Databases

MongoDB is a popular open-source NoSQL database designed for high availability, scalability, and developer flexibility. It stores data in flexible, JSON-like BSON documents and supports features like replication, sharding, and indexing. However, large-scale MongoDB deployments often encounter challenges such as query performance degradation, replica set failures, memory or connection bottlenecks, schema design pitfalls, and sharding imbalances. Effective troubleshooting ensures reliable, performant, and scalable MongoDB database systems.

Apache HBase is a distributed, scalable, NoSQL database built on top of Hadoop's HDFS, modeled after Google's Bigtable. It is designed to store and manage massive amounts of sparse data in a fault-tolerant way. However, large-scale HBase deployments often encounter challenges such as region server failures, data hot-spotting, compaction issues, write amplification, and replication lags. Effective troubleshooting ensures resilient, performant, and scalable HBase operations for enterprise-grade applications.

SQLite is a lightweight, self-contained, serverless relational database engine widely used in embedded systems, mobile applications, and desktop software. Despite its simplicity, large-scale or high-concurrency SQLite deployments often encounter challenges such as database locking errors, performance bottlenecks, corruption risks, file system synchronization issues, and migration complexities. Effective troubleshooting ensures stable, efficient, and reliable use of SQLite in production and embedded environments.

MySQL is one of the world's most popular open-source relational database management systems, widely used for web, cloud, and enterprise applications. It offers scalability, reliability, and high performance. However, large-scale MySQL deployments often encounter challenges such as query performance bottlenecks, connection pool exhaustion, replication lag, data corruption risks, and backup/restore inconsistencies. Effective troubleshooting ensures high availability, performance optimization, and long-term data integrity for MySQL systems.

PostgreSQL is a powerful, open-source object-relational database system known for its robustness, feature richness, and standards compliance. It supports advanced data types, indexing methods, and concurrency models. However, large-scale PostgreSQL deployments often encounter challenges such as query performance degradation, connection pool exhaustion, replication lag, transaction bloat, and backup or recovery complexities. Effective troubleshooting ensures high availability, performance optimization, and long-term data integrity for PostgreSQL systems.

HSQLDB (HyperSQL DataBase) is a lightweight, relational database engine written in Java that supports a wide range of SQL standards. It is often used for development, testing, and lightweight production applications due to its in-memory and embedded capabilities. However, large-scale HSQLDB deployments often encounter challenges such as data persistence issues, memory consumption bottlenecks, concurrency and locking problems, database file corruption, and integration difficulties with Java applications. Effective troubleshooting ensures stable, performant, and reliable database operations with HSQLDB.

Redis is an open-source, in-memory key-value data store known for its high performance, low latency, and flexible data structures. It is widely used for caching, session management, real-time analytics, and message brokering. However, large-scale Redis deployments often encounter challenges such as memory management issues, persistence failures, replication and clustering problems, latency spikes under high load, and security misconfigurations. Effective troubleshooting ensures scalable, performant, and reliable database operations with Redis.

Raima Database Manager (RDM) is a high-performance embedded database engine designed for IoT, real-time, and edge computing environments. It provides deterministic performance, small footprint, and advanced concurrency controls. However, large or complex RDM deployments often encounter challenges such as transaction management errors, data consistency issues under concurrency, memory optimization problems, cross-platform deployment complications, and replication or synchronization failures. Effective troubleshooting ensures reliable, efficient, and scalable embedded database operations with RDM.

ScyllaDB is a high-performance NoSQL database designed as a drop-in replacement for Apache Cassandra, offering low latency, high throughput, and automatic sharding. Built in C++ for maximum efficiency, it is optimized for modern multi-core servers. However, large-scale ScyllaDB deployments often encounter challenges such as latency spikes, node failures under load, schema management issues, compaction inefficiencies, and cluster scaling complexities. Effective troubleshooting ensures resilient, performant, and scalable database operations with ScyllaDB.

Apache Druid is a high-performance, real-time analytics database designed for fast slice-and-dice analytics on large datasets. It is widely used for powering modern data applications that require low-latency queries and flexible data exploration. However, large-scale Druid deployments often encounter challenges such as ingestion failures, query performance degradation, segment management issues, memory and resource contention, and cluster scaling difficulties. Effective troubleshooting ensures stable, efficient, and scalable analytics systems with Druid.

SAP HANA is an in-memory, column-oriented, relational database management system developed by SAP for high-performance analytics and transactional processing. It supports real-time data access, advanced analytics, and mixed OLTP/OLAP workloads. However, large-scale SAP HANA deployments often encounter challenges such as memory management issues, slow query performance, system replication failures, disk I/O bottlenecks, and backup/restore inconsistencies. Effective troubleshooting ensures high availability, optimal performance, and reliability of SAP HANA environments.

MarkLogic is a multi-model NoSQL database platform designed for handling structured, semi-structured, and unstructured data at enterprise scale. It supports search, data integration, transactions, and semantics natively, making it ideal for complex applications involving XML, JSON, RDF triples, and relational data. However, real-world MarkLogic deployments often encounter challenges such as query performance bottlenecks, document ingestion failures, cluster node communication issues, security misconfigurations, and transaction conflicts. Effective troubleshooting ensures high availability, scalability, and performance of MarkLogic applications.