Education Use Case

The goal was to develop a comprehensive end-to-end data analytics solution tailored for the K-12 analytics needs. The solution is designed to seamlessly integrate data from diverse academic and administrative systems, including Student Information Systems (SIS), External Assessments, Attendance, Financial Data, and Feedback Systems.

Discovery Phase

Business Requirement

To empower educational institutions (K-12 schools) to leverage their data effectively — enabling informed decision-making.

Functional Requirements

• A single source of truth for all the data needs of the schools

• Enabling teachers and staff to quickly analyse the student progress

• Enabling AI agents to bring ad hoc in-depth analysis closer to stakeholders.

Technical Requirements

Ingestion of data from various sources, including:

1. Student Information Systems (SIS) – student profiles, admissions, enrolment

2. Attendance & Classroom

3. Curriculum Data

4. Assessment – grades, Usage Records

5. Finance & Fee – tuition, payments

6. Feedback & Survey (students, parents, faculty)

Success Criteria

Deliver advanced metrics capabilities across the following Education domains:

• Admissions Analytics

  • Enrollment Metrics

• Assessment Analytics

  • Subject-wise Performance Insights

  • Assessment at different levels of the curriculum

  • Proficiency Distribution By grades (e.g., high performer, low performer)

  • Assessment Score Trends Over Time

• Financial Analytics

  • Revenue and Expenditure Trends

• Operational Analytics

  • Attendance Insights

  • Survey & Feedback Trends

  • Student Demographics Overview

  • Student Behaviour Trends

  • Professional Development Trends

  • ATL Skills Trends

Data Collection and Integration

Source Identification & Ingestion

A high-level discovery of data sources was conducted across all relevant domains. The identified data was ingested into the Raw layer for raw storage.

Core Business Entities Identified

• Students & Enrollments

• Courses & Programs

• Assessments

• Attendance Records

• Financial Transactions

• Staff Profiles

• Feedback & Surveys (students, parents, and staff)

Implementation Timeline

The implementation timeline will range from 1 week to 4 weeks, depending on the time required to integrate with your SIS.

Using a 2-Layer Architecture (Merged Bronze+Silver → Gold)

1. Data Was Already Clean and Well-Structured

   1. The source data was of high quality, eliminating the need for separate raw (Bronze) and refined (Silver) stages.

   2. Ingestion and basic transformations could be performed in a single step without sacrificing data reliability.

2. Merged Layer Simplifies Pipeline

   1. Combining Bronze and Silver reduces pipeline complexity and minimizes redundancy in ETL steps.

   This consolidation leads to easier development, monitoring, and debugging.

3. Maintained a Clear Separation Between Processing and Consumption

   1. The second (Gold) layer still provides a distinct, business-oriented data model with aggregations, KPIs, and reporting-ready datasets.

   2. This preserves the benefits of the Medallion architecture while removing unnecessary overhead.

4. Improved Performance and Reduced Latency

   1. Fewer processing stages mean faster data availability for business consumers.

   2. Ideal for near-real-time or frequent reporting needs.

5. Optimized Resource Usage and Cost

   1. Reducing one full layer (typically with redundant data storage and compute) leads to lower costs in both storage and processing.

6. Architecture Suited to Simpler Use Cases

   1. The data pipeline did not require extensive cleansing, deduplication, or normalization processes typically handled in Silver.

   2. Use cases mainly revolve around consumption and reporting, which are fully supported by the gold layer.

7. Avoided Overengineering

   1. Applied a “right-sized” architectural pattern based on actual data and business needs.

   2. Following a full 3-layer pattern in this context would have added complexity with no added value.

8. Scalable Design for Future Needs

   1. If data complexity or volume increases later, it's still easy to reintroduce a distinct silver layer without major rework.

   2. The current structure supports both simplicity and future extensibility.

Summary

(Merged Bronze+Silver) → Gold: balances efficiency, performance, and business readiness by:

• Reducing unnecessary complexity,

• Keeping transformation and consumption layers cleanly separated,

• Aligning with the actual data complexity and team capacity.

An Architecture was adopted, organizing data into two layers—Raw and Gold.

Raw Layer- PostgreSQL

• Stores ingested raw data in its raw_zone schema.

• Maintains data in its source structure, ensuring no transformation occurs during ingestion and properly handling null values, integer, and string data types.

• Designed to handle daily and incremental loads.

• Enables auditability by preserving raw transaction details for every data load.

Gold Layer – PostgreSQL

• Stores fully curated and optimized datasets for advanced analytics and BI.

• Ensures relational integrity across students, courses, faculty, assessments, and attendance.

• Serves as the primary source for dashboards, reports, machine learning models, and real-time insights.

Data Load to Raw Layer (PostgreSQL)

Data is ingested from multiple heterogeneous Education systems into the Raw Layer of PostgreSQL. The ingestion supports both batch (ETL/ELT pipelines and jobs) and real-time streaming (Kafka) methods, depending on source type and business requirement.

• Student Information Systems (SIS) – student profiles, admissions, enrollment history

• Assessment

• Finance & Fees– tuition payments, financial aid.

• Faculty – staff profiles, attendance, professional development

• Feedback & Survey – student, parent, faculty

Data Ingestion Workflow

1. Data Extraction

   1. Connects to source systems via JDBC, APIs, file ingestion, or Kafka topics.

   2. Executes custom SQL queries (for SIS, Finance DBs) or stream listeners (for assessment, attendance devices).

2. Data Processing

   1. Converts extracted data into Pandas Data Frames and inserts them into PostgreSQL in batches.

3. Data Load into PostgreSQL (Raw Layer)

   1. Loads raw data into PostgreSQL Raw tables.

   2. Retains the native structure of source systems for traceability.

   3. Supports both batch ingestion (daily SIS) and real-time streaming (attendance logs, assessment)

Key Categories Consideration

Core Components of a K12 are Students, Staff (Teachers), Admissions, Finance, and Surveys.

We are capturing the following School data in the current solution:

• Student Demographic Details

• Student Health Details

• Student Assessment Details

• Student Behavior Data

• Student Guardian Details

• Student Transportation Details

• Student Class Details

• Student Attendance Details

• School Admission Details

• School Financial Details

• School Survey Data – Student, Staff, Parent

• Staff Details

• Staff Professional Learning Details

• Staff Attendance Data

Required Python Packages

pip install PostgreSQL-connect

Environment Variables Setup

Create/set the following environment variables:

# Database Configuration

• host =your-postgres -host

• port=your -port

• database =your-database-name

• user =your-username

• password =your-password

Implementation

Database Connection Setup

The pipeline uses PostgreSQL-connect to connect to PostgreSQL. Ensure your database:

• It is accessible from your execution environment

• Has the required tables

  • User has SELECT permissions on target tables

Important Query Guidelines:

• Include proper date filtering for incremental loads

• Ensure queries are optimized for performance

• Handle NULL values appropriately in transformations

Best Practices

Query Optimization

• Partitioning & Primary Keys: Define partition keys such as academic_year, term_name, and class_id for efficient pruning.

• **Avoid SELECT ***: Always query only the required columns (e.g., student_id, class_id, grade_level, school_year) to reduce I/O and improve performance.

• Query Profiling: Use EXPLAIN and SYSTEM QUERY LOG to analyze performance.

• Batch Aggregations: Pre-aggregate data in the Raw Layer (e.g., daily attendance).

Error Handling

• ETL Scripts: Wrap all extraction and load operations in try-except (Python) or equivalent error handling.

• Logging: Capture errors with context.

• Retry Logic: Implement exponential backoff for transient failures.Graceful Shutdown: Ensure DB connections to PostgreSQL close cleanly on failure.

Security

• Credential Management: Never hardcode credentials—use environment variables.

• Role-Based Access: Assign least-privilege access to PostgreSQL users (etl_user, reporting_user).

• Secure Transport: Enforce TLS/SSL for PostgreSQL and source system connections.

• Network Security: Use VPN/private endpoints for inter-database transfers

Performance

• Batch Inserts: Insert data into PostgreSQL in batches (500–1k rows per insert) for efficiency.

• Connection Pooling: Reuse PostgreSQL connections for multiple inserts.

• Memory Efficiency: Use streaming/chunking when extracting from sis to avoid OOM with large DataFrames.

Monitoring

• ETL Logging: Maintain a log table in PostgreSQL with table name, start time, end time, row counts, and status.

• Row Count Validation: Track record counts per batch vs. source to detect drops.

• Alerting: Set up alerts for pipeline failures and jobs.

Common Issues and Solutions

Database Connection Issues

• Problem: Connection timeouts

• Solution: Check network connectivity, firewall rules, and connection parameters

Memory Issues

• Problem: Large datasets cause memory errors

• Solution: Implement chunking or streaming for large queries

Date Filtering Issues

• Problem: Timezone mismatches

• Solution: Use consistent timezone handling across all date operations

Ingestion Failures

• Problem: Batch insert failures due to schema mismatch

• Solution: Validate schema mapping before inserts, use staging tables in raw for mismatched data.

Testing Strategy

Unit Testing

• Test connectivity to source (SIS, Kafka).

• Validate sample SQL extraction queries with small date ranges.

• Test PostgreSQL batch insert with dummy data.

• Verify error handling by injecting bad records.

Integration Testing

• Run end-to-end ingestion from one education source → Raw → Datamart.

• Validate data accuracy (source vs. PostgreSQL row counts).

• Test partitioning & indexing with range queries.

Performance Testing

• Test with various data volumes.

• Monitor execution time and memory usage.

• Validate concurrent execution scenarios.