ICONIX is a software development methodology which predates both the Rational Unified Process (RUP), Extreme Programming (XP) and Agile software development . Like RUP, the ICONIX process is UML Use Case driven but more lightweight than RUP. ICONIX provides more requirement and design documentation than XP, and aims to avoid analysis paralysis . The ICONIX Process uses only four UML based diagrams in a four-step process that turns use case text into working code.
27-480: A principal distinction of ICONIX is its use of robustness analysis, a method for bridging the gap between analysis and design. Robustness analysis reduces the ambiguity in use case descriptions, by ensuring that they are written in the context of an accompanying domain model . This process makes the use cases much easier to design, test and estimate. The ICONIX Process is described in the book Use Case Driven Object Modeling with UML: Theory and Practice . Essentially,
54-487: A description logic . In the field of computer science a conceptual model aims to express the meaning of terms and concepts used by domain experts to discuss the problem, and to find the correct relationships between different concepts. The conceptual model is explicitly chosen to be independent of design or implementation concerns, for example, concurrency or data storage . Conceptual modeling in computer science should not be confused with other modeling disciplines within
81-417: A domain model produced and some prototype GUIs made. Once use cases have been identified, text can be written describing how the user and system will interact. A robustness analysis is performed to find potential errors in the use case text, and the domain model is updated accordingly. The use case text is important for identifying how the users will interact with the intended system. They also provide
108-473: A business plan that follows this strategy. It is often difficult to implement these plans because of the lack of transparency at the tactical and operational degrees of organizations. This kind of planning requires feedback to allow for early correction of problems that are due to miscommunication and misinterpretation of the business plan. The design of data systems involves several components such as architecting data platforms, and designing data stores. This
135-415: A cloud-based environment using the services from public cloud vendors such as Amazon , Microsoft , or Google . If the data is less structured, then often they are just stored as files . There are several options: The number and variety of different data processes and storage locations can become overwhelming for users. This inspired the usage of a workflow management system (e.g. Airflow ) to allow
162-442: A coherent platform. A conceptual model can be described using various notations, such as UML , ORM or OMT for object modelling, ITE , or IDEF1X for Entity Relationship Modelling . In UML notation, the conceptual model is often described with a class diagram in which classes represent concepts, associations represent relationships between concepts and role types of an association represent role types taken by instances of
189-439: A form influenced by design or implementation concerns. This is often used for defining different processes in a particular company or institute. A domain model is a system of abstractions that describes selected aspects of a sphere of knowledge, influence or activity (a domain ). The model can then be used to solve problems related to that domain. The domain model is a representation of meaningful real-world concepts pertinent to
216-542: A much larger scale than databases can allow, and indeed data often flow from databases into data warehouses. Business analysts , data engineers, and data scientists can access data warehouses using tools such as SQL or business intelligence software. A data lake is a centralized repository for storing, processing, and securing large volumes of data. A data lake can contain structured data from relational databases , semi-structured data , unstructured data , and binary data . A data lake can be created on premises or in
243-472: A solution, where the conceptual model provides a key artifact of business understanding and clarity. Once the domain concepts have been modeled, the model becomes a stable basis for subsequent development of applications in the domain. The concepts of the conceptual model can be mapped into physical design or implementation constructs using either manual or automated code generation approaches . The realization of conceptual models of many domains can be combined to
270-498: Is generally implemented as an object model within a layer that uses a lower-level layer for persistence and "publishes" an API to a higher-level layer to gain access to the data and behavior of the model. In the Unified Modeling Language (UML), a class diagram is used to represent the domain model. Information Technology Engineering Data engineering refers to the building of systems to enable
297-517: Is the process of producing a data model , an abstract model to describe the data and relationships between different parts of the data. A data engineer is a type of software engineer who creates big data ETL pipelines to manage the flow of data through the organization. This makes it possible to take huge amounts of data and translate it into insights . They are focused on the production readiness of data and things like formats, resilience, scaling, and security. Data engineers usually hail from
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#1732793344351324-516: The developer with something to show the Customer and verify that the results of the requirements analysis were correct . During this stage of the ICONIX process the domain model and use case text from milestone 2 are used to design the system being built. A class diagram is produced from the domain model and the use case text is used to make sequence diagrams . Unit tests are written to verify
351-512: The ACID transaction guarantees, as well as reducing the object-relational impedance mismatch . More recently, NewSQL databases — which attempt to allow horizontal scaling while retaining ACID guarantees — have become popular. If the data is structured and online analytical processing is required (but not online transaction processing), then data warehouses are a main choice. They enable data analysis, mining, and artificial intelligence on
378-535: The ICONIX Process describes the core "logical" analysis and design modeling process. However, the process can be used without much tailoring on projects that follow different project management. The ICONIX process is split up into four milestones. At each stage the work for the previous milestone is reviewed and updated. Before beginning the ICONIX process there needs to have been some requirements analysis done. From this analysis use cases can be identified,
405-446: The broader field of conceptual models such as data modelling , logical modelling and physical modelling. The conceptual model attempts to clarify the meaning of various, usually ambiguous terms, and ensure that confusion caused by different interpretations of the terms and concepts cannot occur. Such differing interpretations could easily cause confusion amongst stakeholders, especially those responsible for designing and implementing
432-580: The collection and usage of data . This data is usually used to enable subsequent analysis and data science , which often involves machine learning . Making the data usable usually involves substantial compute and storage , as well as data processing . Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of
459-458: The data is structured and some form of online transaction processing is required, then databases are generally used. Originally mostly relational databases were used, with strong ACID transaction correctness guarantees; most relational databases use SQL for their queries. However, with the growth of data in the 2010s, NoSQL databases have also become popular since they horizontally scaled more easily than relational databases by giving up
486-575: The data itself, and data-driven tech companies like Facebook and Airbnb started using the phrase data engineer . Due to the new scale of the data, major firms like Google , Facebook, Amazon , Apple , Microsoft , and Netflix started to move away from traditional ETL and storage techniques. They started creating data engineering , a type of software engineering focused on data, and in particular infrastructure , warehousing , data protection , cybersecurity , mining , modelling , processing , and metadata management. This change in approach
513-402: The data tasks to be specified, created, and monitored. The tasks are often specified as a directed acyclic graph (DAG) . Business objectives that executives set for what's to come are characterized in key business plans, with their more noteworthy definition in tactical business plans and implementation in operational business plans. Most businesses today recognize the fundamental need to grow
540-506: The domain that need to be modeled in software. The concepts include the data involved in the business and rules the business uses in relation to that data. A domain model leverages natural language of the domain. A domain model generally uses the vocabulary of the domain, thus allowing a representation of the model to be communicated to non-technical stakeholders. It should not refer to any technical implementations such as databases or software components that are being designed. A domain model
567-429: The early 2000s, the data and data tooling was generally held by the information technology (IT) teams in most companies. Other teams then used data for their work (e.g. reporting), and there was usually little overlap in data skillset between these parts of the business. In the early 2010s, with the rise of the internet , the massive increase in data volumes, velocity, and variety led to the term big data to describe
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#1732793344351594-401: The modelled concepts in various situations. In ER notation, the conceptual model is described with an ER Diagram in which entities represent concepts, cardinality and optionality represent relationships between concepts. Regardless of the notation used, it is important not to compromise the richness and clarity of the business meaning depicted in the conceptual model by expressing it directly in
621-479: The next few years, Finkelstein continued work in a more business-driven direction, which was intended to address a rapidly changing business environment; Martin continued work in a more data processing-driven direction. From 1983 to 1987, Charles M. Richter, guided by Clive Finkelstein, played a significant role in revamping IEM as well as helping to design the IEM software product (user data), which helped automate IEM. In
648-559: The operational processing needs of organizations for the 1980s. In particular, these techniques were meant to help bridge the gap between strategic business planning and information systems. A key early contributor (often called the "father" of information engineering methodology) was the Australian Clive Finkelstein , who wrote several articles about it between 1976 and 1980, and also co-authored an influential Savant Institute report on it with James Martin. Over
675-543: The operations, and edges represent the flow of data. Popular implementations include Apache Spark , and the deep learning specific TensorFlow . More recent implementations, such as Differential / Timely Dataflow, have used incremental computing for much more efficient data processing. Data is stored in a variety of ways, one of the key deciding factors is in how the data will be used. Data engineers optimize data storage and processing systems to reduce costs. They use data compression, partitioning, and archiving. If
702-470: The system will match up to the use case text, and sequence diagrams. Finally code is written using the class and sequence diagrams as a guide. Domain model In software engineering , a domain model is a conceptual model of the domain that incorporates both behavior and data. In ontology engineering , a domain model is a formal representation of a knowledge domain with concepts, roles, datatypes, individuals, and rules, typically grounded in
729-435: Was particularly focused on cloud computing . Data started to be handled and used by many parts of the business, such as sales and marketing , and not just IT. High-performance computing is critical for the processing and analysis of data. One particularly widespread approach to computing for data engineering is dataflow programming , in which the computation is represented as a directed graph (dataflow graph); nodes are
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