Drag. Drop. Deploy. No Python. No lock-in. No waiting.
Composer turns ETL operators into visual blocks. Build a production-grade workflow on a canvas — wire a Dataset, blend with Data Blend, transform, then map and load with MIL. Powered by xAQUA's in-memory query engine. Version it in Git. Deploy with one click. First pipeline on Day 1.
Built for data engineers who've had enough of boilerplate, and for analysts who shouldn't need Python to move data. Composer composes the workflow. Your team owns the outcome.
Most data teams spend more time writing and maintaining pipeline code than getting value from the data that moves through it. The scripts sprawl. Schema breaks on Friday at 5pm. Nobody remembers why the cron exists. By the time a new source lands, you're three sprints behind.
Composer replaces the code with a canvas. xAQUA's in-memory query engine runs the workflow underneath — pipelines auto-generate, version in Git, and deploy through CI/CD to Kubernetes. Schema contracts catch breaks at design time. Quality operators catch bad data before it leaves the pipeline. Observability tells you what broke, and why, in minutes.
Pipelines aren't code. They're an operating model.
Composer isn't another drag-and-drop ETL canvas. It's the tool of xAQUA's AI Data Engineer, sitting on a foundation engineered to solve every critical data pipeline challenge — semantics, lineage, observability, master data, migration testing — at the root.
Composer is the tool of xAQUA's AI Data Engineer — an AI agent that proposes pipelines, configures operators, and validates contracts in plain English. Powered by Active Metadata. You review, approve, and steer. Augmentation, not replacement.
Powered by SemantIQ. Composer understands both sides of every pipeline — source and target — in business terms. Source-to-target field mapping is auto-generated, not hand-documented.
Active Metadata from SemantIQ tracks every transform at the column level. Forward impact: "if I change this, what breaks?" Backward root-cause: "this dashboard is wrong — where did the data come from?"
The killer capability. xAQUA Analytics Data Lake lets you reconcile source and target migrations in plain English — no SQL, no scripts. Ask "Do Q3 totals match?"; get row counts, sums, deltas, and the rows that don't reconcile. Migration testing that used to take weeks, in minutes.
SLA tracking, anomaly detection, schema drift alerts, and dataset-level Trust Scores are not bolted on — they're built into every operator. Quality gates fire before bad data leaves the pipeline.
Automated MDM, Probabilistic Entity Resolution, and SCD-0/1/2/3 strategies — all built into Composer's MIL operator. No separate MDM tool. Customer 360, Patient 360, Member 360 — by configuration.
Schema drift. Silent failures. Cascading downstream errors. The firefighting tax. Composer collapses it with a four-part defense — prevent at design time, detect in real time, trace through end-to-end lineage, alert before bad data leaves the gate.
Most data teams report spending roughly 60% of their working time investigating, diagnosing, and repairing pipelines that broke overnight — schemas that drifted, sources that changed, queries that silently returned the wrong rows. That's three days a week, per engineer, lost to firefighting. Composer reclaims those days. Pipelines built on Composer don't break the same way — and when something does shift upstream, you know it minutes after deploy, not the morning the dashboard is wrong.
Government agencies are stuck on mainframes. Commercial firms are stuck on systems someone wrote in 1998. Both face the same trap: undocumented business rules, opaque schemas, and migration projects that overrun every estimate. Composer breaks the trap. Built on a semantic-layer foundation that understands both sides of the migration — your legacy schema and your target system — Composer auto-generates the mapping, enforces master-data quality, and lets you reconcile source and target in plain English.
SemantIQ models the semantics of your legacy source and your target system — Salesforce, Snowflake, Databricks, BigQuery, whatever you're migrating to. With both sides understood, source-to-target field mapping is auto-generated, not hand-documented.
Migration that loses or corrupts master data isn't migration — it's data debt with a new database. Composer's quality engineering is built into every operator: profile, cleanse, deduplicate, resolve, and history-track on the way through.
The killer feature: xAQUA Analytics Data Lake lets you virtually reconcile source and target — without writing a single line of SQL. Ask in English: "Do Q3 totals match?" The data lake responds with row counts, sums, deltas, and the rows that don't reconcile.
BENEFITS_HIST) and the Snowflake target (warehouse.payments). Are totals and row counts identical?
A California state agency ran a tangle of legacy datasets in diverse formats, with severe data quality problems and no reliable master or reference data. Compliance reporting depended on manual reconciliations. They needed to migrate to Salesforce — fast, with audit-grade quality.
Using xAQUA Athyna with natural-language transformations (NL → ConverseSQL → in-memory query engine) for prep, and xAQUA Composer for no-code ETL into Salesforce, the team profiled, cleansed, deduplicated, and loaded six datasets through DEV → TEST → PROD with one fractional analyst. Master-data uniqueness was enforced with SCD-0 and SCD-1 strategies built directly into Composer's MIL operator.
Drag a template. Configure the sources. Deploy. Nine production-grade starting points for the patterns teams build every quarter.
Create. Modify. Deploy. Run. Monitor. The whole loop, on one canvas — no second tools, no copy-paste between systems.
Any source. Any target. Any format. Composer's operators handle the integration tax — extract, transform, resolve, and load across every system you run.
Drag a Dataset Operator to extract. Add a Data Blend Operator to integrate sources. Cleanse and aggregate with the Transformation Operator. Map and load to your target with the MIL Operator. Wire them up — Composer generates the workflow and runs it on xAQUA's in-memory query engine. Version it in Git. Deploy to your K8s cluster.
── composer pipeline · risk_analytics ── [1] UDP Dataset Operator source: "postgres://risk.transactions" asset: "Query Asset · last_30d" ✓ extracted [2] UDP Data Blend Operator join: "INNER · on customer_id" with: "File Asset · customer_master.csv" ✓ blended [3] UDP Transformation Operator tasks: "filter, group_by, aggregate" engine: "in-memory query engine" ✓ transformed [4] UDP MIL Operator target: "snowflake.risk.scores" scd: "SCD-2 · history preserved" ✓ loaded ── deploy · main@a3f9c2 ── workflow RiskAnalytics.pipeline schedule 0 */6 * * * ✓ active k8s pod healthy ✓ green STATUS: GREEN · next run in 47m
Composer is a module of a unified platform — not a standalone pipeline product that needs its own catalog, its own quality engine, and its own lineage.
The AI Data Engineer is an xAQUA agent that lives inside Composer. Powered by Active Metadata from the semantic layer, the catalog, and the lineage graph, the agent understands your sources, your business definitions, and your governance rules. Ask in English; the agent composes the pipeline, configures every operator, validates contracts, and wires the workflow.
Promote ad-hoc work from Athyna — xAQUA's interactive data studio — into Composer with one prompt. Same semantic layer. Same catalog. Same governance. The agent wraps the recipe into a scheduled, monitored, Git-versioned production pipeline. You review, approve, and steer.
See the full AI Data Team →main/pipelines/members_dailySee Composer build a CDC pipeline from Salesforce to Snowflake — with entity resolution, quality gates, Git versioning, and K8s deployment — in under fifteen minutes.