Question 1

What are the signs you have a data reliability problem?

Accepted Answer

A business user finds a bad number before your team does. A pipeline exits successfully but writes stale, duplicate, or incomplete rows, and nothing flags it. The same failure recurs every few weeks and still costs someone a manual rerun. Root-causing a bad metric takes an afternoon of grepping logs and Slack instead of minutes. Any one of these means the gap isn't job-level monitoring, it's that nobody's watching the data itself.

Question 2

We already have Airflow/dbt alerts. Isn't that enough?

Accepted Answer

Job-level alerts catch failures. They don't catch a job that succeeds but produces stale, incomplete, or silently wrong data, which is the failure mode that actually erodes trust in your data. We add the observability layer on top: freshness and schema-change detection, quality checks, and lineage so you can find the upstream cause in minutes instead of an afternoon of Slack archaeology.

Question 3

How do you monitor data quality and catch freshness or schema issues?

Accepted Answer

Checks run on the data itself, not just the job that produced it: freshness (did this table refresh on schedule), schema (did a column type or structure change upstream), volume (did row counts move outside a normal range), and distribution (did values drift in a way that suggests a quality problem, not just a business change). Below dozens of pipelines, we usually implement these directly on your stack — dbt tests, custom SQL checks, lightweight monitoring — tuned to your actual SLAs instead of noisy defaults that get muted within a week.

Question 4

How do you set and track a data SLA or SLO?

Accepted Answer

Start with the datasets where a break actually costs something, the ones a business user or another system treats as ground truth, and define freshness, completeness, and accuracy targets for those, not every table you have. We monitor against the SLO directly and alert on the threshold, not on every anomaly, so the alert volume matches what the business actually cares about.

Question 5

What does 'automated recovery' actually mean here?

Accepted Answer

For known, recurring failure patterns, replay from checkpoint, backfill on schema-compatible changes, and automatic rerun with alerting only on repeated failure, not every failure. The goal is fewer 2am pages for problems that already have a known fix, not a fully autonomous system.

Question 6

How does lineage-based root cause analysis work?

Accepted Answer

Column-level lineage maps which upstream tables, jobs, and transformations feed a given metric or dashboard field. When a number looks wrong, you trace backward through that map to the exact job or schema change that caused it, instead of manually searching logs and Slack threads for what changed recently. The lineage has to be built to the depth your stack actually needs, warehouse, orchestrator, and transformation layer, not just a surface-level table diagram.

Question 7

What's your data incident management process when a pipeline breaks?

Accepted Answer

An alert fires against a defined SLO, not a noisy default threshold. Lineage points at the likely upstream cause before a human starts investigating. Known, recurring failure patterns replay, backfill, or rerun automatically, so a page only goes out for a failure that doesn't already have an established fix. What we hand off is a documented runbook per failure mode, not a promise that someone will remember what to do at 2am.

Question 8

What does the assessment deliver, and how long does it take?

Accepted Answer

We map your critical pipelines and tables, identify where freshness, schema, or quality issues currently go undetected, and score your incident response process. You get a prioritized list of the highest-risk gaps and a reliability SLO proposal for your most business-critical datasets.

Question 9

What's your disaster recovery process for the data platform itself, not just a single database?

Accepted Answer

We set RPO (how much data you can afford to lose) and RTO (how long a failure can take to recover from) per critical dataset or pipeline, not one blanket number for everything. Failover and recovery paths get built to those targets, and we run scheduled recovery drills so the first real test of the plan isn't a live incident. Resilience engineering here means the platform keeps producing correct data through a failure, not just that a database comes back online.

Question 10

Why does "revenue" mean something different in every dashboard?

Accepted Answer

Usually because each dashboard is built off its own query, with its own small assumptions about what to include or exclude. We fix that at the source with a tested semantic/metrics layer, one definition per number, built and checked in dbt, so every report, Power BI, Tableau, Looker, or Metabase, reads the same, correct value instead of five slightly different ones.

Question 11

Do you build or fix the dashboard itself, not just monitor the pipeline?

Accepted Answer

Yes. Reliability doesn't stop at the pipeline: we also clean and model the data into a tested, documented semantic layer, and build or fix up the Power BI, Tableau, Looker, or Metabase report on top of it, so the dashboard itself is reading from data that's checked, not just data that arrived on time.

Question 12

How is this different from database monitoring or uptime monitoring?

Accepted Answer

Uptime monitoring tells you a service is up. It doesn't tell you a table stopped refreshing, a schema change silently broke three downstream dashboards, or a pipeline ran successfully but wrote wrong numbers. Data reliability engineering watches the data itself: freshness, schema, volume, and quality, across the whole pipeline, not just whether a process exited with code 0.

Question 13

Do we need to buy a tool like Monte Carlo or Bigeye first?

Accepted Answer

Not necessarily. Commercial observability platforms make sense once you have dozens of pipelines and a team to own the tool. Below that, we often implement freshness, schema, and quality checks directly on your stack (dbt tests, custom SQL checks, lightweight monitoring) at a fraction of the license cost, and only recommend a platform once you've outgrown that.

Question 14

How is this different from hiring a data reliability engineer in-house?

Accepted Answer

A full-time data reliability or observability engineer is a significant salary commitment before benefits, and takes months to hire and ramp. We deliver the assessment and initial implementation in weeks, at a fraction of that annual cost, then hand off documented runbooks your existing team can run. Some clients later hire in-house once the system is proven; we build it so that handoff is clean either way.

Question 15

How much does data reliability engineering cost?

Accepted Answer

The assessment is priced after we understand how many pipelines and tables are in play. Implementation and ongoing reliability support run on a monthly retainer sized to your coverage footprint. We price after the assessment, not before we know what's actually broken.

Question 16

What actually drives the cost of a reliability engagement?

Accepted Answer

Four things: how many critical pipelines and tables are in scope, the uptime/freshness SLA you need on them, how deep the lineage has to go to trace root cause across your stack, and whether you need on-call coverage or just implementation. We size the assessment and retainer to those, not to a published tier.

Question 17

How do you measure whether data reliability is improving?

Accepted Answer

Time to detect a break (ideally an alert, not a business-user complaint), time to root cause once detected, and how many incidents recur without an automated fix. We baseline these during the assessment and track them against the SLOs we set, so 'reliability improved' is a number you can point to, not a feeling.

Question 18

What are data reliability engineering services?

Accepted Answer

Data reliability engineering services cover the monitoring, root cause analysis, and recovery work that keeps your data correct and on time, not just your pipelines running. That includes data observability, freshness and schema-change detection, data quality monitoring, column-level lineage, data incident response, SLOs, and disaster recovery for the data platform. At DharmOps, we start with a scoped assessment of your critical pipelines, then build the checks and runbooks on the stack you already run.

Question 19

What is data downtime, and how do you prevent it?

Accepted Answer

Data downtime is any period when your data is missing, stale, duplicated, or wrong, even though every job reported success. It shows up as a dashboard that didn't refresh, a revenue number that changed overnight, or a table that quietly lost rows. We prevent it by monitoring freshness, volume, schema, and distribution on the tables that matter, alerting against an SLO, and replaying or backfilling known failure patterns automatically.

Question 20

What is the difference between data observability and data quality?

Accepted Answer

Data quality asks whether a dataset is correct: no duplicates, valid values, complete records. Data observability asks whether you would know the moment it stopped being correct, and where the break came from. Quality checks define what good looks like. Observability adds freshness and schema monitoring, anomaly detection, and lineage so the failure is caught and traced without a manual search. You need both, and we implement them together.

Question 21

What is the difference between data quality software and data quality services?

Accepted Answer

Data quality software is a tool that runs checks and raises alerts. A data quality service is the work around it: deciding which tables need checks, setting thresholds that match your SLAs, tuning out noisy alerts, wiring alerts to an owner, and writing the runbook for each failure. A tool without that work usually ends up muted within a few weeks. We do the service work, on your existing tooling or on a platform we help you select.

Question 22

Do you use data quality tools or build custom checks? How do you decide?

Accepted Answer

It depends on pipeline count and who owns the tool. Below a few dozen pipelines, dbt tests, custom SQL checks, and lightweight open-source monitoring usually cover freshness, schema, and quality at a fraction of a license cost. Once you have dozens of pipelines and a team to own it, a commercial data observability platform earns its price. We decide during the assessment, based on your stack, and we don't push a vendor either way.

Question 23

How do you keep data anomaly detection from flooding the team with false alerts?

Accepted Answer

We alert on SLO breaches for the datasets that carry business risk, not on every statistical wobble across every table. Thresholds are tuned to each table's real refresh pattern, including weekends, month-end spikes, and seasonal volume. Alerts are routed to the owner of the affected pipeline with lineage attached. Alert volume gets reviewed after the first few weeks so the noisy checks are fixed instead of muted.

Question 24

Can you work with our existing warehouse, orchestrator, and BI tools?

Accepted Answer

Yes. The reliability layer sits on top of what you already run: your cloud warehouse, your orchestrator such as Airflow, your dbt or SQL transformation layer, and the Power BI, Tableau, Looker, or Metabase reports built on top. We don't require a migration or a new platform to start. Where something is missing, such as no lineage or no tests on core models, we add it in place.

Question 25

How long does it take to reduce data downtime?

Accepted Answer

The assessment maps your critical pipelines and shows where breaks currently go undetected. Checks and alerts on your highest-risk tables can go live within weeks of that assessment, and each check comes with a runbook. From there we track time to detect, time to root cause, and repeat incidents against the baseline, so you can see downtime shrink instead of taking our word for it.

Situation	Likely Solution
A bad number reached people before your team caught it	Data Reliability — freshness, volume, schema, and quality monitoring on the tables that matter
Jobs succeed, but the data they write is stale, duplicate, or incomplete	Data Reliability — schema-change and quality checks catch a breaking change before it reaches a dashboard
The same failure pages someone every few weeks with no automated fix	Pipeline Reliability — automated retry, backfill, and recovery for known failure patterns
Root cause takes an afternoon of grepping logs and Slack threads, not minutes	Data Observability — lineage-based root cause analysis traces a bad number back to the exact upstream change
"Revenue" is calculated three different ways across three dashboards	Semantic & Metrics Layer — one tested definition, used everywhere

Area	Before	After
Failure detection	A business user reports the stale dashboard first	Freshness and schema-change alerts fire before anyone downstream notices
Root cause	Manual grep through logs and Slack threads	Lineage traces the bad number to the exact upstream job or schema change
Recovery	Someone reruns the job by hand, if they remember how	Known failure patterns replay, backfill, or rerun automatically
Regional failover (healthcare HA engagement)	Manual multi-hour regional cutover	<30s automatic failover, 99.99% uptime SLA across 3 regions
Reporting under load (traffic management engagement)	Peak-hour reporting timeouts	12x faster reports, 99.9% uptime maintained

	DharmOps	Reactive On-Call Only	In-House Hire	Platform Alone
Time to catch a silent break	Tuned to your actual SLAs	After a business user complains	Depends on one person's coverage	Depends on who's watching the tool
Root cause time	Minutes, via lineage	Manual investigation	Manual, until they build lineage themselves	Dashboard exists, still needs a human to interpret it
Cost model	Scoped assessment + retainer, no license	"Free" until an incident costs you	Full salary + benefits, months to hire and ramp	Annual license, plus a team to own it
Coverage	A team, not one person's vacation-limited time	None	Single point of failure	None, it's a tool, not a team

Data Reliability

What Is Data Reliability?

What Problems Trigger This Engagement

A bad number reached people before your team caught it

Jobs succeed, data doesn't

The same failure pages someone every few weeks

Root cause takes an afternoon, not minutes

Match Your Situation to the Fix

Why Reliability Engineering, Not Just Monitoring

Catch Breaks Before Your Business Users Do

Root Cause in Minutes, Not an Afternoon

Recovery That Doesn't Wait on a Human

What DharmOps Builds

Pipeline Reliability