gitignore b/framework/dlt/.gitignore
new file mode 100644
index 00000000..3b28aa3f
--- /dev/null
+++ b/framework/dlt/.gitignore
@@ -0,0 +1,10 @@
+# ignore secrets, virtual environments and typical python compilation artifacts
+secrets.toml
+# ignore basic python artifacts
+.env
+**/__pycache__/
+**/*.py[cod]
+**/*$py.class
+# ignore duckdb
+*.duckdb
+*.wal
\ No newline at end of file
diff --git a/framework/dlt/ddl.sql b/framework/dlt/ddl.sql
new file mode 100644
index 00000000..447e445f
--- /dev/null
+++ b/framework/dlt/ddl.sql
@@ -0,0 +1,41 @@
+CREATE TABLE IF NOT EXISTS "doc"."_dlt_version" (
+	"version" bigint  NOT NULL,
+	"engine_version" bigint  NOT NULL,
+	"inserted_at" timestamp with time zone  NOT NULL,
+	"schema_name" varchar  NOT NULL,
+	"version_hash" varchar  NOT NULL,
+	"schema" varchar  NOT NULL);
+	CREATE TABLE IF NOT EXISTS "doc"."_dlt_loads" (
+	"load_id" varchar  NOT NULL,
+	"schema_name" varchar  ,
+	"status" bigint  NOT NULL,
+	"inserted_at" timestamp with time zone  NOT NULL,
+	"schema_version_hash" varchar  );
+	CREATE TABLE IF NOT EXISTS "doc"."_dlt_pipeline_state" (
+	"version" bigint  NOT NULL,
+	"engine_version" bigint  NOT NULL,
+	"pipeline_name" varchar  NOT NULL,
+	"state" varchar  NOT NULL,
+	"created_at" timestamp with time zone  NOT NULL,
+	"version_hash" varchar  ,
+	"_dlt_load_id" varchar  NOT NULL,
+	"_dlt_id" varchar UNIQUE NOT NULL);
+	CREATE TABLE "doc"."player" (
+	"avatar" varchar  ,
+	"player_id" bigint  ,
+	"aid" varchar  ,
+	"url" varchar  ,
+	"name" varchar  ,
+	"username" varchar  ,
+	"title" varchar  ,
+	"followers" bigint  ,
+	"country" varchar  ,
+	"location" varchar  ,
+	"last_online" bigint  ,
+	"joined" bigint  ,
+	"status" varchar  ,
+	"is_streamer" boolean  ,
+	"verified" boolean  ,
+	"league" varchar  ,
+	"_dlt_load_id" varchar  NOT NULL,
+	"_dlt_id" varchar UNIQUE NOT NULL);
diff --git a/framework/dlt/example.py b/framework/dlt/example.py
new file mode 100644
index 00000000..a42ac2f9
--- /dev/null
+++ b/framework/dlt/example.py
@@ -0,0 +1,32 @@
+from dlt.common.schema import typing
+
+# default dlt columns
+typing.C_DLT_ID = "__dlt_id"
+"""unique id of current row"""
+typing.C_DLT_LOAD_ID = "__dlt_load_id"
+"""load id to identify records loaded in a single load package"""
+
+import dlt
+from dlt.sources.helpers import requests
+
+# Create a dlt pipeline that will load
+# chess player data to the DuckDB destination
+pipeline = dlt.pipeline(
+    pipeline_name='chess_pipeline',
+    destination=dlt.destinations.sqlalchemy("crate://crate:@localhost/"),
+    #destination=dlt.destinations.postgres("postgresql://postgres:@localhost/"),
+    dataset_name='doc',
+)
+
+# Grab some player data from Chess.com API
+data = []
+for player in ['magnuscarlsen', 'rpragchess']:
+    response = requests.get(f'https://api.chess.com/pub/player/{player}')
+    response.raise_for_status()
+    data.append(response.json())
+
+# Extract, normalize, and load the data
+if __name__ == "__main__":
+    pipeline.run(data,
+        table_name='player',
+    )
diff --git a/framework/dlt/foo_pipeline.py b/framework/dlt/foo_pipeline.py
new file mode 100644
index 00000000..f8bca9a4
--- /dev/null
+++ b/framework/dlt/foo_pipeline.py
@@ -0,0 +1,111 @@
+"""The Intro Pipeline Template contains the example from the docs intro page"""
+
+# mypy: disable-error-code="no-untyped-def,arg-type"
+
+from typing import Optional
+import pandas as pd
+import sqlalchemy as sa
+
+import dlt
+from dlt.sources.helpers import requests
+
+
+def load_api_data() -> None:
+    """Load data from the chess api, for more complex examples use our rest_api source"""
+
+    # Create a dlt pipeline that will load
+    # chess player data to the DuckDB destination
+    pipeline = dlt.pipeline(
+        pipeline_name="chess_pipeline", destination='sqlalchemy', dataset_name="player_data"
+    )
+    # Grab some player data from Chess.com API
+    data = []
+    for player in ["magnuscarlsen", "rpragchess"]:
+        response = requests.get(f"https://api.chess.com/pub/player/{player}")
+        response.raise_for_status()
+        data.append(response.json())
+
+    # Extract, normalize, and load the data
+    load_info = pipeline.run(data, table_name="player")
+    print(load_info)  # noqa: T201
+
+
+def load_pandas_data() -> None:
+    """Load data from a public csv via pandas"""
+
+    owid_disasters_csv = (
+        "https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/"
+        "Natural%20disasters%20from%201900%20to%202019%20-%20EMDAT%20(2020)/"
+        "Natural%20disasters%20from%201900%20to%202019%20-%20EMDAT%20(2020).csv"
+    )
+    df = pd.read_csv(owid_disasters_csv)
+
+    pipeline = dlt.pipeline(
+        pipeline_name="from_csv",
+        destination='sqlalchemy',
+        dataset_name="mydata",
+    )
+    load_info = pipeline.run(df, table_name="natural_disasters")
+
+    print(load_info)  # noqa: T201
+
+
+def load_sql_data() -> None:
+    """Load data from a sql database with sqlalchemy, for more complex examples use our sql_database source"""
+
+    # Use any SQL database supported by SQLAlchemy, below we use a public
+    # MySQL instance to get data.
+    # NOTE: you'll need to install pymysql with `pip install pymysql`
+    # NOTE: loading data from public mysql instance may take several seconds
+    engine = sa.create_engine("mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam")
+
+    with engine.connect() as conn:
+        # Select genome table, stream data in batches of 100 elements
+        query = "SELECT * FROM genome LIMIT 1000"
+        rows = conn.execution_options(yield_per=100).exec_driver_sql(query)
+
+        pipeline = dlt.pipeline(
+            pipeline_name="from_database",
+            destination='sqlalchemy',
+            dataset_name="genome_data",
+        )
+
+        # Convert the rows into dictionaries on the fly with a map function
+        load_info = pipeline.run(map(lambda row: dict(row._mapping), rows), table_name="genome")
+
+    print(load_info)  # noqa: T201
+
+
+@dlt.resource(write_disposition="replace")
+def github_api_resource(api_secret_key: Optional[str] = dlt.secrets.value):
+    from dlt.sources.helpers.rest_client import paginate
+    from dlt.sources.helpers.rest_client.auth import BearerTokenAuth
+    from dlt.sources.helpers.rest_client.paginators import HeaderLinkPaginator
+
+    url = "https://api.github.com/repos/dlt-hub/dlt/issues"
+
+    # Github allows both authenticated and non-authenticated requests (with low rate limits)
+    auth = BearerTokenAuth(api_secret_key) if api_secret_key else None
+    for page in paginate(
+        url, auth=auth, paginator=HeaderLinkPaginator(), params={"state": "open", "per_page": "100"}
+    ):
+        yield page
+
+
+@dlt.source
+def github_api_source(api_secret_key: Optional[str] = dlt.secrets.value):
+    return github_api_resource(api_secret_key=api_secret_key)
+
+
+def load_data_from_source():
+    pipeline = dlt.pipeline(
+        pipeline_name="github_api_pipeline", destination='sqlalchemy', dataset_name="github_api_data"
+    )
+    load_info = pipeline.run(github_api_source())
+    print(load_info)  # noqa: T201
+
+
+if __name__ == "__main__":
+    load_api_data()
+    load_pandas_data()
+    load_sql_data()
diff --git a/framework/dlt/pokemon.py b/framework/dlt/pokemon.py
new file mode 100644
index 00000000..9ca44257
--- /dev/null
+++ b/framework/dlt/pokemon.py
@@ -0,0 +1,21 @@
+"""data load tool (dlt) — the open-source Python library for data loading
+
+How to create a data loading pipeline with dlt in 3 seconds:
+
+1. Write a pipeline script
+>>> import dlt
+>>> from dlt.sources.helpers import requests
+>>> dlt.run(requests.get("https://pokeapi.co/api/v2/pokemon/").json()["results"], destination="duckdb", table_name="pokemon")
+
+2. Run your pipeline script
+  > $ python pokemon.py
+
+3. See and query your data with autogenerated Streamlit app
+  > $ dlt pipeline dlt_pokemon show
+
+Or start with our pipeline template with sample PokeAPI (pokeapi.co) data loaded to bigquery
+
+  > $ dlt init pokemon bigquery
+
+For more detailed info, see https://dlthub.com/docs/getting-started
+"""
diff --git a/framework/dlt/requirements.txt b/framework/dlt/requirements.txt
new file mode 100644
index 00000000..13c3bbb2
--- /dev/null
+++ b/framework/dlt/requirements.txt
@@ -0,0 +1 @@
+dlt[sqlalchemy]>=1.3.0