IOblend

feature store

without the store

IOblend: The “Feature Store without the Store” for MLOps

An Alternative Way to run MLOps

Traditional feature stores promise governance, freshness, and reuse of ML features—but they also add another infrastructure layer, more APIs to manage, and new operational risks.

IOblend changes the game by embedding feature store capabilities directly into your MLOps pipelines, without forcing you to adopt separate serving tiers or vendor-hosted APIs.

With IOblend, your data lake or warehouse is the feature store. Use Snowflake, Azure Synapse, Redshift, Oracle, Databricks, etc

IOblend is packed with powerful features right out of the box, such as windowed and decayed aggregations, chained aggregations, joins, deduplication, Slowly Changing Dimensions (SCD I/II), MDM merges, schema drift management, data quality, real-time drift, error handling and disaster recovery.

Feature Engineering In Action

See how you can architect, maintain and govern your ML features using any data warehouse or lake as a store.

Sync your features between offline and online stores and serve the data at ultra low latencies and high throughput.

We take it a step further still: act upon the output of your ML models by embedding AI agents into the data pipeline directly (e.g. Langchain).

Watch Product Demo

Rethinking the Feature Store with IOblend

Instead of bolting on Redis, Dynamo, or Feast-style serving layers, IOblend makes your MLOps pipelines the feature store

Feature definitions

IOblend pipeline specs – JSON playbooks. Sources + transforms + sinks. Sharable and CI/CD compliant.

Feature registry

Record level lineage + metadata catalogue, exportable/synced to e.g. Postgres or integrated into your app’s discovery UI.

Offline store

Your lake/warehouse: Delta, Iceberg, Hudi, Snowflake, BigQuery, Databricks or any other cloud or on-prem lake/warehouse.

Online serving

P99 low-latency queries directly against continuously updated warehouse or lake tables, with optional in-process application-layer caching for accelerated inference and analytics

Freshness & correctness

Guaranteed by IOblend’s streaming engine (watermarks, CDC, late arriving data retractions, disaster recovery)

From Features to AI Agents

IOblend allows you to serve your feature set to your inference model, capture the output from the inference and then generate AI agents within IOblend to action that inference – a complete end to end Agentic system

The result: your existing warehouse or lake is the store. No new infra. No vendor lock-in.

The Role IOblend Already Plays in MLOps

Continuous streaming engine

Turn Spark into a true streaming platform (no micro-batches). Your feature pipelines run with P99 freshness (>1m TPS, network-bound) —critical for real-time ML.

Governance baked in

IOblend tracks record-level lineage, schema evolution, CDC changes, and drift automatically. That’s the registry work a feature store normally builds from scratch.

Runs where you run

Deploy on desktop, on-prem, or in cloud VPC. No external APIs, no vendor-hosted serving tiers—everything stays inside your boundary.

Multi-mode ingestion

Support for batch, streaming, and CDC ingestion. Backfill historical data, keep state fresh, and guarantee point-in-time correctness automatically.

Low code / no code feature development

Use SQL or Python for data transformations to handle your business rules, specific quality policies and other constraints.

IOblend will automatically build, run and manage efficient Spark pipelines in the background for you.

CI/CD ready

Automate, integrate, and deploy your data flows at the speed of DevOps

Where IOblend Feature Factory Delivers Value

Real-Time Personalisation

Deliver tailored recommendations or search results by serving fresh features directly from continuously updated tables.

Fraud & Risk Management

Detect anomalies faster with sub-second feature freshness—no lag from batch updates or stale lookups.

Operational Analytics

Power dashboards, KPIs, and monitoring tools with governed, real-time features that stay accurate as data evolves.

Predictive Maintenance

Continuously track IoT and sensor events, generating live features to anticipate failures before they happen.

Customer 360 Views

Merge data across systems (CRM, transactions, interactions) into a single governed, continuously refreshed view of each customer.

ML Model Training & Inference

Use the same features in both offline training and online inference—ensuring consistency, correctness, and faster deployment cycles.

Edge & Air-Gapped Deployments

Deploy features in constrained environments with snapshot bundles or local caching, without needing cloud-based feature stores.

What This Buys You in MLOps

Feature Store value without new infra

Definitions, governance, freshness, and correctness—all inside IOblend and your warehouse.

Zero external dependency

No IOblend-hosted APIs in the serving path.

Scales to any environment

Works in the cloud, on-prem, edge, even air-gapped deployments.

Unified DataOps + MLOps

One solution for migration, integration, and feature engineering.

Dataflow Architecture Using IOblend

Ingest

CDC from operational DBs + Kafka, Kinesis, IoT event streams.

Process (streaming)

Windowed and decayed aggregations, chained aggregations, joins, deduplication, Slowly Changing Dimensions (SCD I/II), MDM merges.

Materialise

Continuous upserts into Iceberg, Delta, Hudi, Snowflake, BigQuery table or even flat files – whatever your requirement. IOblend will maintain the assests automatically.

Consume

ML inference, BI, and analytics can query those tables directly—with optional caching or vector lookups for ultra-low latency.

Ask Us How IOblend Can Help Your ML and AI Analytics

Request an in-depth technical discussion and demo today.