finding-data-lake-assets

Find Data Lake Assets

Overview

Resolves data lake asset references to concrete catalog entries. Acts as a resolver for other skills and direct user requests. Covers Glue, S3, S3 Tables, and Redshift. Optimized for low token usage — return the answer fast and get out of the way.

Constraints for parameter acquisition:

• You MUST accept a single argument: table name, keyword, column name, or S3 path
• You MUST accept the argument as direct input or a pointer to a file containing the spec
• You MUST ask for the target AWS region if not already set
• You MUST confirm ambiguous input before searching (e.g., "Did you mean table X or bucket Y?")
• You MUST respect the user's decision to abort at any step

Common Tasks

You MUST execute commands using AWS MCP server tools when connected — they provide validation, sandboxed execution, and audit logging. Fall back to AWS CLI only if MCP is unavailable. You MUST explain each step before executing.

1. Verify Dependencies

Check for required tools and AWS access before searching.

Constraints:

• You MUST verify AWS MCP server tools (aws___call_aws) are available; fall back to AWS CLI if not
• You MUST confirm credentials with aws sts get-caller-identity
• You MUST inform the user about any missing tools and ask whether to proceed

2. Classify the Request

Determine the mode:

• Resolve (most common): User/skill references something specific.

Signals: possessive/definite articles ("our X table", "the Y dataset") imply the asset exists. Goal: find it, return the reference, done.

• Search: User is exploring. Signals: "find tables with", "what

has customer_id". Goal: rank candidates, present top matches.

You SHOULD default to Resolve mode when ambiguous.

3. Extract Search Terms

Parse the request into search dimensions:

• Name terms: Table or database names mentioned
• Domain terms: Business concepts (billing, orders, churn)
• Column terms: Specific column names (customer_id, event_type)
• Location terms: S3 paths, bucket names, prefixes

4. Layered Search (stop early)

Search sources in order. Stop at the first layer that returns a high-confidence match. Do NOT search all layers every time.

You MUST track which layers were searched and which were skipped. Report this in the output (see Step 6).

Layer 1: Glue Data Catalog (always start here)

You SHOULD use SearchTables as the primary API — it searches table names, column names, and column comments across the entire catalog in one call. You MUST NOT loop over databases with get-tables unless you already know the database name. See search-strategy.md for patterns.

aws glue search-tables --search-text "orders"
aws glue get-tables --database-name sales --expression "order.*"

Layer 2: S3 Reverse Lookup (S3 path provided)

When a user provides an S3 path, you SHOULD default to reverse lookup first — they usually want the Glue table, not the file contents.

aws glue search-tables --search-text "<path-keyword>"
aws s3api list-objects-v2 --bucket <bucket-name> --prefix <prefix>

Layer 3: Redshift Catalog (if user mentions Redshift, warehouse, or lakehouse)

SELECT schema_name, table_name, table_type
FROM svv_all_tables
WHERE table_name ILIKE '%orders%';

Redshift Spectrum external tables also appear in Glue. If Layer 1 found the table with a Spectrum SerDe, skip Layer 3.

4b. Broad Scan Fallback (single turn)

When search-tables returns nothing and S3 Tables enumeration also misses, you MAY need to scan across databases. Do NOT issue separate CLI calls per database — that burns turns and tokens. Instead, write a short Python script using boto3 paginators that does the full scan in one execution. Write the script to a file and run it with python3.

The script MUST:

• Paginate get_databases() to collect all database names
• For each database, paginate get_tables() with an Expression

filter matching the search term

• Print only matching results as structured output (JSON or table)
• Accept the region and search term as arguments or variables

import boto3, sys, json

region = sys.argv[1]
term = sys.argv[2]

glue = boto3.client("glue", region_name=region)
matches = []

db_paginator = glue.get_paginator("get_databases")
for db_page in db_paginator.paginate():
    for db in db_page["DatabaseList"]:
        db_name = db["Name"]
        tbl_paginator = glue.get_paginator("get_tables")
        for tbl_page in tbl_paginator.paginate(
            DatabaseName=db_name, Expression=f".*{term}.*"
        ):
            for tbl in tbl_page["TableList"]:
                matches.append({
                    "database": db_name,
                    "table": tbl["Name"],
                    "format": tbl.get("Parameters", {}).get("classification", "unknown"),
                    "location": tbl.get("StorageDescriptor", {}).get("Location", ""),
                })

print(json.dumps(matches, indent=2) if matches else "No matches found.")

You MUST only use this fallback after search-tables and S3 Tables enumeration have already returned nothing. This is a last resort, not a first choice.

5. Apply the Confidence Gate

• High confidence (exact name match, single result): Return the resolved

reference immediately. No summary, no options.

• Medium confidence (fuzzy match, 2-3 results): Present top matches with

one line each: name, why it matched, format. Let the user pick.

• Low confidence (many weak matches or none): Report what was searched

and what was skipped, suggest refining the query or running exploring-data-catalog.

6. Return the Reference

For high-confidence resolve, return a structured reference. Always include a "Sources searched / skipped" line so the user knows which data stores were checked and which were not.

Table: database_name.table_name
Catalog: default | catalog_name
Format: Parquet | CSV | JSON | ORC | Iceberg
Location: s3://bucket/prefix/
Partition keys: [key1, key2] or none
Sources searched: Glue Data Catalog
Sources skipped: S3, Redshift (stopped early — high-confidence match in Glue)

S3 Tables use a 4-level hierarchy (catalog / table-bucket / namespace / table), and search-tables does not index s3tablescatalog/*. If the user mentions S3 Tables explicitly or Layer 1 returns nothing for an expected S3 Tables asset, enumerate via aws s3tables list-table-buckets and list-namespaces. Return as:

Table: s3tablescatalog/<table-bucket>/<namespace>/<table>
Format: Iceberg
Location: arn:aws:s3tables:<region>:<account>:bucket/<table-bucket>/table/<table-uuid>
Sources searched: Glue Data Catalog, S3 Tables
Sources skipped: Redshift (not relevant to S3 Tables lookup)

SQL reference: "s3tablescatalog/<table-bucket>"."<namespace>"."<table>".

You MUST always include both "Sources searched" and "Sources skipped" in the output. List the reason for skipping in parentheses. Valid reasons: "stopped early", "not relevant to this request", "access denied", "no results in prior layer".

Troubleshooting

Principles

• You MUST prefer search-tables over iterating databases. One API call beats N.
• You MUST pass an Expression filter when calling get-tables; never call it without one.
• You MUST NOT issue separate CLI calls per database. If a broad scan is needed, use the boto3 paginator script from Step 4b to do it in a single turn.
• You SHOULD resolve fast and stop early. Every extra API call costs tokens.
• You SHOULD assume the asset exists in Resolve mode — search to find it, not to confirm it.

Additional Resources

• Search strategy details
• AWS Glue SearchTables API
• S3 Tables overview
• S3 Metadata tables