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At VaniTech, We are expert in Data oriented systems and custom workflow.

Data-oriented systems are computer systems that are designed to store, manage, and process large amounts of data. They are designed to handle high volumes of data, often in real-time, and provide efficient ways to access and analyze the data.

We Architect, design and implement different type of databases, custom forms, workflows and event oriented systems.

Relational databases

Relational databases are the most commonly used type of data-oriented system and store data in tables with rows and columns. Relational databases use Structured Query Language (SQL) to manage and query the data. SQL is a programming language that is used to create, modify, and query relational databases. It allows users to insert, update, and delete data, as well as to retrieve data from the database in the form of results sets.

Relational databases have several advantages, including:

1. Data integrity: Relational databases enforce data integrity constraints, such as unique keys and foreign keys, which ensure that the data is consistent and accurate.
2. Data normalization: Relational databases use normalization to reduce data redundancy and improve data integrity.
3. Scalability: Relational databases can be scaled horizontally by adding more servers to the system.
4. High performance: Relational databases are designed to handle large amounts of data and can handle complex queries with high performance.
5. Widely supported: Relational databases are widely supported and are used in many industries and applications.

Some popular examples of relational databases include MySQL, Oracle, Microsoft SQL Server, and PostgreSQL. These databases are widely used in web development, business applications, and data warehousing.

Relational databases are a powerful and flexible technology, but they are not always the best solution for every type of data problem. For example, when dealing with unstructured data, other types of databases like NoSQL databases may be more appropriate.

NoSQL databases

NoSQL databases (short for "Not Only SQL") are a type of data-oriented system that are designed to handle unstructured and semi-structured data. Unlike relational databases, which store data in tables with rows and columns and use SQL to manage and query the data, NoSQL databases use a variety of data models such as document, key-value, graph, and column-family.

NoSQL databases have several advantages over relational databases, including:

1. Scalability: NoSQL databases are designed to scale horizontally, which means that more servers can be added to the system as the amount of data grows. This makes NoSQL databases well suited for big data and real-time applications.
2. Flexibility: NoSQL databases do not have a fixed schema, which means that the structure of the data can change as the application evolves. This allows for more flexibility and faster development.
3. Performance: NoSQL databases are optimized for high performance and can handle a large number of read and write operations.
4. Handling unstructured data: NoSQL databases are well suited for handling unstructured and semi-structured data, such as JSON, XML, and social media data.
5. Cloud-friendly: Many NoSQL databases are cloud-native and can be easily deployed in cloud environments.

Some popular examples of NoSQL databases include MongoDB, Cassandra, Redis, and Couchbase. These databases are widely used in web development, mobile applications, social media, and gaming.

NoSQL databases are a powerful technology that provides a flexible and scalable way to handle unstructured and semi-structured data. However, they do not provide the same level of data integrity and data consistency as relational databases. Additionally, they may not support complex queries and transactions as well as relational databases do.

Data warehouses

These systems are used to store large amounts of historical data and provide a central repository for data from various sources. They are optimized for reporting and analytics and support complex queries. Examples include Amazon Redshift, Microsoft Azure Synapse Analytics, and Google BigQuery.