databricks-etl-monitor 0.1.0a22

Metadata-driven ETL Process Monitoring Framework for Databricks / Delta Lake

Databricks ETL Monitor Framework

A metadata-driven ETL process monitoring framework for Databricks / Delta Lake.

Tracks ADF pipelines, Databricks notebooks, Databricks jobs, and Dataflows in a single Unity Catalog schema — without modifying how those jobs are triggered.

Ported from a mature SQL Server / ADF ETL orchestration framework. The monitoring and tracking patterns have been reimplemented as Unity Catalog Delta tables and a self-creating Python class, making them available to pure Databricks teams without a SQL Server dependency.

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Why this framework?

Most existing tools cover one platform or the other:

Tool	What it covers	Gap
ADF monitoring (Azure Portal)	ADF pipeline runs	No DBX notebooks, no task-level app log
Databricks job UI / API	DBX jobs only	No ADF pipelines, no cross-job correlation
Azure Monitor / Log Analytics	Raw platform logs	Not process-aware — no task catalogue, no watermarks
Apache Atlas / Unity Catalog lineage	Data lineage	Not execution-status oriented
Great Expectations / Soda	Data quality	Not ETL step tracking
Custom Delta audit tables	Project-specific	No standard schema, no Python SDK, no ADF bridge

What this framework adds:

1. Single Delta schema tracks ADF pipelines, DBX notebooks, DBX jobs, and Dataflows in one place.
2. Task catalogue (ETLconfigTasks) with sequence/workflow ordering — not just raw run receipts.
3. Delta watermark registry (ETLconfigParameters) with typed parameters and ADF Lookup bridge.
4. Per-process scoping (ProjectCode / ProcessLoad) — HR_DAILY and FIN_MONTHLY tracked independently.
5. Snapshot columns in execution log — history stays accurate even when the task catalogue changes.
6. Self-creating Python class (setup()) — zero manual DDL for Databricks teams.
7. JDBC-queryable by SQL Server, ADF, and any ODBC tool — no Python SDK needed for consumption.

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Quick Start

%pip install databricks-etl-monitor --upgrade --no-deps

from etl_monitor import ETLMonitorFramework

monitor = ETLMonitorFramework(spark, catalog="main", schema="etl")
monitor.setup()   # idempotent — creates 6 tables, 6 views, seeds sequence stages

# Register a domain process (once)
monitor.register_process("CORP", "HR_DAILY", name="HR Daily Load", owner="HR Team", load_frequency="D")

# Register tasks (once)
monitor.register_task("CORP", "HR_DAILY", task_id=0, workflow_id=0, sequence_id=0,
                      task_name="Initiation", source_type="DBX_NOTEBOOK")
monitor.register_task("CORP", "HR_DAILY", task_id=1, workflow_id=1, sequence_id=2,
                      task_name="Load Employees", source_type="DBX_NOTEBOOK",
                      source_system_code="LoadEmployees", task_mandatory=True)

# Register watermarks (once)
monitor.register_parameter("CORP", "HR_DAILY", "SYSDT", "SYSTEM")
monitor.register_parameter("CORP", "HR_DAILY", "LoadEmployees", "DELTA_DATE",
                           description="Last loaded employee timestamp")

# Each run
exec_id = ETLMonitorFramework.generate_execution_id()
monitor.generate_execution_steps(exec_id, "CORP", "HR_DAILY", "2026-04-09")

with monitor.task(exec_id, "CORP", "HR_DAILY",
                  task_id=1, workflow_id=1, sequence_id=2,
                  processing_date="2026-04-09"):
    pass   # your notebook logic here

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Sample Notebooks

After installing, extract the sample notebooks to your workspace:

SAMPLE_USAGE_PATH = monitor.sample_usage(spark)

This copies four notebooks to /Workspace/Users/{you}/databricks-etl-monitor/sample_usage/:

Notebook	Purpose
00-infrastructure.py	Create catalog and ETL schema (run once per environment)
01-install.py	Install framework, call setup(), extract samples
02-config.py	Register a process, tasks, and watermark parameters
03-run.py	Full execution run with status queries and retry demo

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Architecture

┌──────────────────────────────────────────────────────────────────────┐
│           MONITORING VIEWS  (catalog.etl schema)                      │
│  v_processStatus   — cross-process live dashboard                     │
│  v_runSummary      — per execution/attempt rollup                     │
│  v_taskDetail      — task-level with SLA breach flag                  │
│  v_mandatoryBlockers — tasks blocking downstream progress             │
│  v_currentFailures — latest-attempt failures                          │
│  v_watermarks      — watermark state + ActiveValue (ADF Lookup)       │
└──────────────────────┬───────────────────────────────────────────────┘
                        │ reads from
┌──────────────────────▼───────────────────────────────────────────────┐
│           TRACKING TABLES  (catalog.etl schema)                       │
│  ETLconfigSequence   [FRAMEWORK-MANAGED — 7 built-in stages]          │
│  ETLconfigProcess    [USER-MANAGED — domain process registry]         │
│  ETLconfigTasks      [USER-MANAGED — task catalogue]                  │
│  ETLconfigParameters [USER-MANAGED — watermarks + config flags]       │
│  ETLProcessingSteps  [RESULTS — per-task live log, mutable]           │
│  ETLsysLogs          [RESULTS — raw run receipts, append-only]        │
└──────────────────────┬───────────────────────────────────────────────┘
                        │ Python SDK / Spark SQL / JDBC
┌──────────────────────────────────────────────────────────────────────┐
│           CONSUMERS                                                    │
│  DBX Notebooks  → monitor.task() context manager                      │
│  DBX Jobs       → start_task() / end_task() / fail_task()            │
│  ADF Pipelines  → Databricks Notebook activity (utility notebooks)    │
│                   + Lookup activity reads v_watermarks.ActiveValue    │
│  SQL Server     → JDBC linked server reads Delta views                │
│  Dataflows      → post-activity webhook or notebook shim              │
└──────────────────────────────────────────────────────────────────────┘

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Tables Created by setup()

Table	Managed by	Purpose
ETLconfigSequence	Framework	7 workflow stage definitions (auto-seeded)
ETLconfigProcess	User	Domain process registry — one row per domain load
ETLconfigTasks	User	Task catalogue — what runs, in what order, how often
ETLconfigParameters	User	Delta watermarks and config flags per process
ETLProcessingSteps	Results	Per-task live execution log (mutable, partitioned by date)
ETLsysLogs	Results	Raw ADF/DBX run receipts (append-only, IDENTITY PK)

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Workflow Stage Definitions (ETLconfigSequence)

Seeded automatically by setup(). All tasks sharing a SequenceID run in parallel.

SequenceID	SequenceCode	Description
0	LOAD_GO	Initiating ETL Processing — overall run marker
1	LOAD_DB_CONFIG	Load Configuration Data from source
2	LOAD_DB_TRAN	Load Transactional Data from source (staging)
3	LOAD_DIM	Process Master Data — validate staged dimensions
4	LOAD_TRAN	Process Transactional Data — validate staged transactions
5	PRE_PROCESS	Functional Logic — business logic and derivations
6	PROCESS_DATA	Core Data Transformation — output / data mart tables

Custom stages: use SequenceID >= 10 to avoid collision with framework rows.

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WorkFlowID Semantics

WorkFlowID	Meaning
0	Initiation task — always TaskID=0, SequenceID=0; one per process; overall run status indicator
1	First workflow pass (main load)
2	Second pass (enrichment / additional fields / second data iteration)
N	Nth iteration over the same data with a different scope

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Reporting Views

View	Purpose
v_processStatus	Cross-process live dashboard by processing date
v_runSummary	Run-level rollup with task counts per status
v_taskDetail	Per-task detail with SLA breach flag, filtered by ExecutionID
v_mandatoryBlockers	Mandatory failed tasks preventing downstream progress
v_currentFailures	All failed tasks for current / specified date
v_watermarks	Current watermark values with resolved ActiveValue (ADF Lookup bridge)

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Status Values

NQUE  (New Queue)   — task created, first attempt, awaiting execution
RQUE  (Re-Queue)    — reset from FAIL, retry attempt queued
DONE                — completed successfully
FAIL                — failed — awaiting retry or investigation

State machine:

NQUE → DONE
NQUE → FAIL → RQUE → DONE
NQUE → FAIL → RQUE → FAIL → [manual status_reset()] → RQUE → DONE

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ParameterType Values

Type	Active column	Auto-advance on DONE?	Bulk mode
DELTA_DATE	ValueDateTime	Yes — set to task StartTime	ValueDateTime = NULL
DELTA_ID	ValueINT	No — call advance_watermark()	ValueINT = 0
FLAG	ValueBIT	No — read freely	Not applicable
SYSTEM	ValueDateTime	No — set_processing_mode() only	NULL = live date

KNOWN LIMITATION — DELTA_ID: For ID-based watermarks the framework cannot auto-detect the max ID from the source dataset. Developer must call advance_watermark() explicitly after their load logic completes:

max_id = df.agg({"EmployeeID": "max"}).collect()[0][0]
monitor.advance_watermark("CORP", "HR_DAILY", "LoadEmployeesByID", new_int_value=max_id)

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ADF Integration

Watermark lookup

ADF Lookup activity reads ActiveValue — a resolved STRING regardless of ParameterType:

{
  "type": "Lookup",
  "name": "GetWatermark",
  "source": {
    "query": "SELECT ActiveValue FROM `<catalog>`.`etl`.`v_watermarks` WHERE ProjectCode='CORP' AND ProcessLoad='HR_DAILY' AND ParameterName='LoadEmployees'"
  }
}

ADF Copy Activity source query expression:

@concat('SELECT * FROM dbo.Employees WHERE ModifiedDate > ''',
        activity('GetWatermark').output.firstRow.ActiveValue, '''')

Write-back via utility notebooks

ADF calls a Databricks Notebook activity passing widget parameters. Three lightweight utility notebooks are created per project (not shipped with this package):

• etl_start_task.py — receives execution_id, project_code, process_load, task_id, workflow_id, sequence_id, processing_date, source_type as widgets → calls monitor.start_task(...).
• etl_end_task.py — same widgets + log_message, log_type → calls monitor.end_task(...).
• etl_fail_task.py — same widgets + error details → calls monitor.fail_task(...).

ForEach over pending tasks

ADF ForEach iterates get_pending_tasks() output. Tasks sharing the same SequenceID are dispatched in parallel (ADF parallel ForEach). After each SequenceID stage completes, ADF checks v_mandatoryBlockers before advancing.

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Stored Procedure Equivalence

For teams migrating from the SQL Server / ADF version:

Original stored procedure	Python method	Notes
p_ETLProcessingSteps (GenerateMode=1)	generate_execution_steps()	INSERT NQUE rows for all active tasks
p_ETLOrchestrationSteps	get_pending_tasks()	Returns non-DONE tasks; auto-generates on first call
p_ETLProcessingStatusUpdate	end_task() / fail_task()	Status + timing write-back; DELTA_DATE auto-advance on DONE
p_ETLProcessingStatusGet	get_status()	Summary or task-level detail; summary_mode=True for rollup
p_ETLProcessingStatusReset	status_reset()	Bulk or specific task reset; always resets initiation row
p_ETLconfigProcessingMode	set_processing_mode()	Historic mode, live mode, bulk mode, specific param

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Processing Mode

monitor.set_processing_mode("CORP", "HR_DAILY", is_bulk_mode=True)          # full reload
monitor.set_processing_mode("CORP", "HR_DAILY", is_historic_mode=True,
                             processing_date="2026-01-01")                   # historic rerun
monitor.set_processing_mode("CORP", "HR_DAILY")                             # restore live mode

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Status and Retry

# Check status
monitor.get_status("CORP", "HR_DAILY", execution_id=exec_id)           # task detail
monitor.get_status("CORP", "HR_DAILY", summary_mode=True)              # run rollup

# Reset failures for retry
monitor.status_reset("CORP", "HR_DAILY", execution_id=exec_id)         # all failures in run
monitor.status_reset("CORP", "HR_DAILY", execution_id=exec_id,
                     task_id=1, workflow_id=1)                          # specific task only

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Recommended Implementation Order

1. Deploy — run monitor.setup() in a Databricks notebook (idempotent, creates everything).
2. Register processes — register_process() for each domain (HR_DAILY, FIN_MONTHLY, etc.).
3. Register tasks — register_task() with TaskID, WorkFlowID, SequenceID per task.
4. Register parameters — register_parameter() with ParameterType for each watermark.
5. Instrument — add monitor.task() context manager to 2–3 pilot notebooks.
6. Verify — query v_taskDetail and v_runSummary after a pilot run.
7. Dashboard — Lakeview dashboard reading from the 6 monitoring views.
8. ADF integration — utility notebooks for ADF write-back; Lookup activity for watermarks.

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What Was NOT Ported

Original component	Reason not ported
Trigger / orchestration logic	Out of scope — this framework observes only, never triggers
#DELTAPARAMETER# string substitution	Replaced by v_watermarks.ActiveValue
ADFMain / ADFPipelines / ADFMetaData	ADF pipeline driver config — not needed for monitoring
ETLconfigNotifications	Replace with Databricks SQL Alerts or Lakeview dashboards
T-SQL stored procedures	Replaced entirely by Python class methods

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License

MIT