Reliable data is measured, monitored and explainable — not simply delivered.
Complete 12 of 24 practices (50%) and enter your name to unlock the Certificate of Participation.
Data quality asks whether data is fit for its intended use. Good quality is multidimensional: accuracy, completeness, validity, consistency, uniqueness and timeliness can all matter differently by dataset.
Quality is contextual • Critical data elements deserve stronger controls • A passing pipeline does not guarantee trustworthy output
Dimensions
accuracy
completeness
validity
consistency
timelinessStart with the business consequence of bad data; that tells you which quality dimensions deserve the strongest checks.
Validation turns expectations into executable checks. Schema rules, nullability, ranges, uniqueness, referential integrity and business rules can stop or quarantine bad data before it spreads.
Test structure and meaning • Fail fast on critical violations • Quarantine when the data can be investigated safely
assert order_id IS NOT NULL
assert amount >= 0
assert customer_id EXISTS
assert order_id IS UNIQUEA useful test has a clear rule, a clear severity and a clear action when it fails.
Freshness measures whether data arrived when users expected it. A dataset can be perfectly accurate and still be unusable if yesterday’s numbers arrive after today’s decision.
Freshness is a user expectation • Measure source and pipeline delay • Late data can be a quality incident
expected_by = 06:00
arrived_at = 06:18
lag_minutes = 18
status = LATEFreshness should be measured against the business deadline, not just against the previous pipeline run.
Observability begins with useful signals. Runtime, row counts, throughput, error rates, retries, queue depth and resource use reveal whether a pipeline is behaving normally or drifting toward failure.
Metrics reveal behavior • Trends matter more than isolated numbers • Monitor both system health and data health
duration_sec: 412
rows_loaded: 1,240,851
errors: 0
retries: 1
freshness_min: 6The best monitoring combines technical signals with data signals, because either side can fail while the other looks healthy.
Alerts should call attention to conditions that require action. Good alerts are specific, prioritized, routed to the right owner and rich enough with context to reduce investigation time.
Alert on impact, not noise • Include owner and context • Severity should match business consequence
SEV2: CustomerOrders late
Owner: Data Platform
Expected: 06:00
Observed: 06:27
Impact: 08:00 dashboard at riskAn alert that does not help someone decide what to do next is usually just noise.
Lineage shows where data came from, how it changed and what consumes it. During incidents, lineage narrows the search: a bad source field can be traced through transformations to affected tables, models and dashboards.
Know upstream and downstream dependencies • Trace transformations • Use lineage to estimate blast radius
CRM.customer_id
↓
stg_customer
↓
dim_customer
↓
Sales Model
↓
Executive DashboardLineage turns “something is wrong” into a bounded investigation with known upstream causes and downstream impact.
Reliability becomes manageable when expectations are measurable. Service Level Indicators (SLIs) measure behavior, while Service Level Objectives (SLOs) define the target levels users can rely on.
SLI = what you measure • SLO = target you promise internally • Reliability balances quality, freshness and availability
SLI: % daily loads by 06:00
SLO: >= 99% each month
SLI: valid customer IDs
SLO: >= 99.95%An SLO should express a reliability promise that connects engineering performance with user expectations.
Observability pays off when teams can respond quickly and learn from failures. A mature process detects, contains, communicates, restores, analyzes root cause and turns lessons into preventive improvements.
Detect early • Contain impact • Restore service • Learn without hiding failure • Prevent recurrence
Detect → Triage → Contain
↓ ↓
Communicate Restore
↓
Root Cause → Prevent RecurrenceThe goal of incident response is not only to restore data—it is to make the next incident less likely and less damaging.
Can you explain how an orchestrated workflow becomes reliable in production? Open each item after answering it in your own words. The 24 interactive practices above drive certificate progress.
Because technical execution success does not guarantee accuracy, completeness, validity or freshness.
Validation tests explicit expectations; observability provides broader signals that help detect and explain unexpected behavior.
Because “on time” depends on when users need the data to make a decision.
It reveals upstream sources, transformations and downstream consumers, narrowing both root-cause search and impact analysis.
Measured expectations, actionable monitoring, clear ownership, disciplined incident response and continuous improvement.
Match each reliability need with the most useful control or signal.
| Need | Strong candidate |
|---|---|
| Prevent duplicate business keys | NOT NULL + uniqueness tests |
| Detect late daily data | Freshness timestamp / lag metric |
| Spot abnormal pipeline behavior | Runtime, throughput and error trends |
| Find downstream impact | Data lineage |
| Reduce noisy notifications | Severity + actionable alert routing |
| Set a measurable reliability target | SLI + SLO |
Data quality and observability transform pipelines from black boxes into systems that can be measured, explained and improved. The goal is not zero incidents; it is fast detection, limited impact, clear recovery and increasing trust over time.
This training uses vendor-neutral data-engineering concepts so the practices can be applied across SQL Server, cloud platforms, lakehouses and modern orchestration stacks.