Storage & Data

Learn about the unique characteristics of NoSQL databases, why they are so critical to information management today, and why they represent such an important development.

A database query allows you to ask a specific data-related question based on a pre-defined code. This is sent to a database management system (DBMS) with the system sourcing an answer direct from the database before retrieving and presenting it.

Redis® is an open-source, in-memory data structure store used as a database, cache, and message broker. It supports various data types, offers high performance, and can handle millions of requests per second. Redis® open source is versatile and suitable for scenarios requiring quick data access and real-time processing.

Redundant Array of Independent Disks (RAID) is a storage technology that combines multiple disks to improve performance, security, and fault tolerance. Used in business and at home, it ensures efficient and reliable data management.

With its range of features, PostgreSQL can process a range of database types with varying levels of complexity, while delivering optimal performance.

Non-Volatile Memory Express (NVMe) is a high-performance storage protocol for PCIe SSDs. It surpasses older interfaces like SATA by offering significantly faster speeds and lower latency, specifically designed for flash memory, revolutionizing data access in modern computing systems.

Edge storage strategically positions data closer to its source, minimizing latency and conserving bandwidth. Crucial for IoT deployments, it also complements cloud computing. Key advantages include enhanced performance, improved security, and cost-effective operations. Diverse storage solutions address varied organizational needs.

A data lake is a centralized storage space for storing raw, structured, semi-structured or unstructured data. It offers great flexibility for storing and analyzing this data, especially in big data and AI environments.

Data Warehousing acts as a hub, integrating data from various sources into a single repository for analysis. It enables organizations to perform complex queries, uncover trends, and gain insights into operations, driving strategic decision-making through advanced analytics.

Data warehouses consolidate diverse data sources, offering a single source of truth. They empower businesses with historical and current data for analysis, reporting, and informed decision-making. ETL (Extract, Transform, Load) processes prepare data for the warehouse, while BI tools unlock insights.

Data visualization transforms raw data into graphical representations like charts and maps. This practice helps uncover hidden patterns and trends, enabling clearer understanding, insightful decision-making, and effective communication of complex information for individuals and organizations to act upon.

Data transformation converts raw data into a usable format. It’s the process of changing data's structure, format, or values to improve quality and prepare it for analysis, reporting, business intelligence, and unlocking its true value for informed decision-making.

Data streaming is a process for processing data streams in real time. It enables continuous data analysis, providing better insights for quick decision-making. Data streaming is often used in areas such as social networks.

Data storage ensures the safekeeping and accessibility of information using specialised technology. It involves on-premise storage with physical servers in a company's facility, and cloud storage, which utilises remote servers for scalable, accessible data management.

Data security encompasses all of the best practices in terms of protection that must be put in place to guarantee the confidentiality, sovereignty and integrity of the data stored on an IT infrastructure.

Data pipelines automate turning raw data into valuable insights through a series of steps. These steps include collecting data from various sources, refining it, and sending it to a data warehouse, a data lake or a data lakehouse.

Data modeling visually represents data structures and relationships, aiding in database design and efficient data management. Various types exist, like relational and dimensional models. The process involves requirements analysis, conceptual, logical, and physical modeling, plus maintenance. Techniques include ER modeling and normalization.

Data mining refers to the collection, analysis and comparison of data from different and often unconnected sources. The data extracted can be used for many purposes, like improving efficiency and predicting behaviour, for example.

A data lakehouse is an architectural approach combining features of data lakes and data warehouses. It supports both unstructured and structured data, offering vast storage (like a lake) and structured querying capabilities (like a warehouse). This architecture enables advanced analytics, AI, and ML, efficiently handling diverse data types at scale.

Data governance is a system of processes and policies for managing and protecting data. It ensures data quality, security, and compliance with regulations. It also enables organizations to make better decisions, improve operational efficiency, and gain a competitive advantage.

Data backup refers to the process of creating duplicate copies of digital data or files to safeguard against data loss if the originals are lost due to malicious action, accidents or disaster. The right backup processes will enable the enterprise to restore data quickly, causing minimal business disruption.

Data analytics is the process of transforming raw data into actionable knowledge. It involves collecting, cleaning, and analysing data to find patterns, correlations, and hidden trends. Data analytics is vital for businesses as it drives informed decision-making, optimises operations, identifies market trends, and manages risks.

Block storage is a type of storage solution that stores data in equal-sized blocks, either on hard drives or in the cloud. This enables faster access to data compared to other storage methods, with the ability to increase capacity by adding more disks as and when you need them.

SQL databases are relational and use SQL to manage data. NoSQL databases are non-relational and store data in flexible schemas. SQL is ideal for complex queries, while NoSQL offers more scalability for big data applications.

The data lake stores raw and unstructured data, while the data warehouse organizes structured data for precise analysis. The choice depends on the analysis needs.