Relational Databases:
RDBMS : Oracle, MS SQL, Sybase, Informix, PostgreSQL, MySQL
- Expertise in designing and optimizing complex relational database schemas.
- Proficient in writing advanced SQL queries, stored procedures, and triggers.
- Tuning and configuring with database administration tasks, such as indexing and performance tuning.
- Understanding of Normalization Concepts, eliminating data redundancy, data integrity, deletion
of anomalies
- Improving performance using index scan instead table scan and troubleshooting with explain plan
NoSQL Databases:
- MongoDB:
- Experience with document-oriented databases, such as MongoDB.
- Knowledge of JSON-like document structures and schema-less database design.
- DynamoDB:
- Understanding and usage of Amazon DynamoDB for scalable and high-performance NoSQL storage.
Database Migration Tools:
- Liquibase and Flyway
- Database schema version control and management to track schema changes effectively
Database Concepts:
- CAP Theorem:
- Understanding of the CAP theorem (Consistency, Availability, Partition Tolerance)
and its
implications
on distributed database systems.
- Transactional Management in Distributed Databases:
- Experience in implementing and managing transactions across distributed database systems
for
maintaining
data consistency.
- Proper usage of transaction isolation levels to reduce chances of dead locks and knowledge
in dirty
reads,
- committed reads, etc, pessimistic and optimistic approaches and knowledge in shared locks
and
exclusive locks
- Two-phase commits in distributed transactions and ensuring atomicity
- Caching in Distributed Systems:
- Knowledge of caching strategies in distributed systems for optimizing data retrieval and
reducing
latency.
Big Data Processing:
- Hadoop:
- Familiarity with Apache Hadoop for distributed storage and processing of large data sets.
- Knowledge of Hadoop ecosystem components like HDFS, MapReduce, and Hive.
- Serverless Databases (e.g., AWS Aurora Serverless):
- Knowledge of serverless database offerings for automatic scaling based on demand.
ETL and Data Processing:
- FileWatcher and ETL Processes:
- Developed modules including FileWatcher and ETL data loader using the OpenCSV parser,
- showcasing
hands-on experience in data extraction, transformation, and loading processes.
- Data Lakes (e.g., Amazon S3, Azure Data Lake Storage):
- Inclusion of data lake concepts for storing and managing vast amounts of raw data.
- Streaming Data Processing (e.g., Apache Kafka):
- Experience with real-time data streaming and processing technologies for handling
continuous data
flows.