Enterprise Big Data
Engineers & Stream Architectures
We build petabyte-scale distributed data processing platforms, real-time Apache Kafka streaming engines, and Databricks PySpark lakehouses engineered for sub-second insights and enterprise AI models.
Big Data Engineering Metrics
Purpose-Built Distributed Systems
Tailored big data engineering for petabyte storage, Kafka streaming, Databricks PySpark, and ML feature stores.
Petabyte Distributed Storage & Compute
Architect distributed HDFS, S3, & Delta Lake clusters for high-volume petabyte storage and parallel processing.
Real-Time Event Ingestion (Kafka / Flink)
Process millions of events per second with sub-second latency for fraud detection & live telemetry.
PySpark & Databricks ETL Engines
High-throughput PySpark jobs transforming massive unstructured logs, clickstreams, & sensor data.
IoT & Telemetry Stream Processing
Ingest millions of IoT device telemetry streams, sensor feeds, & location vectors with zero data loss.
Enterprise Data Governance & Lineage
Centralized data cataloging (Atlan / Apache Atlas), lineage tracking, & PII compliance policies.
Big Data ML Feature Store Pipelines
Power Machine Learning models with low-latency feature stores (Feast / Hopsworks) for real-time inference.
Big Data Engineering Practice
From PySpark and Databricks to Kafka event streaming, S3 Delta Lakes, and ML feature stores.
Petabyte Distributed Storage & Compute
Break through single-database storage limits. We engineer distributed cloud storage (AWS S3 / Azure ADLS / GCP GCS) combined with Apache Spark and Databricks clusters capable of querying petabytes in seconds.
Big Data SLA Standards
- Petabyte-scale distributed compute via PySpark & Databricks
- Sub-100ms event streaming latency via Apache Kafka / Flink
- Atlan data cataloging & OpenLineage data tracking
- 100% intellectual property & PySpark pipeline ownership
How We Engineer Big Data
A structured 6-stage delivery lifecycle from data volume sizing to Kafka streaming, PySpark pipelines, and 24/7 SRE support.
Big Data Architecture Audit & Sizing
We assess raw data volumes, stream ingestion rates, and compute requirements to model cluster topology.
Data Lakehouse & S3 Storage Design
Architect cloud storage buckets (S3 / ADLS) and Databricks Delta Lake Medallion tables.
Kafka Stream & PySpark Pipeline Setup
Build Apache Kafka event streams and PySpark jobs to ingest and transform unstructured logs.
Data Lineage & Governance Enforcement
Implement Atlan data cataloging, PII masking, and Great Expectations automated data quality checks.
ML Feature Store & BI Integration
Connect feature stores to production ML models and configure Snowflake / BigQuery query layers.
24/7 Cluster Monitoring & FinOps SLA
Monitor PySpark cluster compute, tune spot instance policies, and provide 24/7 SRE cluster support.
Big Data Tech Stack
Big Data Engineering FAQ
Answers to common questions regarding petabyte scale, Medallion architecture, and cloud spot instance savings.
Relational databases struggle when data volumes exceed terabytes or involve unstructured JSON logs. Big Data frameworks (Apache Spark, Databricks, Kafka) split storage and compute across hundreds of parallel nodes, allowing petabytes of data to be processed concurrently.
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