Key Highlights

  • Addressed legacy infrastructure constraints and data latency challenges in the client’s on-premises data warehouse, hindering scalability, agility, and timely decision-making.
  • The client faced limitations in scalability, flexibility, and agility with on-premises data warehouse, coupled with data latency issues impacting market responsiveness and operational efficiency.
  • We migrated on-premises data to AWS S3 using AWS DMS, efficiently structuring it into delta tables with Databricks, and ensuring real-time data consistency through CDC.
  • Streamlined operations with enhanced data accessibility and reliability, empowering stakeholders with actionable insights through Tableau, and realized significant cost savings.

Problem Statement

01

Legacy infrastructure constraints: On-premises data warehouse limitations hindered scalability, flexibility, and agility, leading to high management costs.

02

Data latency challenges: Delays in stored data accessibility hampered timely decision-making, impacting market responsiveness.

03

.Integration and Infrastructure Issues: Challenges with system integration, non-cloud apps, and infrastructure cause operational fragmentation, downtime, and user dissatisfaction.

Solution Overview

01

OptiSol partnered with the Canadian fertilizer company to transition on-premises data to AWS S3 Bucket via AWS DMS, streamlining data management.

02

Leveraging Databricks notebooks, data was structured and transformed for efficient storage in delta tables on AWS S3, enhancing accessibility.

03

AWS DMS facilitated Change Data Capture (CDC) from the on-premises database, ensuring data consistency and minimizing processing overhead.

04

Tableau dashboards were developed using Data Lake-derived Sales Report data, empowering stakeholders with actionable insights.

05

The cloud-native solution ensured scalability, manageability, and improved performance, enabling cost savings and fostering innovation opportunities.

Business Impact

01

Efficient Data Migration: On-premises data was migrated to AWS S3 using AWS DMS, ensuring simplified and consistent data management.
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Improved Operational Efficiency

02

Improved Data Handling: Databricks transformed and structured the data into delta tables on AWS S3, enhancing accessibility and ensuring robust reliability.
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Enhanced Data Structuring

03

Cloud Analytics: The cloud-enabled Tableau dashboards for sales reports improved performance, scalability & cost efficiency, providing valuable insights.
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Improved Cost Efficiency

ABOUT THE

Project

This project implements a transformative solution for a Canadian fertilizer company facing challenges with legacy on-premises data infrastructure. By migrating their data to AWS S3 via AWS Data Migration Service (DMS) and leveraging Databricks for efficient data structuring and transformation, we streamlined their operations and enhanced data accessibility. Change Data Capture (CDC) ensured data consistency, while Tableau dashboards from Data Lake-derived Sales Report data empowered stakeholders with actionable insights. Our cloud-native approach not only improved scalability, manageability, and performance but also reduced costs and fostered innovation, revolutionizing their data handling and operational efficiency.

Tech Stack

FAQ's

Why was AWS S3 chosen instead of other cloud storage platforms?

AWS S3 was used as the target storage for data migration, enabling efficient structuring into delta tables with Databricks. It improved accessibility, reliability, and scalability while delivering significant cost savings for the client.

How does AWS DMS handle large-scale data migration without downtime?

AWS DMS facilitated seamless migration from the on-premises database to AWS S3. By using Change Data Capture (CDC), it ensured real-time data consistency and minimized processing overhead during the migration process.

How is Change Data Capture (CDC) different from traditional data synchronization?

CDC captures only incremental changes in real-time, avoiding full data reloads. Unlike traditional synchronization, it reduces latency, minimizes processing overhead, and ensures consistent, up-to-date data availability across cloud and on-premises systems.

How did cloud adoption impact scalability and agility for the fertilizer company?

Cloud adoption eliminated infrastructure constraints, allowing on-demand scalability and faster data accessibility. It improved agility by enabling real-time insights, reducing delays in decision-making, and supporting seamless integration with advanced analytics tools and services.

Can similar solutions be applied to other manufacturing industries beyond fertilizers?

Yes, similar cloud-based solutions can be applied across industries like pharmaceuticals, automotive, and energy. Any sector relying on data-driven insights can benefit from improved scalability, efficiency, and actionable intelligence enabled by modern cloud infrastructure.

Testimonials of Our Happy Clients

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