Context Aware SDK

Discover the right data quality rules. Enforce them in
every pipeline.

DataBuck analyzes your DataFrame together with metadata, data contracts, policies, and enterprise context to recommend precise, reviewable data quality rules. Export the approved rules to Lakeflow Expectations or validate directly in Python—without manually authoring hundreds of checks.

View Databricks workflow Request Enterprise Access

The APIs shown illustrate the planned developer workflow and may change during the preview.

orders_expectations.py

# Discover context-aware rules and export to Lakeflow.
rules = databuck.discover(
    df,
    context=["customer_policy.pdf", "data_contract.md"]
)

expectations = rules.to_lakeflow()
expectations.save("orders_expectations.json")

# Then use Lakeflow expectations.
@dp.expect_all(expectations)
def validated_orders():
    return spark.read.table("catalog.sales.orders")

# Alternatively, use DataBuck's own validator.
report = databuck.validate(df, rules)
report.assert_passed()

Free Developer SDK
Local rules
DataFrame-native workflow
Designed for portability

THE REVIEW LOOP

From DataFrame to approved rules in minutes

Let DataBuck recommend candidate rules from your data. Review the recommendations, save the approved rules in Git, and enforce them with DataBuck or your platform’s native validation engine.

Discover candidate rules

Analyze patterns, distributions, dependencies, cross-column relationships, and anomalous behavior.

rules = databuck.discover(df)

Review and version

Review each recommendation, its confidence, and the evidence behind it. Approve the rules your pipeline should enforce.

rules.review()
rules.save("orders_rules.yaml")

Enforce at the pipeline boundary

Validate with DataBuck or export approved rules to the framework already running your pipelines.

report = databuck.validate(df, rules)
report.assert_passed()

quality_gate.py

rules = databuck.discover(df)


rules.review()
rules.save("orders_rules.yaml")


report = databuck.validate(df, rules)
report.assert_passed()

PIPELINE QUALITY GATE

Turn every critical DataFrame into a controlled quality gate

01 / Input

DataFrame + approved rule set

A bounded dataset and rules your team has reviewed.

02 / Validate

Execute rules at the pipeline boundary

Run before the next material step in your pipeline.

03 / Decide

Pass, quarantine, warn, retry, or stop

Apply the response your system and data product require.

04 / Observe

Record failures, evidence, and rule-level results

Make every outcome useful to engineering and operations.

WHY DATABUCK

More than profiling. DataBuck discovers rules that reflect how your data should behave.

Automated rule
discovery

Generate candidate checks from the structure, content, history, and relationships within your data.

Context-aware recommendations

Improve rule relevance using table descriptions, metadata, policies, data contracts, and business documentation.

Cross-column
intelligence

Discover conditional and relationship-based rules that single-column profiling misses.

Developer-controlled enforcement

Review, edit, approve, version, and deploy rules through the same engineering workflow used for pipeline code.

Portable execution

Run approved rules with DataBuck or export them to supported engines such as Lakeflow Expectations.

ENTERPRISE KNOWLEDGE RETRIEVAL

Discover rules from both the data and the business context

Data alone can reveal patterns, but it cannot always explain business intent. DataBuck combines observed DataFrame behavior with enterprise knowledge—including data contracts, catalog metadata, policies, table descriptions, and business documents—to recommend rules that are technically valid and operationally meaningful.

Request Enterprise Access

DataFrame behavior

DataBuck enterprise intelligence

Historical distributions

Table and column metadata

Data contracts

Business policies

Enterprise documents

Reference datasets

✦

DataBuck
intelligence

rule recommendations

DATABRICKS WORKFLOW

DataBuck discovers the rules. Databricks enforces them.

DataBuck analyzes Spark DataFrames, Unity Catalog metadata, data contracts, and enterprise policies to recommend data quality rules. Engineers review the recommendations and export approved rules as Lakeflow Expectations. Databricks remains the execution and orchestration platform.

Spark DataFrame

catalog.sales.orders

DataBuck

discover +
review

Lakeflow

native execution

Approved expectations move into your Databricks workflow as a visible, versioned artifact.

Reduce manual expectation authoring

Improve cross-column and conditional coverage

Standardize rules across data products

Store approved expectations in Git

Run validation using Databricks-native execution

GCP DATA + AI

One rule-discovery workflow across GCP data and AI pipelines

Use DataBuck to discover and review rules for BigQuery and Spark DataFrames, then enforce them at ingestion, transformation, model-training, or agent-consumption boundaries. Incorporate catalog metadata, contracts, policies, and business context without introducing another data movement layer.

DESIGNED FOR PORTABILITY

Your rules remain visible, versioned, and portable

DataBuck rules are explicit artifacts—not hidden model behavior. Review them, store them in Git, compare changes through pull requests, and export them to supported validation frameworks. Your team controls which rules are approved and where they run.

DataBuck rule YAML
Lakeflow Expectations
Python validation
SQL expressions
JSON/API representation

FRAMEWORK INTEGRATION PATTERNS

Validate data before an AI agent acts on it

Place a DataBuck quality gate between enterprise data retrieval and agent execution. Based on the validation result, your application can continue, retry the query, request human review, quarantine the result, or stop the workflow.

Retrieve enterprise data

DataBuck quality gate

Agent execution

Continue Retry Review Quarantine Stop

PREVIEW EDITION SCOPE · TWO EDITIONS, ONE ENGINEERING WORKFLOW

Start in code. Add organizational context when you need it.

FREE

DataBuck Developer SDK

Discover and validate data quality rules directly in Python. Keep rules and results in your development environment, review recommendations, store approved rules in Git, and export them to supported validation engines.

Local rule discovery
Local DataFrame validation
Rule review and editing
YAML/JSON rule storage
Git-friendly versioning
Supported native-framework exports

ENTERPRISE

DataBuck Enterprise SDK

Add enterprise context, centralized rule management, governed APIs, organizational standards, and DataBuck intelligence to the same developer workflow.

Enterprise documents and policies
Data contracts and catalog metadata
Central rule repository
DataBuck API integration
DataBuck specialized models
Approval and governance workflows
Centralized results and observability
Enterprise deployment and support