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Version: 2.0.0

Run Your First Data Integration

Before you start

Finish Install Tabsdata and Connect an AI Agent first, and make sure the server is running.

This quickstart uses the Tabsdata MCP integration. To deploy and run Tabsdata without an LLM, switch to the developer doc mirror.

We will be building a Tabsdata Workflow that:

  1. ingests a local persons.csv into a Tabsdata table called persons
  2. builds a second table called people that drops the age column and adds a full_name column
  3. writes people back to your file system as people.jsonl
Click any node for what it does
Local File
Local File
Publisher
qs_pub
Pending
Table
persons
Pending
Transformer
qs_tfr
Pending
Table
people
Pending
Subscriber
qs_sub
Pending
Local File
Local File
Not written yet
Step 1Get the example project
tdk examples extract --name quickstart-ai --dir qs-example
⌄qs-example
⌄input-data
persons.csv
⌄output-data
conn-input.yaml
conn-output.yaml
README.txt

Creates qs-example/, which contains the source data persons.csv and two connection files.

Step 2Login to Tabsdata and start your LLM

Log in, then start your agent from the same terminal:

tdk login --server localhost --user admin --password tabsdata
claude
Step 3Prompt your LLM to build a Tabsdata workflow

Give the following prompt to your LLM:

Build a tabsdata workflow that 
1. ingests `qs-example/input-data/persons.csv`
2. creates a new column for the full name and drops the age column,  
3. writes the result as a .jsonl to the `output-data` folder

More info can be found under `qs-example/README.md`
AI agent session
Build a tabsdata workflow that 1. ingests qs-example/input-data/persons.csv 2. creates a new column for the full name and drops the age column, 3. writes the result as a .jsonl to the output-data folder More info can be found under qs-example/README.md

Read 1 file, listed 1 directory (ctrl+o to expand)

persons.csv has first_name, last_name and age. Three functions cover this: a publisher for the CSV, a transformer for the column changes, and a subscriber for the .jsonl. Getting the connection and publisher guides.

Called tabsdata 3 times (ctrl+o to expand)

Writing , then a source collection pointed at input-data.

Called tabsdata 2 times (ctrl+o to expand)

Registering the publisher that produces persons.

@publisher(
  source=LocalFileSrc(paths=["persons.csv"], format=SrcFileFormat.CSV),
  output_tables=["persons"],
)
def ingest_persons(rows: TableFramesSpec) -> TableFrameSpec:
  return rows[0] if rows else None

Called tabsdata 2 times (ctrl+o to expand)

Registered and committed. Now the transformer — full_name from first_name and last_name, age dropped.

@transformer(
  input_tables=["persons"],
  output_tables=["people"],
)
def build_people(persons: TableFrameSpec) -> TableFrameSpec:
  return persons.with_columns(
      (col("first_name") + " " + col("last_name")).alias("full_name"),
  ).drop("first_name", "last_name", "age")

Called tabsdata 2 times (ctrl+o to expand)

Registered and committed. Last, the subscriber writing people out as .jsonl. Getting the connection and subscriber guides.

Called tabsdata 3 times (ctrl+o to expand)

Writing , then a destination collection pointed at output-data.

Called tabsdata 2 times (ctrl+o to expand)

Registering the subscriber.

@subscriber(
  tables=["people"],
  destination=LocalFileDst(path="people.jsonl", format=DstFileFormat.NDJSON),
)
def export_people(people: TableFrameSpec) -> None:
  pass

Called tabsdata 3 times (ctrl+o to expand)

Registered. The publisher's commit triggered the transformer, which triggered the subscriber, all in one execution plan — committed, 0 failures.

people.jsonl is live in qs-example/output-data/:

┌──────────────┐
│  full_name   │
├──────────────┤
│ Ada Lovelace │
├──────────────┤
│ Grace Hopper │
└──────────────┘
Step 4Check Workflow Execution
⌄qs-example
⌄input-data
persons.csv
⌄output-data
people.jsonl
conn-input.yaml
conn-output.yaml
README.txt

After your LLM finishes processing your request, you should have a new people.jsonl file in the qs-example/output-data/ folder.

Step 5Open the UI

Go to http://localhost:2457 and log in with admin / tabsdata. You'll see your project, its collections, and the tables the flow just produced.