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Data Integration Roundup

Data science platforms (such as those discussed in Website Magazine's January 2018 issue) serve as centralized integration instances, where the work of computer scientists, engineers and developers is brought together with the abundance of proprietary and 3rd-party data in order to be managed and ultimately analyzed.

What makes for the ideal platform? Simple - the top providers (like those mentioned in WM's recently updated list of Tools to Manage Big Data) make the ease of data integration a priority.

Data integration, of course, requires mastery of a great deal of individual highly technical practices and familiarity with an immense amount of terminology. So what is data integration and what must your enterprise consider as it begins this process? Check out some of the key terms to know as you travel the world of digital data integration: 
 


  • Data Integration

    Involves combining data from several different sources using various technologies in order to provide a better and more unified view into performance.

  • Data Warehouse

    A large store of data accumulated from a wide range of sources within g a company and use to guide management decisions.

  • Data Migration

    The process of transferring data, typically performed in a programmatic manner, between storage types and formats.

  • Data Conversion

    The process of converting one format of digital data into another format; perhaps from a CSV file to a SQL database.

  • Field Mapping

    The process of matching the available fields of data in one database to ensure consistency across all systems.

  • Application Integration

    The sharing of processes and data among different applications using real-time communication to increase efficiency and enhance scalability.

  • Cluster

    A means of storing data together from multiple tables when the data contains common information that is needed for analysis.

  • Connector

    Software used to create a data connection between different digital application. Sometimes referred to as middleware.

  • Data Cleansing

    Transforming data in its native state to a pre-defined standardized format using vendor software.

  • Data Mining

    Extracting previously unknown information from databases and using that data for important business decisions, in many cases helping to create new insights.

  • Data Replication

    The frequent copying of data between databases so all users have the same information, resulting in a distributed database.

  • Decision Support System (DSS)

    A system that supports organizational decision making activities. Often used when data changes rapidly or is not easy to extrapolate.

  • Data Virtualization

    Allows applications to retrieve and manipulate data without requiring technical details about the data; seen as an alternative to the traditional ETL process.

  • Integration Platform as a Service (iPaaS)

    A suit of cloud services enabling the execution and governance of Data Integration flows connecting to on-premise and cloud-based processes.

  • Relational Database Management System (RDBMS)

    A system used to store data manged in relational tables, typically organized according to the relationship between different data values.

  • Schema

    The structure that defines how data inside a database is organized. The unified, reconciled view of the data, called a global schema, can be queried by a user.

  • Extract, Transform, Load (ETL)

    In managing databases, extract, transform, load (ETL) refers to three separate functions combined into a single programming tool. ETL processes pull data from one data base and place it into another database.

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