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AI Data Engineering

Describe It. Ship It.

Build production data pipelines with natural language. Apache Spark, real-time streaming, CDC, and intelligent orchestration—all powered by AI agents that optimize continuously.

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Natural Language to Pipeline

Describe your data flow in plain English. Watch AI generate production-ready Spark code.

Natural Language to Pipeline
Describe your data flow in plain English. AI generates production-ready Spark, SQL, or Python code.
"Ingest customer events from Kafka, dedupe by user_id, aggregate hourly, write to Iceberg"
Autonomous Optimization
AI agents continuously analyze pipeline performance and apply optimizations—partitioning, caching, shuffle tuning.
Reduced job runtime by 47% through automatic partition pruning
Smart Code Generation
Generate data contracts, schema migrations, and test cases from natural language or existing data samples.
Auto-generated 23 data quality checks from schema analysis

Enterprise-Grade Data Engineering

Every capability you need for batch, streaming, and CDC—unified in one platform.

Apache Spark Native
10x faster
First-class Spark support with Velox acceleration. Run batch and streaming on the same unified engine.
Real-Time Streaming
Sub-second latency
Kafka and Flink integration for sub-second latency. Process millions of events with exactly-once semantics.
Change Data Capture
Zero-impact CDC
Native CDC from databases, SaaS apps, and mainframes. Incremental ingestion without full table scans.
Intelligent Orchestration
Self-healing pipelines
DAG-based scheduling with dependency awareness, automatic retries, and SLA monitoring built-in.

Every Pipeline Pattern

From batch ETL to real-time streaming to ML feature pipelines—all on one platform.

Batch ETL
Scheduled transformations with Spark
Spark
dbt
Trino
Streaming Ingestion
Real-time event processing
Kafka
Flink
Spark Streaming
CDC Replication
Database change capture
Debezium
Kafka Connect
Airbyte
ML Pipelines
Feature engineering & training
Spark MLlib
Ray
Feast

Why AI Data Engineering?

See how we compare to traditional data engineering tools

Pipeline Definition
Streaming Support
CDC Integration
Optimization
Orchestration
Traditional Tools
Code-only or limited visual
Add-on or separate tool
Third-party tools required
Manual tuning required
Separate Airflow/Dagster
With Clarity
Natural language + visual + code
Native Kafka, Flink, Spark Streaming
Built-in, zero-impact capture
AI-driven, autonomous
Integrated DAG scheduling

One Control Plane. Any Data Plane.

Kubernetes-native orchestration across all environments. Open standards, zero lock-in.

Unified Batch & Stream
One API for all processing modes
K8s-Native Scaling
Elastic compute per pipeline
Git-First Workflows
Version control & CI/CD built-in
200+ Connectors
Sources and sinks out of the box

Ready to get started

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