Drag. Drop. Deploy. No-code ETL/ELT pipelines on Apache Airflow plus AI-powered interactive data prep — all from one agent, all in plain English.
Your data team spends 60–80% of their time preparing data and writing pipeline code by hand. xAQUA fixes both. Composer builds, deploys, and monitors Airflow DAGs visually. Athyna turns natural-language requests into instant transformations on an in-memory SQL engine. First pipeline on Day 1. No Python required.
Works alongside your data engineering team — eliminating the scripting tax, compressing prep time by 20×, and giving business users a self-serve on-ramp without compromising governance.
SalesforceCDC → EntityResolve → Dedupe → QualityCheck → SnowflakeLoadauto-generated.
Most data teams build every pipeline the same way: open an IDE, hand-code Python DAGs for Airflow, wait for a code review, fight connector quirks, and spend 60–80% of their day on data prep no one sees. xAQUA collapses the whole lifecycle into drag-and-drop — and gives business users a natural-language on-ramp so the engineering team stops being the org's pipeline ticket queue.
Every org has more pipeline requests than engineering hours. Prep work swallows analyst days. DAGs fail silently at 3am. The backlog grows. Hiring doesn't fix it.
Every pipeline is hand-coded Python. Every schema change is a PR. Every new source takes two sprints. Your most expensive engineers are spending their days writing connector boilerplate, not solving real problems.
Industry consensus: 60–80% of analyst and scientist time goes into preparing data — cleaning, reshaping, joining, deduplicating. All of it happens in one-off scripts and private notebooks that no one else can reuse.
A schema change upstream breaks a join. The DAG keeps running. Nulls propagate. The dashboard looks fine. You find out three weeks later when someone notices the numbers are wrong. There's no contract. There's no gate.
Connect any source. Compose the pipeline. Prep the data interactively. Deploy and observe. All without writing Python.
Composer handles batch and streaming pipelines. Athyna handles interactive, real-time prep. Same metadata, same catalog, same governance.
Drag-and-drop pipeline builder. Compose Apache Airflow DAGs visually. Python for the DAG auto-generates — no code required from you.
● live1000+ operators out of the box — Airflow native, provider packs, and your custom operators. Drag into any DAG. Visually configure parameters.
● 1000+ opsClean, transform, and explore data in real time. Drag-and-drop reshapes plus natural-language commands — all running on an in-memory SQL engine for instant feedback.
ai co-pilot"Merge these three files on customer_id, drop duplicates, parse the date column." Athyna's AI co-pilot converts intent into SQL and runs it — 20× faster than manual prep.
ai-generatedBuilt-in operators for validation, deduplication, probabilistic entity resolution, and integrity enforcement. Quality gates at every step — not just at the end.
● gatedChange Data Capture via Apache Kafka streams. Real-time and near-real-time sync across operational systems. Source to target without the batch delay.
streamingDAG versioning with integrated Git repository. One-click deploy to Kubernetes. Automated CI/CD across dev, stage, prod. Rollback with a single click.
git + k8sHistoric pipeline performance, SLA tracking, schema contract enforcement, anomaly detection, and alerts. Know why a pipeline broke — in minutes, not hours.
continuousNine out-of-the-box pipeline templates. All composable. All deployable. All governed.
Composer turns operators into visual blocks; Airflow DAG Python auto-generates. Athyna turns natural-language prep requests into SQL that runs on an in-memory engine.
Composer for production DAGs. Athyna for interactive prep. Same metadata, same catalog, same governance. Zero Python either way.
From a business user prepping their first file to a senior data engineer shipping CDC to production — same agent, same drag-and-drop canvas.
dept/division/business_unit → unit. Date formats: MM/DD/YYYY + DD-Mon-YYYY + ISO → parsed and unified.
sf_to_snowflake_cdc DAG with 6 operators:
SalesforceCDC → Account + Opportunity via Kafka stream
EntityResolve → probabilistic match on email Spark UDF
Dedupe → keep latest by LastModifiedDate
SchemaContract → enforce target schema design-time + runtime
QualityGate → fail DAG if quality < 99% gated
SnowflakeLoad → upsert on PK
*/15 * * * *. Git committed to main. K8s deployed. SLA 10 min. Anomaly alerts wired to Slack.
✓ DAG composed & deployed in 3 min · 0 lines of Python written
claims_ingestion_daily failed at step 4 SchemaContractCONTRACT BREACH.
claim_sub_type (VARCHAR). Target schema contract rejected the drift.
Different skill level. Different problem. Same agent.
The AI Data Engineer is your pipeline + prep agent. Composer for durable, production-grade ETL/ELT and CDC. Athyna for interactive, natural-language prep that runs at in-memory speed.
It doesn't just build pipelines. It enforces schema contracts, gates on quality, versions every DAG in Git, deploys through CI/CD, and watches every run for SLA breaches and anomalies. All while giving business users a self-serve on-ramp through Athyna's plain-English prep.
Airflow runs DAGs — but someone has to write them. dbt models transforms — but someone has to code them. Fivetran moves data — but doesn't transform it. xAQUA does all of it, visually, governed, and together.
The AI Data Engineer takes the routine 80% — connector wiring, DAG scaffolding, deployment YAML, dedupe logic, schema drift handling — so your human engineers can focus on the 20% that actually needs judgement: architecture, strategic data modeling, and performance at scale.
"We delivered a multi-source customer 360 data product in six weeks — something our team had been trying to finish for over a year."
All operating on the same semantic layer. All part of your AI Data Team.
See how the AI Data Engineer turns hand-coded DAGs into drag-and-drop pipelines — and gives business users a natural-language prep on-ramp. First pipeline on Day 1. Private by default. Works on the stack you already have.