Summary
Data engineering infrastructure management is the practice of designing, building, and maintaining the systems and architectures that enable the collection, storage, processing, and delivery of data within an organization. It’s the foundation upon which data-driven insights and decision-making are built.
In essence, data engineering infrastructure management is about building the engine that powers an organization’s data capabilities. It’s a crucial function for any business that wants to leverage its data assets for competitive advantage.
Source: Gemini AI Overview
OnAir Post: Infrastructure Management
About
Core Components
- Data Collection
Gathering raw data from various sources like databases, APIs, sensors, and logs.
- Data Storage
Organizing and storing data in a way that allows for efficient retrieval and processing. This includes databases, data warehouses, and other storage solutions.
- Data Processing
Transforming raw data into a structured and usable format for analysis. This often involves ETL (Extract, Transform, Load) processes.
- Data Delivery
Making data accessible to data scientists, analysts, and other users for reporting, analysis, and other purposes.
- Data Pipelines
Automated workflows that move data through the different stages of the data lifecycle.
- Data GovernanceEnsuring data quality, security, and compliance with relevant regulations.
Source: Google Gemini Overview
Key Responsibilities of a Data Infrastructure Engineer
- Designing, building, and maintaining data storage and processing systems.
- Developing and managing data pipelines.
- Ensuring data quality and integrity.
- Optimizing system performance and scalability.
- Collaborating with other teams to understand data needs and implement solutions.
- Implementing data governance policies and procedures.
- Monitoring and troubleshooting data infrastructure issues.
Source: Google Gemini Overview
Importance
- Enables data-driven decision-making: Provides the foundation for businesses to extract valuable insights from their data.
- Improves operational efficiency: Streamlines data management processes and reduces manual effort.
- Supports data analysis and reporting: Ensures data is readily available and in a usable format for analysis.
- Enhances data security and compliance: Protects sensitive data and ensures adherence to regulations.
- Facilitates business growth: Enables organizations to adapt to changing data needs and scale their data infrastructure accordingly.
Source: Google Gemini Overview
More Information
A data infrastructure is a digital infrastructure promoting data sharing and consumption.
Similarly to other infrastructures, it is a structure needed for the operation of a society as well as the services and facilities necessary for an economy to function, the data economy in this case.
Background
There is an intense discussion at international level on e-infrastructures and data infrastructure serving scientific work.
The European Strategy Forum on Research Infrastructures (ESFRI) presented the first European roadmap for large-scale Research Infrastructures.[1]
These are modeled as layered hardware and software systems which support sharing of a wide spectrum of resources, spanning from networks, storage, computing resources, and system-level middleware software, to structured information within collections, archives, and databases. The e-Infrastructure Reflection Group (e-IRG) has proposed a similar vision.
In particular, it envisions e-Infrastructures where the principles of global collaboration and shared resources are intended to encompass the sharing needs of all research activities.[2]
In the framework of the Joint Information Systems Committee (JISC) e-infrastructure programme, e-Infrastructures are defined in terms of integration of networks, grids, data centers and collaborative environments, and are intended to include supporting operation centers, service registries, credential delegation services, certificate authorities, training and help desk services.[3] The Cyberinfrastructure programme launched by the US National Science Foundation (NSF) plans to develop new research environments in which advanced computational, collaborative, data acquisition and management services are made available to researchers connected through high-performance networks.[4]
More recently, the vision for “global research data infrastructures” has been drawn by identifying a number of recommendations for developers of future research infrastructures.[5]
This vision document highlighted the open issues affecting data infrastructures development – both technical and organizational – and identified future research directions.
Besides these initiatives targeting “generic” infrastructures there are others oriented to specific domains, e.g. the European Commission promotes the INSPIRE initiative for an e-Infrastructure oriented to the sharing of content and service resources of European countries in the ambit of geospatial datasets.[6]
Related Projects
See also
- Data cooperative
- Hybrid Data Infrastructure
- Information Infrastructure
- Research Infrastructure
- Spatial Data Infrastructure
References
- ^ European Strategy Forum on Research Infrastructures. (2010). Strategy Report on Research Infrastructures. Publications Office of the European Union. http://ec.europa.eu/research/infrastructures/index_en.cfm?pg=esfri
- ^ e-Infrastructure Reflection Group. (2010). Blue Paper. E-IRG
- ^ Joint Information Systems Committee. (2006). e-Infrastructure Briefing Paper. JISC
- ^ “Cyberinfrastructure Vision for the 21st Century Discovery” (PDF). National Science Foundation. Cyberinfrastructure Council. 2007.
- ^ Thanos, C. (2011). “Global Research Data Infrastructures: The GRDI2020 Vision”. Archived from the original on 2012-01-26.
- ^ European Parliament, Council. (2007, 3 14). Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE).
- ^ D4Science Website http://www.d4science.org/
- ^ OpenAIRE Website http://www.openaire.eu/
