According to the Data Management Body of Knowledge (DMBOK), Data Architecture “includes specifications used to describe existing state, define data requirements, guide data integration, and control data assets as put forth in a data strategy.” Data Architecture bridges business strategy and technical execution, and according to our 2017 Trends in Data Architecture Report: “Data Architecture is as much a business decision as it is a technical one, as new business models and entirely new ways of working are driven by data and information.”
Data modeling is the process of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the way data needs to flow. The diagram can be used to ensure efficient use of data, as a blueprint for the construction of new software or for re-engineering a legacy application.
Data modeling is an important skill for data scientists or others involved with data analysis. Traditionally, data models have been built during the analysis and design phases of a project to ensure that the requirements for a new application are fully understood. Data models can also be invoked later in the data lifecycle to rationalize data designs that were originally created by programmers on an ad hoc basis.
An ETL Developer is an IT specialist who designs data storage systems for companies, and works to fill that system with the data that needs to be stored. ETL stands for “extract, transform, load,” which is the process of loading business data into a data warehousing environment, testing it for performance, and troubleshooting it before it goes live. ETL Developers must be experts at taking a big-picture view of a company’s data situation to come up with comprehensive data storage solutions.
ETL Developers generally work as part of a team. They are sometimes employed by a single company. Or, they may act as consultants to multiple organizations. According to the Bureau of Labor Statistics and demand for Database Administrators, which includes ETL Developers, is expected to increase 11 percent through 2024. This growth will be driven by the increased data needs of companies, which will also increase the need for ETL Developers.
Performance tuning is the improvement of system performance. Typically in computer systems, the motivation for such activity is called a performance problem, which can be either real or anticipated. Most systems will respond to increased load with some degree of decreasing performance. A system's ability to accept higher load is called scalability, and modifying a system to handle a higher load is synonymous to performance tuning.
Database administration is the function of managing and maintaining database management systems (DBMS) software. Mainstream DBMS software such as Oracle, IBM DB2 and Microsoft SQL Server need ongoing management. As such, corporations that use DBMS software often hire specialized information technology personnel called database administrators or DBAs.
Incorta is a radical, modern approach to the concepts of analytics and business intelligence. As an end-to-end analytics platform, Incorta sources data, stores data, and provides a visualization interface for analytics all in a single platform.
Where traditional approaches require complex modeling and reshaping of data, Incorta allows you to analyze the data in its original shape and preserves the original data fidelity. If your underlying data source has 190 tables with 358 joins across some four-billion rows of data, Incorta allows for direct analysis of that data with all the joins occurring dynamically and access to all of the detailed transactions. Further, the analyses, including dynamic aggregations on that data, occur in seconds or sub-seconds. There are no star schemas or cubes.
By leveraging data in shape, Incorta lets you deploy an analytic solution quickly and using a much simpler process compared to traditional technologies. With traditional approaches, the data is extracted from the source system (usually using an ETL tool), modeled into a data warehouse or data mart structure, a semantic model is created, and finally, reports and dashboards are put in place. It is not uncommon for this process, in a traditional deployment to take 37-88 weeks (or more).
Incorta, conversely, removes many of those steps. Data is loaded, in shape, to Incorta. An optional semantic layer can be placed on top of the data to group like elements for analysis, and then users leverage both packaged and self-service/ad hoc reporting capabilities. With Incorta, deployments average 6-18 weeks in total.