What Is Data Infrastructure? A Simple Overview
Data infrastructure - required to manage, store, and process data - can take many forms. We look at the different types, aspects, and provide tips on how to build a robust data infrastructure in today's blog.
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.
What Is Data Infrastructure?
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.
Examples include Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
- 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.
- Data Governance: This involves the policies, processes, and practices to control, manage, and ensure the quality and privacy of data and compliance with regulations such as GDPR.
- Data Architecture: This is the design or blueprint of the data infrastructure. It describes how an organization collects, stores, transforms, processes, and consumes data.
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.
The Benefits of Having a Solid Data Infrastructure
- Improved Decision-Making: A solid data infrastructure enables organizations to collect, analyze, and interpret high-quality data, leading to better-informed business decisions.
- Increased Efficiency: Standardized and streamlined processes for data storage, retrieval, and analysis can lead to significant time and cost savings, resulting in increased overall operational efficiency.
- Enhanced Data Governance: Robust data infrastructure promotes better data management and governance, improving data quality, integrity, and reliability.
- Greater Security: Strong data infrastructure often includes comprehensive security measures that protect sensitive data from breaches, ensuring data privacy and regulatory compliance.
- Scalability: Solid data infrastructure can easily accommodate business growth, allowing for easy scalability when data volumes increase.
- Competitive Advantage: Access to real-time, actionable data can create a significant competitive advantage, allowing companies to capitalize on market trends and opportunities more quickly.
- Innovation: A sound data infrastructure forms the foundation for innovation, enabling organizations to gain insights that can drive the development of new products, services, or business models.
- Regulatory Compliance: A reliable data infrastructure helps to ensure that a business adheres to industry regulations and standards by managing and protecting data by legal requirements.
- Enhanced Collaboration: With a well-designed data infrastructure, data can be easily shared and accessed across different departments within the organization, leading to improved collaboration and productivity.
What Are Examples of Data Infrastructure?
Data infrastructure can be diverse and span various hardware, software, technologies, and methodologies. Some examples of data infrastructure include:
- Databases: Databases are structured and organized repositories that store and retrieve large amounts of data. Examples include MySQL, PostgreSQL, MongoDB, and SQL Server.
- Data Warehouses: These are central repositories of integrated data from one or multiple disparate sources used for reporting and data analysis. Examples include Amazon Redshift, Google BigQuery, and Microsoft Azure SQL.
- Data Lakes: Data lakes store raw data in an unstructured manner, allowing for flexible processing and analysis. Examples include Amazon S3 and Azure Data Lake Storage.
- Cloud Computing Platforms: Platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) to handle data storage and processing.
- Networking Infrastructure: These are physical hardware and software components that facilitate connectivity, communication, and management of various devices to exchange and access data. This includes routers, switches, and firewalls.
- Big Data Platforms: These infrastructure components process, analyze, and store large volumes of data at high velocity. Examples include Hadoop and Apache Spark.
- Data Protection and Security Tools: These tools ensure data confidentiality, integrity, and availability. Examples include encryption software, firewalls, and backup software.
- Data Integration Tools: These tools extract, transform, and load (ETL) data from different sources to central repositories. Examples include Informatica PowerCenter, Microsoft SQL Server Integration Services (SSIS), and Talend Open Studio.
- Data Governance Tools: These tools help manage, improve, and maintain data quality and ensure data adheres to policies, standards, and regulations. Examples include Collibra, Alation, and Informatica Axon.
- IoT Infrastructure: Comprising IoT devices and networks that facilitate data collection from various industrial, residential, and commercial environments and transfer it to data processing or storage applications.
Learn How Digital Guardian Can Fortify Your Data Strategy
Building a robust data infrastructure can be complex. Still, with the right plan and team, your business can effectively leverage its data for improved operations and decision-making.
Our top-notch data security at Digital Guardian can protect your data from unauthorized access, corruption, and loss by implementing robust security measures.
Schedule a demo today to learn how we can assist your data infrastructure data.