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Data Engineering

Big Data Analytics Platform

Case study: Enterprise data lake processing 300TB+ daily with Kafka, Spark, and Delta Lake. Real-time analytics and ML pipelines with 99.9% reliability.

300TB+
Daily Volume
99.9%
Reliability
Real-time
Analytics
Real-time stream processing
Machine learning pipelines
Enterprise-grade reliability
Apache Kafka Apache Spark Vector.dev AWS S3 Delta Lake Python Airflow
Big Data Analytics Platform - Data Lake Architecture with Kafka and Spark
Case Study

Project Overview

Our client was struggling with a fragmented data infrastructure. Data was siloed across multiple systems, analytics were slow and inconsistent, and the existing infrastructure couldn’t scale to meet growing data volumes.

We designed and built a modern data lake platform that unified data across the organization while enabling real-time analytics and machine learning at scale.

Architecture Highlights

The platform was built on modern data engineering principles:

  • Streaming Layer: Apache Kafka for real-time data ingestion and event streaming
  • Processing Layer: Apache Spark for batch and streaming data processing
  • Storage Layer: Delta Lake on S3 for reliable, ACID-compliant data storage
  • Orchestration: Apache Airflow for workflow management and scheduling
  • Analytics: Self-service analytics platform with SQL and notebook interfaces

Operational Excellence

The platform was designed for operational excellence from day one:

  • Automated data quality monitoring and alerting
  • Comprehensive observability with metrics, logs, and traces
  • Self-healing capabilities for common failure scenarios
  • Disaster recovery with cross-region replication

!
Challenges

  • Processing massive data volumes in real-time
  • Ensuring data quality and consistency
  • Building reliable ML pipelines
  • Managing costs at scale
  • Supporting diverse analytics use cases

Solutions

  • Designed scalable streaming architecture with Kafka
  • Implemented Delta Lake for ACID transactions
  • Built automated data quality frameworks
  • Created self-service analytics platform
  • Optimized storage costs with tiered architecture

Results & Impact

300TB+ daily data processing

99.9% data pipeline reliability

Real-time analytics capabilities

60% reduction in time-to-insight

Unified data platform for analytics

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