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2. Data engineering - Wikipedia

   en.wikipedia.org/wiki/Data_engineering

   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. [3] [4] These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s.

3. Big data - Wikipedia

   en.wikipedia.org/wiki/Big_data

   Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. [26] Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale. [27]

4. Lambda architecture - Wikipedia

   en.wikipedia.org/wiki/Lambda_architecture

   Lambda architecture depends on a data model with an append-only, immutable data source that serves as a system of record. [2]: 32 It is intended for ingesting and processing timestamped events that are appended to existing events rather than overwriting them. State is determined from the natural time-based ordering of the data.

5. Software engineering - Wikipedia

   en.wikipedia.org/wiki/Software_engineering

   Software engineering is a field within computer science focused on designing, developing, testing, and maintaining of software applications. It involves applying engineering principles and computer programming expertise to develop software systems that meet user needs.

6. Very large database - Wikipedia

   en.wikipedia.org/wiki/Very_large_database

   The vague adjectives of very and large allow for a broad and subjective interpretation, but attempts at defining a metric and threshold have been made. Early metrics were the size of the database in a canonical form via database normalization or the time for a full database operation like a backup.

7. Big data maturity model - Wikipedia

   en.wikipedia.org/wiki/Big_Data_Maturity_Model

   The TDWI big data maturity model is a model in the current big data maturity area and therefore consists of a significant body of knowledge. [6] Maturity stages. The different stages of maturity in the TDWI BDMM can be summarized as follows: Stage 1: Nascent. The nascent stage as a pre–big data environment. During this stage: