Data & Streaming

Data platforms for real-time decisions

We connect streaming, batch, and analytics into a pipeline that stays resilient and scalable.

Data & Streaming

Overview

Streaming platforms deliver data in real time. We build pipelines that ingest, transform, and serve data reliably.

What we deliver

Event streaming

Kafka architecture, topics, retention, and scaling.

Batch & ETL

Robust data processing for analytics and reporting.

Data quality

Validation, monitoring, and ownership models.

Governance

Access models, compliance, and data catalogs.

Typical use cases

  • Real-time analytics and event processing
  • Data platforms for machine learning
  • Streaming IoT or sensor data
  • Data lake / lakehouse architectures
  • Reporting across business units

Process

Discovery

Capture sources, volume, and SLA requirements.

Pipeline build

Integrate streaming, ETL, and monitoring.

Operations

Improve stability, cost, and data quality over time.

FAQ

Do we need Kafka? v
For real-time, high-throughput use cases, Kafka is often a strong choice.
How do you ensure data quality? v
With validation, monitoring, and ownership models.
Batch or streaming? v
Often a hybrid approach works best based on the use case.
Which tools do you integrate? v
Kafka, Spark, Flink, Airflow, dbt, and cloud-native services.