Big Data Analytics with Hadoop

Nov 2018 • Competitor Product Change Dashboards

Implemented global competitor product change dashboards processing 6B+ monthly records—powering pricing & inventory intelligence across 32 countries.

6B+ monthly records
83% turnaround reduction
$150K annual savings
99% efficiency gain

Overview

Implemented competitor product change dashboards using Hadoop-based big data solutions for a leading UK-based electronic components manufacturer and distributor operating across 32 countries. The solution addressed challenges in managing massive monthly data volumes (6+ billion records), delivering actionable competitive intelligence for pricing and inventory strategies at a global scale.

Description

The client sought to efficiently analyze competitors pricing trends to support new and existing product lines. They faced:

  • Large-scale data processing challenges due to high data volume, velocity, and variety.
  • Manual, time-intensive data handling.
  • Limitations in existing BI architecture hindering timely, holistic reporting on global competitor KPIs (price changes, inventory, trends, matched articles).

Key Challenges

  • Manual and slow data processing.
  • Difficulty executing competitive analysis with massive, diverse data sets.
  • Inability to quickly analyze historical trends involving price and product category changes.
  • Complications in meeting critical deadlines (ETAs).

Solution Highlights

  • Designed and deployed a 6-node Hadoop cluster (20 cores, 150GB RAM, 2TB storage) for scalable ingestion & processing.
  • Transitioned storage to optimized Hive tables (row-columnar) for efficient retrieval.
  • Developed ETL logic using Apache Pig automating integration across systems.
  • Automated end-to-end workflows from raw extraction to interactive reporting.
  • Collaborated with BI teams to implement Tableau dashboards backed by real-time Hive aggregates.

Results & Impact

  • Reduced dashboard & processing turnaround time by 83%.
  • Deployed a scalable Pricing Data Warehouse managing 12M+ inventory items & 43 competitors globally.
  • Enabled analysis of six months (~6B records) at inventory level—previously unmanageable.
  • Decreased report lead time from 3 weeks to 1 day.
  • Batch processing supports all global markets (DE, FR, GB, IT, JP, CN) simultaneously.
  • Automated workflows saved ~3 weeks manual effort monthly.
  • Improved process efficiency by 99%, eliminating manual file handling.
  • Achieved $150K annual savings on processing, storage & analytics.
  • Freed 2 FTEs via automation.

Tech Stack

  • Hadoop cluster (6-node)
  • Hive (row-columnar tables)
  • Apache Pig ETL workflows
  • Tableau dashboards
  • Automated batch orchestration

Platform Visual

Hadoop big data analytics illustration