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Big Data Analytics Platform

Success Story

Building a Unified, Enterprise-Grade Big Data Analytics Platform

A scalable foundation enabling real-time insights, advanced analytics, and data-driven operations.

90%+ inventory accuracy

(up from 60%) through centralized data & real-time tracking.

Real-time

reporting & ML forecasting for pricing, delivery times, and route optimization.

Technologies

Kafka

Airflow

Java microservices

Teradata

Google BigQuery

Tableau

ML models

Event-driven architecture

Real-time data streaming

Expertise

Big data

Cloud

Microservices

EDA

Data pipelines

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Siloed and outdated data infrastructure

Data was scattered across multiple legacy systems with no unified data layer, resulting in fragmented reporting, inconsistent metrics, and limited real-time visibility across supply chain, inventory, and sales operations.

Poor data quality and slow decision cycles

Inconsistent and delayed data reduced trust in reports and forecasts, impacting inventory accuracy, pricing decisions, and logistics planning, and forcing teams to rely on manual workarounds instead of real-time insights.

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Enterprise-grade data architecture and engineering

Zoolatech specializes in building high-scale data platforms using event-driven architectures, robust pipelines, and cloud-ready designs that support advanced analytics and ML workloads.

End-to-end delivery with strong technical ownership

Our teams integrate seamlessly with enterprise environments, driving architecture, implementation, and optimization with a focus on reliability, performance, and long-term maintainability.

Zoolatech is a senior-heavy engineering firm with Silicon Valley roots and a Miami HQ, specializing in legacy modernization, system re-architecture, and AI deployment to drive long-term, compounding value.

2017

Year Founded

600+

Employees

96%

Client Satisfaction

Phase 1

Data capturing

The first step was capturing data from multiple, disconnected sources within the client’s legacy systems. Zoolatech specialists set up a unified system to collect data in real time, ensuring that all relevant information, such as sales transactions, inventory levels, and logistics data, was captured without delay.

Phase 2

Data streaming

Kafka’s event-driven architecture ensures that every data event, such as a sale, inventory update, or delivery status change, is immediately streamed to the data platform. This allows for instant, ongoing data flow rather than relying on slower batch processing.

Phase 3

Eventualization and centralization

As the data streams through Kafka, it is eventualized — meaning each significant business action or transaction is treated as a distinct event. These events are processed through Java-based microservices. By organizing data as events, the system provides granular visibility into every business transaction in real time.

Phase 4

Data processing

To manage and automate the processing of large volumes of data, Zoolatech used Airflow. Airflow orchestrates the cleaning, filtering, and transformation of raw data. This ensures that by the time data reaches the central warehouse, it is accurate, standardized, and ready for analysis.

Phase 5

Data storage and warehousing

Once processed, the data is stored in a centralized warehouse. This warehouse is based on Teradata (but it is going to be migrated later on to Google BigQuery for improved scalability and cost efficiency). The warehouse provides a single source of truth for all the client’s data, ensuring that insights are drawn from consistent, high-quality data.

Phase 6

Data analysis

Tableau was used to provide the client with easy-to-use, customizable dashboards. These dashboards allow business users to generate reports, analyze trends, and make informed decisions based on real-time, accurate data.

Modern analytics starts with unified data. This platform transformed disconnected systems into real-time intelligence that powers confident, data-driven decisions.

Core platform implementation

We provided the company with a team of strong and experienced Big Data engineers and Java engineers. The Zoolatech team has implemented a scalable and highly robust Big Data analytics platform that supports advanced analytics and machine learning models.

Data consolidation and integration

Data consolidation: Unifying fragmented data from multiple systems into a single platform.
Event-driven approach: Implementing real-time data streaming for better operational visibility.

Automation, optimization, and data quality

Automation and optimization: Building hundreds of robust data pipelines for reliable data flow and processing.
Enhanced data quality: Ensuring the integrity and accuracy of data to reduce errors and provide high-quality data for BI reports and ML models training.

Option	Risk	Mitigation
Inconsistent data from legacy systems	Fragmented legacy sources could introduced incomplete, conflicting, or low-quality data into the new platform.	Data validation rules and quality checks were integrated into every pipeline to ensure accuracy before data reached analytical layers.
High-volume processing and real-time load	Large, continuous data streams risked overwhelming infrastructure during peak operations.	Distributed, event-driven processing patterns and scalable components were implemented to handle surges and maintain system stability.
Operational complexity across teams	Coordinating BI, Big Data, and ML workflows across departments created risks of delays and dependency bottlenecks.	Clear ownership boundaries, automated orchestration, and standardized data processes enabled smoother collaboration.
Long-term scalability and cloud migration	Reliance on on-prem or legacy warehousing limited future growth and cost efficiency.	A future migration path to BigQuery was incorporated to ensure scalability, elasticity, and improved performance.

The new big data platform improved operational visibility, strengthened analytics capabilities, and enabled ML-driven optimization across the retailer’s value chain.

Consolidated reporting and data visibility

The scalable, event-driven analytics platform eliminated fragmented reporting and restored trust in data. The new architecture provided real-time access to previously siloed information, giving the company a unified operational view.

Actionable insights through advanced analytics

Tableau-powered dashboards enabled teams to generate accurate reports, analyze trends, and make informed decisions quickly, improving overall business agility.

ML-driven operational optimization

Machine learning was used to streamline logistics, optimize routes, forecast pricing, and accurately predict delivery times. These capabilities reduced delays, improved customer satisfaction, and supported stronger supply chain performance.

Improved inventory accuracy

Inventory accuracy increased from 60% to over 90% after integrating advanced scanning, real-time tracking, and centralized data processing. Stock levels, item movements, and product attributes could be monitored reliably across the network.

Overall business impact

The introduction of unified data and ML capabilities transformed decision-making across the organization. Enhanced logistics, pricing, and inventory visibility contributed directly to cost reduction and improved customer experiences.

Enterprise-wide confidence in data

Teams now rely on consistent, high-quality data to guide decisions, accelerating planning cycles and reducing operational uncertainty.

Faster innovation through unified analytics

With consolidated data and automated pipelines, the organization can roll out new insights, ML models, and reporting capabilities far more rapidly than before.