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Data engineering is a field of study that focuses on the design, development, implementation, and maintenance of systems and processes for storing, managing, and analyzing large and complex data sets. Data engineers are responsible for building and maintaining the infrastructure that enables data-driven decision making in an organization. This often involves designing and implementing data pipelines, data storage systems, and data processing frameworks. Data engineers may also work on data governance and security, as well as on developing tools and frameworks for data analysis and machine learning. Data engineering is a critical field that plays a key role in supporting data-driven business and research.
Functional Data Engineering#
Functional data engineering is a software engineering discipline that focuses on the design, development, and operation of systems and pipelines for collecting, storing, processing, and serving data. Functional data engineering emphasizes the use of functional programming paradigms and principles, such as immutability, composability, and declarative programming, in order to build robust, scalable, and maintainable data systems. Functional data engineering also emphasizes the use of data pipelines, data lakes, and other modern data architectures and technologies, in order to enable real-time, batch, and streaming data processing, and to support a wide range of data-driven applications and use cases. Functional data engineering is an emerging field, and it is closely related to other fields, such as data science, big data, and machine learning. It is an important discipline for organizations that need to manage and derive value from large and complex data sets.
Platform Engineering#
Platform engineering is the practice of designing, building, and maintaining the underlying infrastructure and technical architecture that supports the development and deployment of software applications. It involves creating and managing the infrastructure, tools, and services that enable developers to build, test, and deploy their applications quickly and efficiently. This can include things like cloud computing infrastructure, continuous integration and deployment pipelines, and APIs and other interfaces for accessing data and services.
Data Platform Engineering#
Data platform engineering is a sub-discipline of platform engineering that focuses specifically on the design, development, and maintenance of the infrastructure and tools used to collect, store, process, and analyze large sets of data. This can include things like data warehousing, data lakes, data pipelines, data processing frameworks, and data visualization tools. A data platform engineer will work to ensure that the data infrastructure is scalable, reliable, and able to handle the volume and complexity of data that is being generated by the organization. Additionally, they will work to make data accessible and usable to other teams within the organization, such as data scientists and business analysts.