Real-time Analytics and Data Mesh Implementation for Personalized Learning Paths

Medium Priority
Data Engineering
Online Learning
👁️11500 views
💬555 quotes
$50k - $150k
Timeline: 16-24 weeks

Our enterprise company is seeking a comprehensive data engineering solution to enhance the delivery of personalized learning experiences in our Online Learning & MOOCs platform. By leveraging real-time analytics and implementing a data mesh architecture, we aim to optimize content recommendation engines, improve learner engagement, and streamline data operations.

📋Project Details

We are an enterprise-level online learning platform looking to transform our data architecture to support personalized learning experiences. The project focuses on implementing a data mesh architecture and utilizing real-time analytics to enhance our recommendation systems. The current monolithic data infrastructure limits our agility in responding to diverse learner needs and content trends. By adopting a data mesh approach, we aim to decentralize data management, allowing domain teams to own and operate their respective data pipelines independently. Key technologies will include Apache Kafka for event streaming, Spark for real-time analytics, and Airflow for orchestrating data workflows. Additionally, we'll use dbt and Snowflake to enable scalable transformations and queries, with Databricks providing the collaborative environment for data scientists. Our goal is to create a robust, scalable system that supports MLOps for continuous improvement of our recommendation algorithms, enhancing learner satisfaction and completion rates. The successful execution of this project will position us as a leader in personalized education delivery.

Requirements

  • Experience with real-time data processing
  • Proficiency in implementing data mesh architecture
  • Knowledge of MLOps best practices
  • Ability to integrate diverse data sources
  • Expertise in data observability and monitoring

🛠️Skills Required

Apache Kafka
Spark
Airflow
dbt
Snowflake

📊Business Analysis

🎯Target Audience

Our primary users are students and professionals seeking customized learning experiences, as well as educational institutions looking to integrate advanced learning technologies.

⚠️Problem Statement

Our current data infrastructure is cumbersome and limits our ability to deliver personalized learning content in real time, impacting learner engagement and satisfaction.

💰Payment Readiness

There is strong market demand for personalized education solutions due to competitive advantages and significant improvements in student outcomes, motivating investment in cutting-edge data architectures.

🚨Consequences

Failure to address this issue may result in decreased learner engagement, higher dropout rates, and loss of market position to competitors offering advanced personalized learning options.

🔍Market Alternatives

Current alternatives include basic content recommendation systems and static analytics reports, which do not meet the growing demand for dynamic and personalized learning plans.

Unique Selling Proposition

Our project uniquely combines a data mesh approach with real-time analytics, enabling rapid adaptation to learner preferences and trends, significantly enhancing the learning experience.

📈Customer Acquisition Strategy

We will leverage strategic partnerships with educational institutions and conduct targeted marketing campaigns to showcase the enhanced personalization capabilities of our learning platform.

Project Stats

Posted:July 21, 2025
Budget:$50,000 - $150,000
Timeline:16-24 weeks
Priority:Medium Priority
👁️Views:11500
💬Quotes:555

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