CognitOps: Scaling Secure SaaS

Success Story

Boosting Scalability and Data Security

Building a unique, hyper-scalable, multi-tenant architecture to process petabytes of data while ensuring bulletproof security and customer isolation for exponential SaaS growth.

10X faster

customer onboarding year-over-year.

Petabyte-scale

architecture with shared core & isolated data privacy.

Technologies

React

PostgreSQL

Red Hat

Google Cloud

Apache Spark

Redis

Docker

Kubernetes

JavaScript/Node.js

AWS

TypeScript

MuleSoft

Next.js

RabbitMQ

Java

Spring

Elastic

Firebase

Figma

Atlassian Tools

Kafka

Expertise

Microservices

Data analysis

Cloud

Back-end engineering

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Exponential scalability

Scale the system to support the exponential SaaS customer base growth and speed up the onboarding process for new customers.

Data privacy and security

Ensure bulletproof data privacy and client experience compartmentalization.

Performance isolation

Ensure that the increased load or data quality issue for some customers is not affecting others.

Cost and complexity control

Ensure that the complexity of the solution and maintenance cost would both remain under control as the quantity of customers increased, and collected data and customizations grew exponentially.

Have a Similar Problem?

Accelerate your SaaS growth and manage petabytes of data securely with custom solutions.

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Architectural design

Zoolatech was chosen for its expertise in back-end engineering and cloud engineering & DevOps, critical for designing a unique architecture capable of managing exponential SaaS growth without compromising performance.

Data security and handling

Our team was selected for its proficiency in data analysis and designing solutions that ensure bulletproof data privacy and client experience compartmentalization across petabytes of sensitive supply chain data.

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

Architecture creation

The team designed a unique, multi-tenant architecture capable of collecting and storing petabytes of data while ensuring customer isolation.

Phase 2

Data normalization

A significant effort was dedicated to creating a specialized "Normalizer" utility for processing incomplete or broken external data feeds.

Phase 3

Data modeling

We executed the creation of a generic data model applicable to all customers, standardizing data structures regardless of the original incoming format.

Phase 4

Onboarding optimization

The newly created architecture was deployed to facilitate the smooth and rapid onboarding of new customers, irrespective of their existing data structures or business processes.

"We took a bet when we decided to partner with Zoolatech and it’s been paying off in spades. In particular, we’ve been impressed with the quality of teammates and the speed at which Zoolatech has been able to source and land engineers that could help our internal team."

Alex Ramirez

CEO, CognitOps

Architecture and isolation

The team created a unique architecture for collecting and storing petabytes of data, which allowed analyzing it to generate meaningful insights and statistics. Customers were isolated from each other while the code and core functions were shared.

Security and feature reusability

The design guarantees data privacy and security for each customer. Whenever a new feature was added for one customer, it was possible to reuse it for others.

The normalizer

Many efforts were spent to create a so-called “Normalizer” which helps to use data that might not be complete or could be broken.

Data standardization

It was even possible to make a generic data model for all customers, regardless of the data that is received.

Onboarding solution

Finally, the newly created architecture allowed smooth onboarding of new customers regardless of the data structures and business processes they are using.

Customer isolation achieved

Customers were isolated from each other while the code and core functions were shared.

Data standardization enabled

The generic data model allowed for smooth onboarding of new customers regardless of the data structures and business processes they are using.

Petabyte-scale solution

The architecture created was unique for collecting and storing petabytes of data.

Mutual client value

By creating a solution that ensures an extreme level of data privacy and security, the team leveraged the platform’s anonymized knowledge for the mutual benefit of all the platform clients.

Analytic capability

The architecture allowed for the analysis to generate meaningful insights and statistics, transforming petabytes of raw logistics data into predictive intelligence and actionable supply chain optimizations for Fortune 500 decision-makers.