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Data Pipelines

We design and build production-grade data infrastructure — from real-time streaming with Kafka to batch ETL with Airflow, data lakes on S3/GCS, and modern warehouses like Snowflake and BigQuery. Data you can trust, when you need it.

Source Kafka Sink 1.2M events/sec p99 latency: 12ms

What We Deliver

⚡

Real-Time Streaming

Event-driven architectures with Kafka and Kinesis. Process millions of events per second with exactly-once semantics. Real-time analytics and instant data availability.

  • Kafka / AWS Kinesis cluster setup
  • Event-driven architecture design
  • Stream processing with Flink / Spark Streaming
  • Exactly-once delivery semantics
  • Schema registry & event versioning
🔄

Batch ETL Pipelines

Robust batch processing with modern orchestration. Incremental loads, data quality checks, and transformation pipelines that handle petabytes reliably.

  • Airflow / Dagster orchestration
  • dbt transformations & modeling
  • Incremental processing patterns
  • Data quality checks & validation
  • Automated retry & alerting
🗄️

Data Lake / Warehouse

Modern lakehouse architecture combining the flexibility of data lakes with warehouse performance. Cost-effective storage with blazing-fast queries.

  • S3 / GCS data lake architecture
  • Snowflake / BigQuery / Redshift setup
  • Lakehouse with Delta Lake / Apache Iceberg
  • Partitioning & clustering optimization
  • Cost-effective storage tiering
🎯

Data Orchestration

Production-grade workflow orchestration with proper monitoring, alerting, and failure handling. DAGs that your team can understand and maintain.

  • Airflow / Dagster deployment
  • Workflow monitoring & observability
  • Dependency management
  • Failure handling & recovery
  • SLA tracking & alerting
✅

Data Quality & Governance

Trust your data with automated quality checks, schema evolution, and full lineage tracking. Data contracts between producers and consumers.

  • Great Expectations integration
  • Data contracts & SLAs
  • Schema evolution management
  • Data lineage & impact analysis
  • Catalog integration (DataHub, Atlan)
📊

Analytics Infrastructure

Self-service analytics that empowers your team. Semantic layers, metrics definitions, and BI tool integration that scales with your organization.

  • BI tool integration (Metabase, Looker)
  • Semantic layer setup
  • Metrics layer & definitions
  • Self-service analytics enablement
  • Dashboard performance optimization

Our Tech Stack

Streaming

Apache Kafka, AWS Kinesis, Apache Flink

Orchestration

Apache Airflow, Dagster, Prefect

Transformation

dbt, Apache Spark, Python

Warehouses

Snowflake, BigQuery, Redshift

Lakehouse

Delta Lake, Apache Iceberg, Hudi

Quality

Great Expectations, dbt tests, Monte Carlo

Typical Engagement

Week 1

Data Audit & Architecture

We audit your current data infrastructure, map data sources and flows, and design the target architecture. You get a detailed data architecture document and implementation plan.

Week 2-3

Pipeline Development

We build out the data pipelines, set up orchestration, and implement transformations. Everything is tested, documented, and follows data engineering best practices.

Week 4

Testing & Deployment

We deploy to production, set up monitoring and alerting, and validate data quality. Your team gets full documentation and training on the new infrastructure.

Ready to Build Reliable Data Pipelines?

Get a free technical briefing. We'll review your current data infrastructure and provide a detailed roadmap for your data pipeline architecture.

Book a Call