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Development of Scalable Big Data Processing Platform with Automated Workflow Orchestration

syberry.com

Business services

Information technology

Financial services

eCommerce

Challenges in Scaling Data Processing Automation

The startup struggles to balance manual data processing demands with long-term automation goals while handling large datasets (up to 1GB TXT files). Legacy systems require complete re-architecture to transition from Python-based Airflow to Java-based Cadence workflow orchestration.

About the Client

A data analytics startup seeking to automate end-to-end data processing for enterprise clients

Platform Modernization Goals

Develop automated data processing pipeline eliminating manual intervention
Implement scalable architecture supporting enterprise-level data volumes
Create unified UI/UX across multiple development teams
Establish robust workflow prioritization system

Core System Capabilities

Adaptive data extraction with edge case handling
Centralized metadata API for cross-team integration
Priority-based workflow gatekeeper
Unified user interface with consistent UX
Automated data validation and pattern recognition

Technology Stack Requirements

Java 13

Spring Boot

Docker

GCP

CassandraDB

Gradle

JUnit

Mockito

JMeter

Hibernate

Uber Cadence

Python

System Integration Needs

Google Cloud Platform services
Apache Airflow legacy components
Uber Cadence workflow engine
Third-party data source APIs

Operational Requirements

Horizontal scalability for petabyte-scale data processing
Sub-8-hour processing SLA for 1GB+ datasets
99.99% system availability
Role-based access control (RBAC)
Automated failover mechanisms

Business Transformation Potential

Enables enterprise clients to process massive datasets with 80% reduced manual effort, accelerates time-to-insight by 70%, and positions the startup as a market leader in automated data processing, potentially increasing valuation by $100M+ within 18 months.