Prepare customer information, reporting files, or recurring exports, deliver them between systems, and keep a record of each run.
Explore workflow examples · See how it works
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Free Alpha evaluation. No card required. Use test data and non-production systems.Illustrative Alpha configurations. Your workflows can use other combinations of the available connectors and operations.
When purchase details live outside the CRM, prepare customer order summaries from your database, such as MongoDB, and write them to mapped HubSpot properties. Account managers can review that context alongside the customer record.
Join customer and order records from a database or CSV files in GitHub into a JSON reporting file in S3-compatible storage. The synthetic walkthrough below uses PostgreSQL and shows the prepared file and its run record.
Read order records from an HTTP API’s JSON response, prepare them, and deliver a scheduled CSV export to the agreed FTP path, ready for the partner’s next import.
Select ledger rows from Snowflake for an accounting period and export a CSV file to S3-compatible storage for reconciliation and later review.
In this synthetic example, customer details and paid orders need to be brought together for reporting. Braidplane collects both, matches orders to customers, and delivers a prepared file.
Read the PostgreSQL-to-S3 walkthrough. Requires an Alpha account, the public CLI, and your own non-production resources.
Setup needs someone who understands your systems and data. Configure connections and permissions, prepare the records with SQL, and review the workflow definition in Git. Then deploy a version and run it manually or on a schedule.
Keep a record of workflow changes so your team can review them before deployment.
Define the fields and types you expect. Braidplane checks them where your workflow declares those expectations.
Start a workflow manually or configure a schedule. Braidplane manages execution and keeps a run record.
See which steps completed or failed and inspect the available diagnostics to investigate.
Read the customer records, orders, or other information your workflow needs.
Collect customer details and paid orders.
Combine related records and select the information you need.
Match each order with its customer.
Send the prepared information to another system or file.
Deliver a prepared reporting file.
Workflows are pipelines with source, transform, and sink steps. Git holds the reviewable definition; SQL prepares data, and explicit input and output contracts check declared fields and types between steps. Configuration requires knowledge of your systems and data.
Two source reads, one SQL join, and one JSON write, with explicit data checks between steps.
metadata: slug: golden-path-joined-orders name: Golden Path Joined Orderssteps: - kind: query slug: fetch-paid-orders name: Fetch paid orders query: data_source_ref: golden-path-orders-postgres operation_type: sql operation_ref: golden-path-orders output_contract: name: golden-path-paid-orders representation: rows fields: - name: order_id type: string required: true - name: customer_slug type: string required: true - name: order_total_cents type: integer required: true - name: discount_cents type: integer required: true - name: net_total_cents type: integer required: true - name: placed_at_utc type: string required: true - kind: query slug: fetch-active-customers name: Fetch active customers query: data_source_ref: golden-path-customers-postgres operation_type: sql operation_ref: golden-path-customers output_contract: name: golden-path-active-customers representation: rows fields: - name: customer_slug type: string required: true - name: customer_name type: string required: true - name: customer_segment type: string required: true - name: region type: string required: true - kind: transform slug: join-orders-customers name: Join orders to active customers transform: inputs: orders: fetch-paid-orders customers: fetch-active-customers input_contract: name: golden-path-join-inputs representation: rows fields: - name: orders.order_id type: string required: true - name: orders.customer_slug type: string required: true - name: orders.order_total_cents type: integer required: true - name: orders.discount_cents type: integer required: true - name: orders.net_total_cents type: integer required: true - name: orders.placed_at_utc type: string required: true - name: customers.customer_slug type: string required: true - name: customers.customer_name type: string required: true - name: customers.customer_segment type: string required: true - name: customers.region type: string required: true sql: | SELECT orders.order_id AS order_id, orders.customer_slug AS customer_slug, customers.customer_name AS customer_name, customers.customer_segment AS customer_segment, customers.region AS region, orders.order_total_cents AS order_total_cents, orders.discount_cents AS discount_cents, orders.net_total_cents AS net_total_cents, CAST(orders.net_total_cents AS DOUBLE) / 100 AS net_total_amount, orders.placed_at_utc AS placed_at_utc FROM orders JOIN customers ON customers.customer_slug = orders.customer_slug ORDER BY placed_at_utc, order_id output_kind: rows output_contract: name: golden-path-joined-orders representation: rows fields: - name: order_id type: string required: true - name: customer_slug type: string required: true - name: customer_name type: string required: true - name: customer_segment type: string required: true - name: region type: string required: true - name: order_total_cents type: integer required: true - name: discount_cents type: integer required: true - name: net_total_cents type: integer required: true - name: net_total_amount type: number required: true - name: placed_at_utc type: string required: true - kind: sink slug: write-joined-orders name: Write joined orders to S3 sink: idempotency_class: naturally_idempotent input: join-orders-customers input_contract: name: golden-path-joined-orders-s3-input representation: rows fields: - name: order_id type: string required: true - name: customer_slug type: string required: true - name: customer_name type: string required: true - name: customer_segment type: string required: true - name: region type: string required: true - name: order_total_cents type: integer required: true - name: discount_cents type: integer required: true - name: net_total_cents type: integer required: true - name: net_total_amount type: number required: true - name: placed_at_utc type: string required: true data_source_ref: golden-path-s3-sink operation_type: s3 operation_ref: golden-path-s3-write-joined-orders config: key: runs/{{runtime.sink_replay_identity}}/joined-orders.json format: json content_type: application/json metadata: source: braidplane-golden-path run: "{{runtime.run_slug}}" overwrite: trueConnect databases, warehouses, APIs, and storage in the same workflow.
Alpha evaluation is free and requires no card. Use test data and non-production systems to check a workflow, its output, and a failure case. Alpha is not production access.
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An agent can write a workflow, and a small one-off script may be enough. Braidplane is useful when your team needs to run that workflow repeatedly and maintain it.
An agent can build these controls into a custom solution too. Braidplane gives your team a shared platform to operate them. You still need to review the workflow and the access you grant.
Git-based workflows are not unique to Braidplane. Dagster also offers YAML components and data checks; Prefect supports flow code stored in Git. Those features alone are no reason to switch.
Braidplane focuses on moving business data through built-in connector operations and SQL transforms. A pipeline definition describes what to collect, how to prepare it, and where to deliver it. Connections are selected for each environment, deployment selects a specific version, and the product records its runs.
Consider Braidplane if that workflow model fits your job and you want to configure and operate it in one product. This is a choice about product fit, not a claim that Dagster or Prefect cannot do the same job. Try a real workflow and a failure case with the available Alpha connectors. If your existing tool works well, keep it.
Resolve conflicting file edits in Git through your usual review and merge process. Braidplane does not resolve those edits for you.
Before importing changes, Braidplane shows you a preview. If someone updates the workflow in Git before you apply it, Braidplane asks you to preview again so you can review the latest changes.
Importing changes does not deploy them automatically. You choose a persisted workflow version and an environment to deploy. Runs use that deployed version, so a later branch change does not silently change what executes.
Yes. An agent can help write or edit a pipeline definition in Git. Review the changes and validate the definition before deploying it. Braidplane runs supported connector operations and SQL transforms; a generated script does not automatically become an executable pipeline.
Yes. The person configuring connections and workflow steps needs to understand the systems and data involved. SQL prepares data, and Git keeps the reviewable definition. Once configured, your team can follow runs and inspect results in the product.
Check the connector catalogue above. “Available in Alpha” lists current connectors; “Planned” entries are future work. Read and write operations vary by connector. If your system is missing, tell us about your workflow so we can scope the connection it needs.
The run record shows step status and available diagnostics so you can investigate the failure. After fixing the cause, check which writes already completed before starting another run. A failed run can still have delivered some data.
Discuss whether Braidplane fits your workflow on a free call, or explore independently with a free Alpha account.
Free Alpha evaluation. No card required. Use test data and non-production systems.