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Know what your change will break, across every platform, before it lands.

Callisto checks every pull request against lineage and runtime history from Snowflake, dbt, Airflow, Azure Data Factory and the rest of your estate.

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  • Column-level lineage, no manual curation
  • Runtime metrics per job version
  • Answers in the PR thread, Slack, CLI or web UI
  • Read-only outside PR comments and suggested changes

Trusted by top global enterprises

  • Citi
  • RBC
  • JCPenney
  • Skylar
  • Cotiviti
  • 3M
  • Reliant Funding

Your agent is locally smart and globally blind.

Your agent sees the code in front of it. The context that makes the change safe lives elsewhere.

  • The Semantic Model
    your Cortex agent
    reads

    Rename a column and the agent won’t error. It keeps answering, just worse, because a relationship it relies on quietly broke.

    Source
    Snowflake
    ANALYTICS.TENANTS
    Columns
    tenant_id
    tenant_name
    plan_tier
    created_at
    + 9 more
    Semantic view
    product_usage
    RELATIONSHIPS
    usage_to_tenantBroken
    AI_SQL_GENERATION
    active tenant hintStale
    Cortex agent
    product analytics
    Which tenants werehighly active last week?
    Here are the 14 mostactive tenants…
    No errors
    Your agent is still answering
    1 broken relationship · 1 stale hint
  • Source
    Production database
    analytics.orders
    Selected columns
    customer_id
    order_date
    amount
    product_category
    + 12 more
    orders_to_crm
    Reverse-ETL sync
    Active
    Syncs warehouse rows to CRM
    Runs every 15 minutes
    Last synced
    3 minutes ago
    24,180 rows
    Destination
    CRM
    Customer Account
    Mapped fields
    customer_idAccount ID
    order_dateLast Order Date
    amountLifetime Value
    product_categoryCategory
    + 8 more
    Not in catalog
    This dependency is not registered
    in your data catalog.
    Discovered by impact analysis
    This sync uses analytics.orders but is not
    documented in your catalog.

    The Tableau extract
    three systems away

    Two joins and an orchestrator sit between your change and the dashboard a director opens daily.

  • jupyter
    Notebooks
    churn_features.ipynb
    eda.ipynb
    customer_ltv.ipynb
    daily_metrics.ipynb
    revenue_analysis.ipynb
    churn_features.ipynb
    Scheduled
    Code
    Markdown
    Run
    View
    1# Load customer data
    2df = spark.table("analytics.orders")
    3features = df.select(
    4"customer_id",
    5"order_date",
    6"amount",
    7"product_category"
    8)
    alexwocs
    Last edited 14 Mar 2023 · 2 years ago
    Author deactivated
    customer_id
    This column is being renamed
    in an open change.
    Runs every Monday
    06:00 UTC
    Column lineage
    customer_id
    used in 8 downstream assets
    stg_orders
    dim_customer
    customer_metrics
    + 5 more

    The notebook
    someone scheduled
    two years ago

    It still runs every Monday. The person who wrote it left. It reads the column you are about to rename.

  • The reverse-ETL
    sync nobody
    documented

    It pushes warehouse rows into a system your catalog has never seen. Sales notices before you do.

    Source
    Production databaseRAW.ORDERS
    Columns
    order_id
    customer_id
    order_date
    amount
    product_category
    + 12 more
    dbtstg_orders
    Airflowdaily_revenue
    Destination
    Tableaurevenue_daily.hyper
    Daily Revenue
    Extract refreshes dailyUsed in 4 dashboards · 12 viewers
    Finance dashboardOpened daily by 12 users
    3 systems downstreamThis change affects a Tableau extractused by a finance dashboard.

Three things your current stack cannot tell you

Your catalog knows what depends on what, not what it costs.

Callisto joins operational lineage to data lineage. Other tools tell you which models break. Callisto also tells you the new version scans four times the bytes.

Metrics come from warehouse and orchestrator run history for each job version. Callisto works from metadata, lineage and runtime behaviour only.

Every other tool compares your change against what’s already shipped.

Callisto compares proposed changes against each other. When every team ships faster, collisions grow faster than changes do, and no per-change analysis catches a collision, because each change is individually fine.

Your lineage stops where your warehouse does.

One column-level graph across dbt, Airflow, Azure Data Factory, Snowflake, S3 and Tableau. Edges are parsed from SQL or inferred from metadata, and unmapped namespaces show as gaps.

Callisto column lineage view for ANALYTICS.REVENUE_DAILY.CUSTOMER_ID. Upstream: dbt stg_orders and Snowflake CORE.ORDERS, parsed from SQL, and Avro events on S3, inferred by schema match. Downstream: a Tableau extract, plus a Salesforce namespace detected but not yet mapped.

Built for three people

  • Data engineer / analytics engineer

    Keeps them up at night

    Being the person who renamed the column and broke a dashboard they have never heard of.

    What Callisto gives them

    The blast radius of the diff, in the PR: every downstream consumer, column-level, with upstream dependencies named. Ask “who uses this table” and get an answer in the thread.

  • Data platform lead / engineering manager

    Keeps them up at night

    Velocity up, production stability down, and no way to prove which change caused which incident.

    What Callisto gives them

    Change management that lives in review instead of in a meeting. A written record per pull request, runtime cost visible before merge, and a review bottleneck that does not grow with agent output.

  • Data architect / enterprise data lead

    Keeps them up at night

    Committing to a migration plan built on an incomplete map of the estate.

    What Callisto gives them

    Design-time and runtime lineage in one landscape, impact assessment per proposed change, point-in-time (ASOF) reads of how the estate looked, and an explicit list of what is not yet mapped.

Is this you?

Worth knowing before a call rather than after one.

Strong fit

  • Snowflake plus at least two other platforms
  • Something consuming data outside the warehouse: BI, reverse-ETL, notebooks, applications
  • Airflow, Dagster or similar orchestration
  • More than one team touching the same tables
  • A live migration, consolidation or AI-enablement programme
  • Engineers using AI coding tools on pipeline code

Not a fit

  • Single-platform shops
  • Small teams where one person genuinely holds the whole picture
  • Organisations that prohibit AI tooling outright. A local-model story is coming.
  • Anyone shopping for a data catalog, a business glossary or data quality monitoring. Those are different products and we do not have them.

Horizon works beautifully inside Snowflake. Your estate isn’t inside Snowflake.

Bundled lineage is design-time lineage. It describes how objects are declared to relate, inside one platform’s boundary, and stops short of how the jobs actually behaved on their last run.

Callisto reads design-time and runtime together, across dbt, Airflow, Azure Data Factory, Snowflake and Avro on S3, and tells you what the estate did rather than what it was declared to do.

See an impact report on your own pull request.

Thirty minutes, your estate, one open PR. Callisto reads GitHub and posts comments. It changes nothing else.

Single-tenant deployment. Callisto reads metadata, lineage and run history. It is read-only outside the comments it posts.