What Is Data Infrastructure?
Data must be supported by a digital infrastructure that facilitates its collection, storage, processing, and dissemination.
For this reason, data infrastructure plays a vital role in an organization's overall data strategy, enabling its data to be secured, processed, accessed, and utilized effectively and efficiently.
Data Infrastructure is the digital infrastructure built to manage, store, and process data. This includes databases, data warehouses, servers, hardware and software, network equipment, and data centers. It's the foundation upon which data management strategy is built.
An effective data infrastructure enables organizations to organize large volumes of data, apply analytics, facilitate access, and ensure data integrity, ultimately leading to meaningful information and knowledge for decision-making. It also comprises the rules, procedures, and standards set in place to ensure data quality and ease of use.
What Are the Types of Data Infrastructure?
The types of Data Infrastructure can be broadly categorized in the following ways:
Traditional Infrastructure
This refers to a standard IT infrastructure that uses in-house servers, storage systems, and network devices to collect, store, process, and manage data. This kind of infrastructure requires significant resources and technical expertise to manage.
Cloud Infrastructure
Cloud-based data infrastructure uses cloud storage and computing resources provided by third-party cloud service providers. It offers scalability, cost-effectiveness, and convenience, eliminating the need to maintain onsite server rooms and IT staff.
Hybrid Infrastructure
Hybrid data infrastructure combines the elements of both traditional and cloud infrastructures. Some data may be stored on-premises for speed and security considerations, while others may be stored in the cloud for cost-effectiveness and scalability.
Converged Infrastructure
Converged data infrastructure pre-packages multiple IT components into a single, optimized solution. This type includes storage, servers, networking, and software provisioned and managed as a single entity.
Hyper-Converged Infrastructure
This is an evolution of converged infrastructure wherein all components are software-defined, with tight integration between technologies, offering even greater simplification and cost savings.
Edge Infrastructure
Edge data infrastructure processes data closer to its source, reducing latency and transmission costs. With the rise of Internet of Things (IoT) devices, edge computing has become increasingly important.
What Are the Various Aspects of Data Infrastructure?
Data infrastructure has several fundamental aspects supporting data collection, storage, processing, distribution, and usage. The main aspects of data infrastructure include:
Physical Infrastructure
This comprises hardware components like servers, computers, routers, and physical storage devices. It can also include data centers and the physical facilities that house the hardware.
Software Infrastructure
This includes databases, data warehouses, extraction, transformation, and loading (ETL) tools, data analytics software, and other applications and platforms that facilitate data management, processing, and analysis.
Network Infrastructure
This involves the telecommunication networks (LAN, WAN, Internet) that enable data transfer within and between systems, servers, and storage devices.
Cloud Storage
With technological advancements, more companies are moving their data infrastructure to the cloud. Cloud storage provides a flexible and scalable solution that can be expanded or contracted based on storage needs.
Data Processing Frameworks
These are software libraries or engines that enable large-scale data processing. Examples include Hadoop, Apache Spark, etc.
Security Infrastructure
Security measures are essential to protect data from unauthorized access, breaches, or cyber-attacks. These measures can include firewalls, encryption software, and user-access controls.
Here’s How to Build a Robust Data Infrastructure
To leverage their data effectively, organizations must understand how to construct a robust, interconnected data infrastructure that enables secure collaboration.
Identify Your Data Needs
The initial step towards building a data infrastructure is to identify the types of data your organization requires, how frequently it is generated, and the specific units or individuals using it.
Choose the Right Technology
Choose the appropriate technology that suits your organization's data discovery requirements. This could include databases, data warehouses, data lakes, and data management and analytics tools.
Design a Data Flow Map
Design a visual representation showing how data moves through your organization. This includes where data is sourced, processed, stored, and used.
Implement a Data Governance Strategy
Establish rules and policies to manage data accuracy, integrity, security, and privacy. This includes having a well-defined process for data ingestion, transformation, storage, and access.
Build a Scalable System
Your data infrastructure should be designed to grow with your business needs. For this, it is good to adopt easily scalable systems in terms of storage capacity and computational resources.
Automate Data Processes
Automating repetitive data processes can save a significant amount of time and reduce the possibility of errors. This includes data extraction, transformation, and loading processes.
Enable Real-Time Data Processing
For many businesses, real-time data processing is crucial for operational efficiency and decision-making. Therefore, include technologies that support real-time processing and analysis in your data infrastructure.
Hire and Train the Right Teams
Building and maintaining a data infrastructure requires data engineering, database administration, and data security skills. Have the right team or engage with a trusted partner to manage your data infrastructure.