Data Pipeline Infrastructure
ML models are only as good as their data. We build data pipelines that ingest, validate, transform, and store training and feature data reliably. No more stale data, silent schema changes, or transformation bugs corrupting your models.
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We implement, you ship. Async, documented, done in days.
Ingestion & Orchestration
We deploy Airflow or Prefect for batch orchestration with proper retry logic, SLA tracking, and failure alerting. Streaming pipelines use Kafka or Pulsar for real-time ingestion. Data sources get abstracted behind connectors so adding a new source doesn't require rewriting the pipeline. Backfill support lets you reprocess historical data when transformation logic changes.
Transformation & Processing
Transformations run in Spark, dbt, or pandas depending on data volume. Each transformation step is idempotent and testable in isolation. We implement incremental processing — only changed data gets reprocessed, cutting pipeline runtime by 10-100x for large datasets. Schema evolution handling ensures upstream changes don't silently break downstream consumers.
Data Validation
Great Expectations or custom validation checks run after every transformation: null checks, range validation, referential integrity, distribution analysis, and freshness guarantees. Validation failures halt the pipeline before bad data reaches your feature store or training pipeline. Failed checks log detailed context — which rows failed, what the expected distribution looked like, and suggested fixes.
Storage & Access Patterns
Data gets stored in a lakehouse architecture (Delta Lake, Iceberg, or Hudi) with time-travel support for reproducible training. Partitioning and compaction strategies optimize for your access patterns — training reads (full scan) versus feature lookups (point query). You get a data platform that serves both analytics and ML workloads without duplication.
Why Anubiz Engineering
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Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.