When should we use stream processing versus batch?

Use stream processing when you need low-latency insights, continuous aggregation, or event-driven ML. Batch is appropriate for large-scale historical processing, complex joins, and cost-optimized ETL. Many systems use hybrid approaches: stream for realtime updates and batch for backfills and heavy transforms.

How do you guarantee ordering and exactly-once semantics?

We design pipelines with idempotent consumers, deduplication, event- or ingestion-time windowing, and use transactional sinks or write-ahead logs when available. Where required, we adopt frameworks that support checkpointing and exactly-once delivery semantics and validate behavior under fault injection.

Stream & Batch Processing

Process real-time and historical data pipelines for analytics and AI

We design pipelines that handle streaming telemetry and large-scale batch workloads — with windowing, stateful processing, feature pipelines, and operational guarantees for correctness and scale.

Overview

Processing transforms raw events and historical records into actionable datasets. Whether you need streaming analytics, event-driven feature updates, or batch ETL for model training, we build pipelines that meet your latency, correctness, and cost requirements.

Core Capabilities

Stream Processing

Low-latency transforms, event-time/windowing, joins, and stateful operators for realtime analytics.

Batch ETL & Backfills

Cost-effective bulk processing, complex joins, and historical re-computation for training and audits.

State & Windowing

Managed state, window semantics, and late-arrival handling to ensure correct aggregations and analytics.

Exactly-once & Idempotency

Design patterns and platform choices to minimize duplicates and ensure correctness across failures.

Feature Pipelines for ML

Online and offline feature computation, consistency between training and serving, and feature stores.

Observability & Testing

Metrics, lineage, data-quality checks, canary runs, and replayability for robust operations.

Our approach

We align processing architecture with business outcomes: minimize end-to-end latency where it matters, use batch for cost-effective heavy transforms, and ensure reproducibility for analytics and ML. Trade-offs are documented and validated with performance and correctness testing.

Deliverables

Processing architecture (streaming, batch, or hybrid)
Windowing, state, and late-arrival strategies
Pipeline implementations, CI/CD, and replay support
Feature engineering pipelines and consistency guarantees
Monitoring, SLAs, and operational runbooks

Why partner with us

We’ve built production streaming and batch systems for analytics and ML. Our emphasis is on correctness, reproducibility, and operational simplicity so teams can derive value from data with confidence.

Design & delivery process

Discover

Map data sources, SLAs, and analytical/ML requirements.

Design

Choose streaming vs batch, windowing, state, and storage patterns.

Implement

Build pipelines, transforms, and feature stores with observability.

Validate

Test correctness, latency, throughput, and data quality.

Operate

Autoscale, monitor, and maintain pipelines with cost controls.