Database Systems Development

This course provides students with a comprehensive understanding of database architecture, including the internal workings of relational database management systems (RDBMS). Students explore storage structures, buffer management, indexing methods, query execution plans, and the principles of concurrency control. Through case studies and labs, they examine the trade-offs in performance and scalability across centralized and distributed systems. The course builds a foundation for understanding how databases manage memory, I/O, transactions, and data consistency. This expanded version includes detailed exploration of ACID properties, distributed databases, and multi-version concurrency control. Students engage in lab exercises that simulate various architectural designs to evaluate trade-offs in performance and fault tolerance. The course also includes an introduction to columnar storage and memory-optimized databases.

Students gain hands-on experience writing efficient and complex SQL queries to retrieve, update, and manipulate relational data. The course goes beyond basic SQL into joins, subqueries, CTEs, views, and stored procedures. Special focus is placed on performance optimization techniques such as indexing strategies, query refactoring, and understanding execution plans. Students also analyze case scenarios to identify and resolve inefficient queries using profiling tools and optimization hints. Students also explore query optimization tools, dynamic SQL generation, and use of indexes in JOIN operations. They work with performance benchmarks to measure improvements and write SQL code for real-world reporting and data analysis scenarios. Advanced filtering, pivoting, and JSON/XML handling in SQL are introduced.

Students develop the skills to design normalized relational databases from real-world requirements. The course covers conceptual, logical, and physical data modeling using ERDs and normalization techniques up to 5NF. Students translate business rules into table structures, enforce referential integrity, and optimize schema design for scalability and efficiency. Through projects, they learn to model one-to-many, many-to-many, and recursive relationships. Students also explore dimensional modeling techniques for analytical databases and star/snowflake schemas. The course introduces tools like ER/Studio or Lucidchart for modeling, and students assess trade-offs between normalization and denormalization in high-volume transactional environments.

This course focuses on writing procedural code using PL/SQL for Oracle environments. Students build stored procedures, triggers, user-defined functions, and packages. The course covers exception handling, cursors, dynamic SQL, and bulk data operations. Students also practice modular programming, encapsulation, and error recovery in database programs. Students also build real-world transaction systems using packages, control structures, and PL/SQL optimization techniques. They test procedures for edge cases, simulate concurrent access using triggers, and enforce complex business rules programmatically within the database.

Students learn to implement comprehensive backup and recovery plans using native database utilities and tools. Topics include full and incremental backups, point-in-time recovery, flashback technologies, RMAN scripting, and disaster recovery procedures. Students simulate common failure scenarios and practice recovery strategies to ensure minimal data loss. The course also includes hands-on labs with various backup scenarios, testing logical and physical corruption recovery techniques. Students write custom scripts for automating backups, schedule policies using cron jobs, and create backup validation reports. The importance of data integrity and regulatory compliance in backup strategies is emphasized.

This course explores database-level security principles, including authentication, authorization, access control, data encryption, and auditing. Students configure roles, privileges, and user access policies to protect data integrity. Case studies cover regulatory standards like GDPR, and students practice detecting and mitigating vulnerabilities through hardening techniques. Students also practice applying database firewall rules, anonymizing datasets, and conducting security audits. Labs include simulating SQL injection and cross-site scripting threats to understand mitigation. Policies are developed for multi-layered security and incident response within database environments.

Students work with industry tools to monitor database health, resource usage, and performance. They configure alerts, analyze logs, interpret wait statistics, and use dashboards to identify slow queries and system bottlenecks. The course emphasizes proactive database maintenance and capacity planning. Students also examine performance baselining and use trend analysis to forecast system issues. They integrate alerts into automated workflows and correlate monitoring outputs with OS-level logs to perform root cause analysis. Integration with tools like Prometheus, Grafana, or SQL Profiler is introduced.

Students learn methods to ensure continuous data availability through clustering, log shipping, database mirroring, and replication. The course includes hands-on setup of HA solutions and analysis of failover strategies. Concepts like quorum, synchronous vs. asynchronous replication, and zero-downtime upgrades are discussed in depth. The course includes labs on failover cluster setup and multi-master replication configurations. Students simulate data center outages, measure RTO and RPO metrics, and evaluate cloudbased HA options. Group-based projects emphasize designing cost-effective HA architectures for various business scenarios.

Students are introduced to non-relational databases such as MongoDB, Cassandra, and Redis. They explore use cases for document, key-value, columnar, and graph databases. Emphasis is placed on understanding schemaless design, scalability, consistency models (CAP theorem), and integrating NoSQL solutions into hybrid data environments. Students also compare NoSQL databases for different use cases, exploring CAP trade-offs and consistency tuning. They work with sharded collections, replica sets, and polyglot persistence. Use of NoSQL in real-time analytics, IoT, and recommendation engines is discussed.

This course dives into advanced performance optimization strategies, with a focus on indexing methods, partitioning, materialized views, and query rewriting. Students analyze slow queries, leverage indexes effectively, and measure throughput, latency, and resource consumption. The course includes in-depth labs using profiling tools to isolate and resolve performance issues. The course also explores adaptive query processing, plan caching, and use of database advisors. Students perform trace-based performance profiling and compare hash vs. B-tree indexing for different workloads. Labs require tuning database workloads under constraints and analyzing performance trends.

Students write scripts using Python and shell scripting to automate repetitive DBA tasks such as backups, user provisioning, data imports, and system checks. The course introduces scheduling tools like cron and integrates scripts with monitoring systems for alerts and logging. Students also work with APIs to perform database tasks programmatically. Students also design logging and error-handling modules, work with subprocesses and environment variables, and simulate event-driven automation in test environments. They integrate their scripts with external systems and dashboards for dynamic status reporting.

Students learn to provision, configure, and manage databases on leading cloud platforms such as AWS, Azure, or Google Cloud. Topics include managed database services (RDS, Cloud SQL), scaling, backup policies, encryption, network access control, and cost optimization. Labs focus on deploying secure and efficient cloudbased data solutions. Students also practice configuring virtual private clouds, identity and access management (IAM), and autoscaling parameters. They simulate database failovers in cloud environments and optimize storage tiers. Cloud-native monitoring and alerting tools are incorporated into lab work.

Students are introduced to big data concepts and how relational databases interface with distributed processing platforms like Hadoop and Spark. Topics include ETL pipelines, data lakes, and real-time ingestion tools (Kafka, Flume). Students gain foundational skills in architecting data flow and integrating structured and semistructured data sources. Students also explore data transformation pipelines using Spark SQL and structure streaming for near-real-time ingestion. They assess data quality and latency in data lake architectures. Labs include integrating relational data with semi-structured sources for reporting and visualization.

This course focuses on establishing data governance frameworks and ensuring regulatory compliance. Students learn to configure auditing features, track changes, produce compliance reports, and define data retention policies. They explore how organizations meet internal and external standards through documentation, oversight, and continuous assessment. Students also implement audit trails for user and system activity, simulate regulatory compliance scenarios, and create audit dashboards. They evaluate data classification policies and retention schedules aligned with legal mandates. Labs focus on preparing audit reports for internal and external stakeholders.