How Valoriz Unified Multi-Cloud Data Architecture
Our client needed dedicated expert teams managing all cloud-based data integration and engineering operational requirements.
Industry
Cloud Computing
Overview
Services Provided
The client required comprehensive data engineering consulting services to consolidate fragmented datasets across on-premises and cloud environments while supporting diverse engineering teams with rapidly changing business requirements.
We delivered an enterprise data lake strategy using Azure-native technologies and composite team expertise, enabling scalable data processing, automated DevOps workflows, and flexible architecture for continuous business evolution.
Cloud Consulting Services
Azure Cloud
Services Provided
The Goal
Our customer needed an expert team to manage their cloud-based data integration and engineering, while ensuring scalability, reliability, and agility.
Data Integration
The data was spread across on-premises and cloud systems. The goal was to unify it into a reliable, scalable foundation so every team could access the same trusted source.
Agile Delivery
The teams needed data delivered quickly, not in weeks. The aim was a cloud-native, agile model that made data faster, more flexible, and responsive to constant business changes.
Challenges
Data Complexity
The datasets were located in both on-premise systems and the cloud. This made it hard to combine, process, and ensure consistent access for multiple engineering teams.
Rapid Changes
The businesses kept changing quickly. This required a flexible engineering model that could respond to new needs without slowing down delivery.
fragmentation
scale
changes
consistency
Process
DetermineWe start by understanding your needs, challenges, and assumptions to lay a strong foundation for your project. This ensures a smooth ecommerce website development services journey.
STEP 1
STEP 2
DescribeFrom project scope to risk assessment and milestones, we map out every detail, creating a clear roadmap as a leading ecommerce development agency for seamless execution.
DesignWith wireframes, prototypes, and a user-centric approach, we craft intuitive UI/UX and robust system architecture, enhancing your store with best ecommerce hosting services.
STEP 3
STEP 4
DevelopEngineering, API integrations, QA, and security come together to build a high-performing, secure, and scalable solution with expert Ecommerce web development.
DeployFrom environment setup to product deployment and migration, we ensure a smooth launch with ongoing support, backed by reliable best ecommerce hosting services.
STEP 5
Solution
We put together a team of cloud architects, integration specialists, and data engineers. By using Azure-native practices, modern data tools, and agile DevOps, we created a flexible and scalable framework that could keep up with business changes.
Scalable Data Integration
Scalable Data Integration
Problem
The customer’s data was scattered across on-premise systems and several cloud platforms. This made it tough to consolidate, process, and deliver data consistently.
Solution
We combined the datasets using Azure Data Lake, ADF, and ISE. This established a single, scalable foundation that simplified access and enhanced collaboration among engineering teams.
Flexible Engineering Model
Flexible Engineering Model
Problem
Frequent business changes meant the existing data engineering setup was too rigid to meet new requirements.
Solution
We introduced a cloud-native agile model supported by DevOps automation. This made data delivery faster, more adaptable, and reliable, keeping engineering teams in sync with changing priorities.
Reliability and Performance
Reliability and Performance
Problem
The existing data processes were inconsistent, leading to delays, errors, and a lack of trust in the information given to teams.
Solution
We used Azure Databricks and automated validation pipelines to ensure reliability and accuracy. This improved performance, reduced errors, and gave decision-makers confidence in the data they relied on.
Technologies used
Azure Cloud
Leveraged Azure Cloud for scalable and secure infrastructure.
Azure DevOps
Enabled efficient deployment with Azure DevOps.
Azure Databricks
Used Azure Databricks for unified data analytics and accelerated data processing.
Azure Data Factory
Implemented Azure Data Factory to orchestrate and automate data integration workflows efficiently.
Java Spring Boot
Developed backend services using Java Spring Boot for reliable application performance.
MongoDB
Utilized MongoDB for flexible, high-performance data storage and real-time access.
SQL Server
Integrated SQL Server for structured data management and optimized query performance.
40%+
Reduced Integration
Effort
2x+
Increased Deployment
Speed
95%+
Uptime For
Data Services
Impact
Faster Data Access
Engineering teams may access aggregated datasets 50% faster, minimizing analytical delays and allowing for faster responses to business requirements across departments.
Higher Deployment Velocity
With the cloud-native agile process and DevOps automation, release cycles were doubled, allowing teams to deploy updates and new data features notably faster.
Improved Data Reliability
Data mistakes were decreased by 30% thanks to automated pipelines and Azure-native tools, resulting in consistent, correct information for engineering and business teams to make decisions.
Operational Efficiency Gains
Manual integration efforts decreased by 40%, allowing the data engineering staff to focus on higher-value work rather than monotonous maintenance.
Seamless Scalability
The system enabled rising datasets without sacrificing speed, allowing the company to easily handle peak workloads and plan for future business growth.
Conclusion
This engagement showed us how a cloud-native approach changed complex data challenges into reliable and business-ready outcomes.
Stronger Foundation
By bringing together fragmented datasets into a single cloud platform, the customer gained a dependable foundation that improved access, reduced errors, and made sure the data was always trustworthy and current.
Future Ready
With agile processes, DevOps automation, and expandable architecture, the customer is now prepared to handle future business needs, respond quicker, and make confident data-driven decisions.
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