dqx-end-to-end

name

dqx-end-to-end

description

Run DQX validation end-to-end — read an input table or path, apply checks, and write valid and quarantined rows to output locations — in a single call. Use when the user asks for "apply and save", "quality-check a table and split the output", "DQX on a whole table", "save valid and invalid rows", or wants to drop DQX into a Lakeflow / workflow that runs on a table or path. Covers apply_checks_and_save_in_table, the by_metadata variant, InputConfig / OutputConfig, and incremental streaming mode.

DQX — End-to-end apply + save

One method call: read, check, write valid rows, (optionally) write quarantined rows.

from databricks.labs.dqx import check_funcs
from databricks.labs.dqx.engine import DQEngine
from databricks.labs.dqx.rule import DQRowRule, DQDatasetRule
from databricks.labs.dqx.config import InputConfig, OutputConfig
from databricks.sdk import WorkspaceClient
# `spark` is available in Databricks notebooks / jobs. Locally, create it with
# `from pyspark.sql import SparkSession; spark = SparkSession.builder.getOrCreate()`.

dq = DQEngine(WorkspaceClient())

checks = [
    DQRowRule(criticality="warn",  check_func=check_funcs.is_not_null,   column="col3"),
    DQDatasetRule(criticality="error", check_func=check_funcs.is_unique, columns=["col1", "col2"]),
]

# Split into valid + quarantine tables
dq.apply_checks_and_save_in_table(
    checks=checks,
    input_config=InputConfig(location="catalog.schema.input"),
    output_config=OutputConfig(location="catalog.schema.valid"),
    quarantine_config=OutputConfig(location="catalog.schema.quarantine"),
)

# Or — keep everything in a single annotated output table
dq.apply_checks_and_save_in_table(
    checks=checks,
    input_config=InputConfig(location="catalog.schema.input"),
    output_config=OutputConfig(location="catalog.schema.annotated"),
)

Metadata form (checks loaded from storage)

dq.apply_checks_by_metadata_and_save_in_table(
    checks=checks_metadata,      # list[dict] — see dqx-define-checks
    input_config=InputConfig(location="catalog.schema.input"),
    output_config=OutputConfig(location="catalog.schema.valid"),
    quarantine_config=OutputConfig(location="catalog.schema.quarantine"),
)

If checks is omitted and checks_location is set on a RunConfig (workspace install flow), checks are loaded automatically from that storage — see dqx-storage.

InputConfig / OutputConfig — common options

• location — Unity Catalog table (catalog.schema.table) or Unity Catalog Volume path (/Volumes/catalog/schema/volume/...).
• format — default delta; set when writing to a volume as parquet/json/csv.
• options — dict passed through to reader/writer (e.g. {"mergeSchema": "true"}).
• mode — "append" (default for batch) or "overwrite".
• Streaming — set input_config.is_streaming=True to opt into incremental reads. Provide options={"checkpointLocation": "/Volumes/.../checkpoints/xxx"} on the output config. DQX's batch-style incremental reader uses an AvailableNow-style trigger by default; set a different trigger via the output config if you need continuous streaming. Full details: Applying Quality Checks.

Multi-table / pattern execution

• apply_checks_and_save_in_tables(run_configs) — accept a list of RunConfig and fan out over each (input, output, quarantine) triple.
• apply_checks_and_save_in_tables_for_patterns(patterns, checks_location, run_config_template=...) — expand wildcard patterns (Python list[str], e.g. ["main.product001.*", "main.product002"]) against Unity Catalog and reuse one RunConfig template for every matched table. Output / quarantine names are derived from the input name + output_table_suffix / quarantine_table_suffix (defaults _dq_output / _dq_quarantine). The semicolon-delimited form is a convention of the databricks labs dqx CLI, not the Python API.

Do / Don't

• Do set a unique checkpointLocation per output when using streaming — DQX uses it to track watermarks; sharing one across pipelines causes silent data loss.
• Do point quarantine_config at a separate table. Without it, failed rows get the _errors column but remain in the single output table.
• Do use the same method signature for batch and streaming — the difference is one is_streaming flag on InputConfig.
• Don't manually pre-filter _errors.isNull() before writing — DQX already routes error-criticality rows to quarantine_config when you provide it.
• Don't mix checks= and checks_location in a RunConfig — pick one source of truth.

Canonical docs:

• End-to-end apply patterns: https://databrickslabs.github.io/dqx/docs/guide/quality_checks_apply#applying-checks-defined-with-dqx-classes
• Config dataclasses: https://databrickslabs.github.io/dqx/docs/reference/engine
• Demos: https://databrickslabs.github.io/dqx/docs/demos