Pipeline & App Optimization

One pipeline. Five systems. Including the AI ones.

Kafka

Airflow

Databricks

Snowflake

BigQuery

Mosaic AI

Vector DB

GenAI app

+ dbt · Spark · Airflow MWAA · Cortex

End-to-end pipeline visibility

Your pipeline crosses five systems — including the AI ones. ^{Your monitoring shouldn't stop at one.}

Most reliability tools watch one box at a time. The Spark UI watches the cluster. The model dashboard watches the model. When the GenAI app slows down, you stitch it together yourself. Unravel traces the work itself, all the way through.

——— Why is today slower?

You can't fix what your monitoring _{can't compare.}

Spark UI shows you today's run. The dashboard shows you a number. Neither tells you what changed since yesterday's run finished in 22 minutes. Unravel diffs every run against its own history — query plan, data volume, infrastructure, code — and surfaces the delta in plain English.

How we're different

You probably already have tools. ^{Here's where they stop.}

Every category sees one box. Reliability lives in the spaces between them.

——— Reliability looks different per workload

Same platform. ^{Three failure modes.}

Batch breaks at the SLA. Streaming breaks when lag drifts past the budget. GenAI breaks when an agent silently goes off the rails. Arvix watches all three the same way.

The Reliability framework

Predict. Diagnose. ^Resolve.

Three capabilities every modern data and AI platform needs. What Unravel delivers at each stage — across batch, streaming, training, and GenAI.

Catch the failure
before the SLA breaks.

Most platforms find out a job is late when it misses the SLA. Arvix watches every run as it executes — plan, data volume, infrastructure, freshness — and flags drift before downstream feels it.

Live SLA forecasting

Per-run completion projections, updated as the job runs. “On time” vs “trending late” with a time-to-SLA window.

Run-vs-run drift detection

Plan flips, shuffle blow-ups, spill spikes — caught the moment they diverge from the job’s own history.

Upstream data-health signals

Schema changes, stale stats, late-arriving partitions surfaced as causes — not separate alerts in another tool.

GenAI freshness + cost guardrails

Vector-index staleness, prompt-spend anomalies, model-latency drift — the same way as a Spark job.

CI checks for cost + perf

Pre-merge guardrails block regressions before they reach production. Bad deploys blocked, not reported.

arvix · sla-forecast · in-flight runs

Runs in flight

142

across 4 platforms

On track

134

healthy

At risk

action queued

Forecast late

paged · owners notified

In-flight risk — forecasted

feature_eng_pipelinespill risingSLA −38m

customer_facts_dagplan flippedSLA −54m

support_assistant_idxstaleness +12mSLO breach soon

churn_model_traincheckpoint slow+1h 20m

Stop reading dashboards.
Read the cause.

Most teams know something’s wrong in 90 seconds. Then they spend 90 minutes proving it. Arvix correlates query plan, data, infra, and code into a single root-cause statement — in plain English.

Root-cause in one sentence

“Stale stats on customer_dim caused the optimizer to fall back from broadcast to sort-merge.” Not a chart — a sentence.

Cross-system trace

Kafka → Airflow → Spark → model → GenAI app, in one timeline. Stitched once, by the agent, not by you.

Run-history diff

Yesterday vs today — every dimension that changed, automatically. No more guessing what was different.

War room replaced

Single shareable trace link. Platform, ML, and data engineers see the same picture. No swivel-chairing.

Audit-ready postmortem

Every incident produces a structured timeline — cause, blast radius, fix, validation — ready to share.

arvix · incident · INC-2417 · root cause

feature_eng_pipeline · +278% runtime

Diagnosed in 47s

Cause · Plan regression

Stale stats on customer_dim → optimizer fell back from BroadcastHashJoin to SortMergeJoin.

since 02:14

Confirmed

Trigger · Upstream schema change

customer_dim ingestion job dropped/recreated table. ANALYZE not re-run — stats marked stale 12h ago.

12h ago

Linked

→

Blast radius

support_assistant (GenAI) · product_recs_index · hourly_kpi_dash — all downstream of feature_eng_pipeline.

3 systems

Mapped

From recommendation
to running.

Other tools tell you what to fix. Unravel fixes it. AutoApply for low-risk actions. Human-in-the-Loop for production code. You set the threshold — the platform does the work.

Arvix code rewrites

Query and config fixes generated, validated against 30-day history, applied with one click. Side-by-side diff, full rollback.

Shadow validation

Every fix is tested against real workload data before it reaches production. No “looks right — ship it.”

AutoApply + Human-in-the-Loop

Config changes apply automatically. Code changes need sign-off. Set the policy per workload.

Cluster + warehouse right-sizing

Sized to actual workload behavior — across Databricks, Snowflake, BigQuery. Not guesswork.

Recurrence guardrails

Fixes become policies. The same regression can’t ship twice. Validated savings tracked to the dollar.

arvix · resolution · INC-2417

3 actions · SLA recovered + 33m headroom

2 AutoApply ready

✓

customer_dim · ANALYZE TABLE refresh

Refresh stats so the optimizer can pick BroadcastHashJoin again. Validated against shadow run.

19m 42s

AutoApply

✎

feature_eng_pipeline · broadcast threshold

Raise spark.sql.autoBroadcastJoinThreshold 10MB → 64MB. Diff staged, awaits owner approval.

+headroom

Needs Review

◇

Recurrence guardrail

Add CI check: ANALYZE must run after schema change on dim_* tables. Same regression can’t ship twice.

policy

Stage

Trust by design

Every Arvix optimization is validated against real production ^{behavior before it's applied.}

No heuristics. No black boxes. No surprises. If a change would break something, Arvix doesn't apply it. That's why customers let Arvix run on AutoApply for 70%+ of actions.

Zero production incidents from Arvix-applied optimizations · all enterprise customers

The Reliability framework

Predict. Diagnose. ^Resolve.

Three capabilities every modern data and AI platform needs. What Unravel delivers at each stage — across batch, streaming, training, and GenAI.

Catch the failure
before the SLA breaks.

Most platforms find out a job is late when it misses the SLA. Arvix watches every run as it executes — plan, data volume, infrastructure, freshness — and flags drift before downstream feels it.

Live SLA forecasting

Per-run completion projections, updated as the job runs. “On time” vs “trending late” with a time-to-SLA window.

Run-vs-run drift detection

Plan flips, shuffle blow-ups, spill spikes — caught the moment they diverge from the job’s own history.

Upstream data-health signals

Schema changes, stale stats, late-arriving partitions surfaced as causes — not separate alerts in another tool.

GenAI freshness + cost guardrails

Vector-index staleness, prompt-spend anomalies, model-latency drift — the same way as a Spark job.

CI checks for cost + perf

Pre-merge guardrails block regressions before they reach production. Bad deploys blocked, not reported.

arvix · sla-forecast · in-flight runs

Runs in flight

142

across 4 platforms

On track

134

healthy

At risk

action queued

Forecast late

paged · owners notified

In-flight risk — forecasted

feature_eng_pipelinespill risingSLA −38m

customer_facts_dagplan flippedSLA −54m

support_assistant_idxstaleness +12mSLO breach soon

churn_model_traincheckpoint slow+1h 20m

Stop reading dashboards.
Read the cause.

Most teams know something’s wrong in 90 seconds. Then they spend 90 minutes proving it. Arvix correlates query plan, data, infra, and code into a single root-cause statement — in plain English.

Root-cause in one sentence

“Stale stats on customer_dim caused the optimizer to fall back from broadcast to sort-merge.” Not a chart — a sentence.

Cross-system trace

Kafka → Airflow → Spark → model → GenAI app, in one timeline. Stitched once, by the agent, not by you.

Run-history diff

Yesterday vs today — every dimension that changed, automatically. No more guessing what was different.

War room replaced

Single shareable trace link. Platform, ML, and data engineers see the same picture. No swivel-chairing.

Audit-ready postmortem

Every incident produces a structured timeline — cause, blast radius, fix, validation — ready to share.

arvix · incident · INC-2417 · root cause

feature_eng_pipeline · +278% runtime

Diagnosed in 47s

Cause · Plan regression

Stale stats on customer_dim → optimizer fell back from BroadcastHashJoin to SortMergeJoin.

since 02:14

Confirmed

Trigger · Upstream schema change

customer_dim ingestion job dropped/recreated table. ANALYZE not re-run — stats marked stale 12h ago.

12h ago

Linked

→

Blast radius

support_assistant (GenAI) · product_recs_index · hourly_kpi_dash — all downstream of feature_eng_pipeline.

3 systems

Mapped

From recommendation
to running.

Other tools tell you what to fix. Unravel fixes it. AutoApply for low-risk actions. Human-in-the-Loop for production code. You set the threshold — the platform does the work.

Arvix code rewrites

Query and config fixes generated, validated against 30-day history, applied with one click. Side-by-side diff, full rollback.

Shadow validation

Every fix is tested against real workload data before it reaches production. No “looks right — ship it.”

AutoApply + Human-in-the-Loop

Config changes apply automatically. Code changes need sign-off. Set the policy per workload.

Cluster + warehouse right-sizing

Sized to actual workload behavior — across Databricks, Snowflake, BigQuery. Not guesswork.

Recurrence guardrails

Fixes become policies. The same regression can’t ship twice. Validated savings tracked to the dollar.

arvix · resolution · INC-2417

3 actions · SLA recovered + 33m headroom

2 AutoApply ready

✓

customer_dim · ANALYZE TABLE refresh

Refresh stats so the optimizer can pick BroadcastHashJoin again. Validated against shadow run.

19m 42s

AutoApply

✎

feature_eng_pipeline · broadcast threshold

Raise spark.sql.autoBroadcastJoinThreshold 10MB → 64MB. Diff staged, awaits owner approval.

+headroom

Needs Review

◇

Recurrence guardrail

Add CI check: ANALYZE must run after schema change on dim_* tables. Same regression can’t ship twice.

policy

Stage

Trust by design

Every Arvix optimization is validated against real production ^{behavior before it's applied.}

No heuristics. No black boxes. No surprises. If a change would break something, Arvix doesn't apply it. That's why customers let Arvix run on AutoApply for 70%+ of actions.

Zero production incidents from Arvix-applied optimizations · all enterprise customers

Want to see a Predict → Diagnose → Resolve cycle on your own pipeline?

Bring a recurring SLA miss — we'll trace it end‐to‐end on a 30‐minute call.

Book a Demo

The objection no other reliability tool answers

Engineering won't fight Unravel.
^{Because Unravel doesn't ask them to leave their tools.}

Forecasts, root causes, and fixes land in the surfaces engineers already use — Slack, PagerDuty, the PR, the Spark UI. Arvix opens a PR with the proposed fix; the team reviews and merges a diff, the same way they handle any other code change. No new dashboard, no new on-call rotation, no new vocabulary.

Engineering owns reliability because the platform makes prevention the path of least resistance — not because you wrote a runbook nobody reads.