The team at 2K-Software has extensive experience in developing business applications and complex distributed software systems across various industries. Almost all our projects involve working with databases encompassing storage, retrieval, updating, processing, and data analytics.

Database Management Systems (DBMS) for Effective Data Handling Using database management systems (DBMS) is the most effective way to handle data operations. DBMS providers ensure maximum flexibility, performance, scalability, and reliability through features like replication, clustering, and sharding. Depending on the tasks at hand, one or multiple specialized databases can be utilized.

Relational Databases.

Relational databases are the most common type of DB. The database structure is described using a schema, consisting of tables and the relationships between them. Each table includes columns, each with a name and data type. Primary and foreign keys, which are also columns, establish relationships between tables. Each record in a table is uniquely identified by the primary key. Performance is optimized using indexes on one or more columns. The SQL language and its vendor-specific derivatives are used to interact with relational databases. The structure of relational DBs makes them flexible tools for handling various types of data and levels of connectivity.

Examples of Relational DBMS: MS SQL, Oracle, MySQL, PostgreSQL, MariaDB, SQLite.

NOSQL Databases.

NoSQL databases emphasize their non-relational nature and are categorized as follows:

Document-Oriented – Based on document storage with hierarchical structure support, requiring no schema specification. Documents, often represented in XML, JSON, or BSON formats, allow for dynamic data structuring. Examples: MongoDB, CouchDB.

Key-Value Store – Store arbitrary objects by key. These primitive and highly scalable databases are commonly used for caching data or storing user sessions. Examples: Redis, Memcached.

Columnar Stores – Store data by column rather than by row, offering performance advantages for searching and indexing large datasets. Examples: Cassandra, cstore_fdw (PostgreSQL extension).

Graph Databases– Store information as node properties with relationships represented by edges. Suitable for scenarios requiring relationship processing, such as social network algorithms and fraud detection in financial services. Examples: Neo4j, OrientDB.

TIME SERIES DB

pecialized for storing and processing time series data, often used in monitoring systems, telemetry collection and analysis, and constructing graphical time metrics. Examples: InfluxDB, Prometheus, TimescaleDB (PostgreSQL extension).

2K-Software specialists are proficient in working with various databases, including:

MS SQL, Oracle, MySQL, SQLite, MongoDB, Redis, InfluxDB.

For applications requiring relational databases, we often use object-relational mapping (ORM) systems. ORMs allow developers to work with a conceptual data model rather than a relational storage schema, reducing code volume and shifting the focus of data processing to application code. This simplifies development, and eases code maintenance and evolution.

In C# development, popular ORM systems include Entity Framework (by Microsoft) and NHibernate.

We primarily use Entity Framework (EF), which supports MS SQL Server, SQLite, PostgreSQL, and MySQL.

Entity Framework Core, a cross-platform version for .NET Core, can run on both Windows and Linux.