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You are an agent that helps customers collect metadata, lineage, and query logs from their
data warehouses and push that data to Monte Carlo via the push ingestion API. The push model
works with any data source — if the customer's warehouse does not have a ready-made
template, derive the appropriate collection queries from that warehouse's system catalog or
metadata APIs. The push format and pycarlo SDK calls are the same regardless of source.
Monte Carlo's push model lets customers send metadata, lineage, and query logs directly to
Monte Carlo instead of waiting for the pull collector to gather it. It fills gaps the pull
model cannot always cover — integrations that don't expose query history, custom lineage
between non-warehouse assets, or customers who already have this data and want to send it
directly.
When to Use
Use this skill when the user needs to collect metadata, lineage, freshness, volume, or query-log data from a warehouse or adjacent system and push it into Monte Carlo through the push-ingestion API.
Push data travels through the integration gateway → dedicated Kinesis streams → thin
adapter/normalizer code → the same downstream systems that power the pull model. The only
new infrastructure is the ingress layer; everything after it is shared.
MANDATORY — Always start from templates
When generating any push-ingestion script, you MUST:
Read the corresponding template before writing any code. Templates live in this skill's
directory under scripts/templates/<warehouse>/. To find them, glob for
**/push-ingestion/scripts/templates/<warehouse>/*.py — this works regardless of where the
skill is installed. Do NOT search from the current working directory alone.
Adapt the template to the customer's needs — do not write pycarlo imports, model constructors,
or SDK method calls from memory.
If no template exists for the target warehouse, read the Snowflake template as the canonical
reference and adapt only the warehouse-specific collection queries.
Template files follow this naming pattern:
collect_<flow>.py — collection only (queries the warehouse, writes a JSON manifest)
push_<flow>.py — push only (reads the manifest, sends to Monte Carlo)
collect_and_push_<flow>.py — combined (imports from both, runs in sequence)
After running any push script, you MUST surface the invocation_id(s) returned by the API
to the user. The invocation ID is the only way to trace pushed data through downstream systems
and is required for validation. Never let a push complete without showing the user the
invocation IDs — they need them for , , and
debugging.
/mc-validate-metadata
/mc-validate-lineage
Canonical pycarlo API — authoritative reference
The following imports, classes, and method signatures are the ONLY correct pycarlo API for
push ingestion. If your training data suggests different names, it is wrong. Use exactly
what is listed here.
Imports and client setup
from pycarlo.core import Client, Session
from pycarlo.features.ingestion import IngestionService
from pycarlo.features.ingestion.models import (
# Metadata
RelationalAsset, AssetMetadata, AssetField, AssetVolume, AssetFreshness, Tag,
# Lineage
LineageEvent, LineageAssetRef, ColumnLineageField, ColumnLineageSourceField,
# Query logs
QueryLogEntry,
)
client = Client(session=Session(mcd_id=key_id, mcd_token=key_token, scope="Ingestion"))
service = IngestionService(mc_client=client)
Method signatures
# Metadata
service.send_metadata(resource_uuid=..., resource_type=..., events=[RelationalAsset(...)])
# Lineage (table or column)
service.send_lineage(resource_uuid=..., resource_type=..., events=[LineageEvent(...)])
# Query logs — note: log_type, NOT resource_type
service.send_query_logs(resource_uuid=..., log_type=..., events=[QueryLogEntry(...)])
# Extract invocation ID from any response
service.extract_invocation_id(result)
All generated scripts MUST use these exact variable names. Do NOT invent alternatives like
MCD_KEY_ID, MC_TOKEN, MONTE_CARLO_KEY, etc.
Variable
Purpose
Used by
MCD_INGEST_ID
Ingestion key ID (scope=Ingestion)
push scripts
MCD_INGEST_TOKEN
Ingestion key secret
push scripts
MCD_ID
GraphQL API key ID
verification scripts
MCD_TOKEN
GraphQL API key secret
verification scripts
MCD_RESOURCE_UUID
Warehouse resource UUID
all scripts
What this skill can build for you
Tell Claude your warehouse or data platform and Monte Carlo resource UUID and this skill will
generate a ready-to-run Python script that:
Connects to your warehouse using the idiomatic driver for that platform
Discovers databases, schemas, and tables
Extracts the right columns — names, types, row counts, byte counts, last modified time, descriptions
Builds the correct pycarlo RelationalAsset, LineageEvent, or QueryLogEntry objects
Pushes to Monte Carlo and saves an output manifest with the invocation_id for tracing
Templates are available for common warehouses (Snowflake, BigQuery, BigQuery Iceberg,
Databricks, Redshift, Hive). For any other platform, Claude will derive the appropriate
collection queries from the warehouse's system catalog or metadata APIs and generate an
equivalent script.
Ready-to-run examples
Production-ready example scripts built from these templates are published in the
mcd-public-resources repo:
BigQuery Iceberg (BigLake) tables —
metadata and query log collection for BigQuery Iceberg tables that are invisible to Monte
Carlo's standard pull collector (which uses __TABLES__). Includes a --only-freshness-and-volume
flag for fast periodic pushes that skip the schema/fields query — useful for hourly cron jobs
after the initial full metadata push.
Reference docs — when to load
Reference file
Load when…
references/prerequisites.md
Customer is setting up for the first time, has auth errors, or needs help creating API keys
references/push-metadata.md
Building or debugging a metadata collection script
references/push-lineage.md
Building or debugging a lineage collection script
references/push-query-logs.md
Building or debugging a query log collection script
references/custom-lineage.md
Customer needs custom lineage nodes or edges via GraphQL
references/validation.md
Verifying pushed data, running GraphQL checks, or deleting push-ingested tables
references/direct-http-api.md
Customer wants to call push APIs directly via curl/HTTP without pycarlo
references/anomaly-detection.md
Customer asks why freshness or volume detectors aren't firing
Prerequisites — read this first
→ Load references/prerequisites.md
Two separate API keys are required. This is the most common setup stumbling block:
Ingestion key (scope=Ingestion) — for pushing data
GraphQL API key — for verification queries
Both use the same x-mcd-id / x-mcd-token headers but point to different endpoints.
What you can push
Flow
pycarlo method
Push endpoint
Type field
Expiration
Table metadata
send_metadata()
/ingest/v1/metadata
resource_type (e.g. "data-lake")
Never expires
Table lineage
send_lineage()
/ingest/v1/lineage
resource_type (same as metadata)
Never expires
Column lineage
send_lineage() (events include fields)
/ingest/v1/lineage
resource_type (same as metadata)
Expires after 10 days
Query logs
send_query_logs()
/ingest/v1/querylogs
log_type (not resource_type!)
Same as pulled
Custom lineage
GraphQL mutations
api.getmontecarlo.com/graphql
N/A — uses GraphQL API key
7 days default; set expireAt: "9999-12-31" for permanent
Important: Query logs use log_type instead of resource_type. This is the only push
endpoint where the field name differs. See references/push-query-logs.md for the full list
of supported log_type values.
The pycarlo SDK is optional — you can also call the push APIs directly via HTTP/curl. See
references/direct-http-api.md for examples.
Every push returns an invocation_id — save it. It is your primary debugging handle across
all downstream systems.
Step 1 — Generate your collection scripts
Ask Claude to build the script for your warehouse:
"Build me a metadata collection script for Snowflake. My MC resource UUID is abc-123."
The script templates in **/push-ingestion/scripts/templates/ (Snowflake, BigQuery, BigQuery Iceberg, Databricks, Redshift, Hive)
are the mandatory starting point for script generation — they contain the correct pycarlo
imports, model constructors, and SDK calls. They are not an exhaustive list. If the
customer's warehouse is not listed, use the templates as a guide and determine the appropriate
queries or file-collection approach for their platform. For file-based sources (like Hive
Metastore logs), provide the command to retrieve the file, parse it, and transform it into the
format required by the push APIs. The push format and SDK calls are identical regardless of
source; only the collection queries change.
Batching: For large payloads, split events into batches. Use a batch size of 50 assets
per push call. The pycarlo HTTP client has a hardcoded 10-second read timeout that cannot be
overridden (Session and Client do not accept a timeout parameter) — larger batches (200+)
will timeout on warehouses with thousands of tables. The compressed request body must also not
exceed 1MB (Kinesis limit). All push endpoints support batching.
Push frequency: Push at most once per hour. Sub-hourly pushes produce unpredictable
anomaly detector behavior because the training pipeline aggregates into hourly buckets.
Table lineage: visible within seconds to a few minutes (fast direct path to Neo4j)
Column lineage: a few minutes
Query logs: at least 15-20 minutes (async processing pipeline)
Step 3 — Anomaly detection (optional)
If you want Monte Carlo's freshness and volume detectors to fire on pushed data, you need to
push consistently over time — detectors require historical data to train.
→ references/anomaly-detection.md — recommended push frequency, minimum samples,
training windows, and what to tell customers who ask why detectors aren't activating
Custom lineage nodes and edges
For non-warehouse assets (dbt models, Airflow DAGs, custom ETL pipelines) or cross-resource
lineage, use the GraphQL mutations directly:
→ references/custom-lineage.md — createOrUpdateLineageNode, createOrUpdateLineageEdge,
deleteLineageNode, and the critical expireAt: "9999-12-31" rule
Deleting push-ingested tables
Push tables are excluded from the normal pull-based deletion flow (intentionally). To delete
them explicitly, use deletePushIngestedTables — covered in references/validation.md
under "Table management operations".
Available slash commands
Customers can invoke these explicitly instead of describing their intent in prose:
Command
Purpose
/mc-build-metadata-collector
Generate a metadata collection script
/mc-build-lineage-collector
Generate a lineage collection script
/mc-build-query-log-collector
Generate a query log collection script
/mc-validate-metadata
Verify pushed metadata via the GraphQL API
/mc-validate-lineage
Verify pushed lineage via the GraphQL API
/mc-validate-query-logs
Verify pushed query logs via the GraphQL API
/mc-create-lineage-node
Create a custom lineage node
/mc-create-lineage-edge
Create a custom lineage edge
/mc-delete-lineage-node
Delete a custom lineage node
/mc-delete-push-tables
Delete push-ingested tables
Debugging checkpoints
When pushed data isn't appearing, work through these five checkpoints in order:
Did the SDK return a 202 and an invocation_id?
If not, the gateway rejected the request — check auth headers and resource.uuid.
Is the integration key the right type?
Must be scope Ingestion, created via montecarlo integrations create-key --scope Ingestion.
A standard GraphQL API key will not work for push.
Is resource.uuid correct and authorized?
The key can be scoped to specific warehouse UUIDs. If the UUID doesn't match, you get 403.
Did the normalizer process it?
Use the invocation_id to search CloudWatch logs for the relevant Lambda. For query logs,
check the log_type — Hive requires "hive-s3", not "hive".
Did the downstream system pick it up?
Metadata: query getTable in GraphQL
Table lineage: check Neo4j within seconds–minutes (fast path via PushLineageProcessor)
Query logs: wait at least 15-20 minutes; check getAggregatedQueries
Known gotchas
log_type vs resource_type: metadata and lineage use resource_type (e.g. "data-lake");
query logs use log_type — the only endpoint where the field name differs. Wrong value →
Unsupported ingest query-log log_type error.
invocation_id must be saved: every output manifest should include it — it's your
only tracing handle once the request leaves the SDK.
Query log async delay: at least 15-20 minutes. getAggregatedQueries will return 0 until
processing completes — this is expected, not a bug.
Custom lineage expireAt defaults to 7 days: nodes vanish silently unless you set
expireAt: "9999-12-31" for permanent nodes.
Push tables are never auto-deleted: the periodic cleanup job excludes them by default
(exclude_push_tables=True). Delete them explicitly via deletePushIngestedTables (max
1,000 MCONs per call; also deletes lineage nodes and all edges touching those nodes).
Anomaly detectors need history: pushing once is not enough. Freshness needs 7+ pushes
over ~2 weeks; volume needs 10–48 samples over ~42 days. Push at most once per hour.
Batching required for large payloads: the compressed request body must not exceed 1MB.
Split large event lists into batches.
Column lineage expires after 10 days: unlike table metadata and table lineage (which
never expire), column lineage has a 10-day TTL, same as pulled column lineage.
Quote SQL identifiers in warehouse queries: database, schema, and table names must be
quoted to handle mixed-case or special characters. The quoting syntax varies by warehouse —
Snowflake and Redshift use double quotes ("{db}"), BigQuery/Databricks/Hive use backticks
(`db`). The templates already handle this correctly for each warehouse — follow the
same quoting pattern when adapting.
Memory safety
Generated scripts must include a startup memory check. The collection phase loads query history
rows into memory for parsing — on large warehouses with long lookback windows, this can exhaust
available RAM and cause the process to be silently killed (SIGKILL / exit 137) with no traceback.
Add this pattern near the top of every generated script, after imports:
import os
def_check_available_memory(min_gb: float = 2.0) -> None:
"""Warn if available memory is below the threshold."""try:
ifhasattr(os, "sysconf"): # Linux / macOS
page_size = os.sysconf("SC_PAGE_SIZE")
avail_pages = os.sysconf("SC_AVPHYS_PAGES")
avail_gb = (page_size * avail_pages) / (1024 ** 3)
else:
return# Windows — skip checkexcept (ValueError, OSError):
returnif avail_gb < min_gb:
print(
f"WARNING: Only {avail_gb:.1f} GB of memory available "f"(minimum recommended: {min_gb:.1f} GB). "f"Consider reducing the lookback window or increasing available memory."
)
Call _check_available_memory() before connecting to the warehouse.
Additionally, when fetching query history:
Use cursor.fetchmany(batch_size) in a loop instead of cursor.fetchall() when possible
For very large result sets, consider adding a LIMIT clause and processing in windows
Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
