How is this different from Data Integration or BI?

Integration connects systems with a data sync layer. BI focuses trusted data into decisions with a reporting and analytics layer. Data Engineering builds the pipeline those paths depend on — contracts, transforms, freshness, and lineage.

How is this different from Product Engineering?

Product Engineering ships product bets with a cross-functional squad. Data Engineering ships trusted data paths. Many programs use both — a trusted pipeline for ops truth, then product features on top.

Do you build lakes, warehouses, or both?

Whichever the serve surface needs. The hero story is a trusted pipeline with contracts and freshness — lakehouse, warehouse, or hybrid when consumers and SLAs demand it.

What do you need from us to start?

Clear sources and consumers, someone who owns freshness decisions, and access to map lineage. Landscape Sketch is a fast way to surface integration and data risks before the first pipeline ships.

When should we wait for BI instead?

When the primary ask is dashboard polish and the pipeline is already trusted. If numbers disagree or freshness is hoped, start here.

DATA ENGINEERING_

Ship a trusted data pipeline — not dashboard theater

Parallaxis data engineering turns messy sources into a trusted data pipeline operators can ship and audit: ingest → contract → transform → observe → serve. Lakes and warehouses with freshness SLAs, lineage, and quality gates — so BI and automation run on numbers you can defend.

Every engagement runs a trusted data pipeline: Ingest → Contract → Transform → Observe → Serve. Tap the stages on the right.

Map your systems Talk through your map

Trusted data pipeline_

Messy sources become a trusted pipe operators can ship and audit. Tap a stage — ingest through serve.

Tap a stage

Ingest_

Stage 01 / 05

Land sources with owners

Connect systems of record with clear cadence, schema hints, and who gets paged when the feed stalls.

Sources
Owners

Full conduit_

01 Ingest
02 Contract
03 Transform
04 Observe
05 Serve

Contracts + freshness + lineage — not dashboard theater

01Ingest sourcesOwners, cadence, landing
02Publish contractsSchemas and freshness SLAs
03Transform & testTrusted models in the warehouse
04Observe & serveWatermarks, alerts, handoff

02 · CHAOS VS PIPELINE_

Same sources. Two very different paths.

Toggle Spreadsheet chaos vs trusted data pipeline — watch where trust dies or completes.

Same constraint_

“Ops needs yesterday’s numbers by 9am — and nobody trusts the shared drive workbook.”

Toggle path — pipeline morphs_

Path nodes_

Export CSVs by hand
Flowing_
Paste into shared workbook
Flowing_
Contracted schemas
Never reached_
Freshness SLA + alerts
Never reached_
Auditable lineage
Breaks here_

Outcome_

Dashboard theater

Manual exports, stale tabs, and silent formula drift. Nobody can say which number is true.

03 · FRESHNESS BOARD_

Trust dies when freshness is hoped

Toggle freshness pressures — trust score and outcome update live.

Freshness pressures_· Toggle what erodes trust

Trust score_

54/100

2 pressures active_

Outcome_

Fragile pipeline

Some trust exists, but late sources or backfills can still poison serve quietly.

04 · LINEAGE INSPECTOR_

Know the blast before you break a job

Tap a node — see upstream, downstream, and what burns if it fails.

Lineage nodes_· Tap to inspect

CRM export_

Nightly Salesforce extract into landing.

Upstream_

← Salesforce API

Downstream_

→ stg_accounts
→ dim_customer

Break-blast radius_

Customer join keys go stale — every revenue dashboard drifts.

05 · SERVE CHECKLIST_

What a data engineering engagement leaves behind

Open a chapter — contracts through serve. Vocabulary the owning team runs next.

Checklist chapters_

5 chapters

- Source inventory and owners
- Published schemas / contracts
- Freshness SLAs per serve surface

In scope_

Trusted data pipeline: ingest → contract → transform → observe → serve
Pipelines, lakes, warehouses with freshness and lineage
Ops handoff so the pipeline stays trusted

Out of scope_

Source connectivity only (see Data Integration)
Dashboard polish without a trusted pipeline (see Data Analysis & BI)
Product squads without a data constraint (see Product Engineering)

FIT_

Is Data Engineering the right next step?

Self-qualify — we work best when sources, consumers, and freshness are the constraint.

Good fit_

You have clear sources and consumers who need trusted tables or feeds
Freshness or quality failures already hurt ops, finance, or automation
Someone can own the pipeline after handoff — not a one-off dashboard ask

Not yet_

You are still choosing metrics with no owners (start with Landscape Sketch or BI later)
You only need source connectivity with no warehouse / serve surface yet (see Data Integration)
You want product features shipped by a squad with no data pipeline constraint (see Product Engineering)

NEXT STEP_

Ready to put a trusted data pipeline on your sources?

Map your systems — or talk through the feed that must be trusted by 9am. We will confirm sources, consumers, freshness needs, and whether integration or BI should land next.

Map your systems

Common questions

We run a consistent path for data engineering: ingest → contract → transform → observe → serve. We build pipelines, lakes, and warehouses operators can audit — with freshness SLAs, lineage, and quality gates — not slideware dashboards.

Ship a trusted data pipeline — not dashboard theater

Land sources with owners

01ContractsSchemas and SLAs producers and consumers share.−

02TransformsVersioned models with tests that mean the same tomorrow.+

03Quality testsGates that fail the job before bad data is served.+

04Freshness & observeWatermarks, alerts, and on-call who can act.+

05Serve & handoffConsumers know what is trusted — and who owns the pipe.+

Good fit_

Not yet_

Ready to put a trusted data pipeline on your sources?

Common questions

Talk through your map