Definition: Data Integration

DDL – Data Definition Language (DDL)

DML – Data Manipulation Language (DML)


EDI – Electronic Data Interchange (EDI)

FTP – File Transfer Protocol

ETL – extract, transform and load, the processes that enable companies
to move data from multiple sources, reformat and cleanse it, and load
it into another database, a data mart or a data warehouse for analysis,
or on another operational system to support a business process.

EL – Extract and Load – from a data movement perspective, extract and
load – basically detecting existence of new/changed data, and doing a
delta comparison is all that’s needed. Icing on the cake is having a
visual no-code, drag and drop development paradigm that handles and
manages metadata along the way.

T – Transformation – the process of converting the extracted data from
its previous form into the form it needs to be in so that it can be
placed into another database. Transformation occurs by using rules or
lookup tables or by combining the data with other data.  The bottleneck
in getting data into any data store quickly

ELT – Extract, Load and Transform –  shift the "T" bottleneck
from the ETL into ELT, we have a very strong case for scalability by leveraging the power of your data warehouse – the
resulting engine leveraging the best-of-breed, latest RDBMS
capabilities, and taking advantage of every ounce of scalability and
parallelism (and load-balancing) that the RDBMS can muster up.

EII – Enterprise Information Integration

EAI – Enterprise Application Integration

ESB – Enterprise Service Bus – is an open standards-based distributed
synchronous or asynchronous messaging middleware that provides secure
interoperability between enterprise applications via XML, Web services
interfaces and standardized rules-based routing of documents. In
practice, this means that data files are passed to and from their
destinations based on pre-established guidelines that are common to all
parties sharing the information to ensure that the data maintains its
integrity as it is routed. The multi-language and multi-platform design
of an ESB allows enterprises to process data between applications from
various sources. Two common distributed computing architectures used by
ESBs are J2EE and .NET.  ESB is an extension of EAI, an earlier form of middleware, but ESB adds several key functions:

  • transformation – the ability to transform XML documents from one
    data format into another so that the receiving party can interface with
    the data in an application format that is different from the one in
    which it is sent.
  • portability – the ability to share the data between different computer systems and operating environments.
  • load balancing/clustering – the ability to distribute processing among several devices so that no one device becomes overloaded
  • failover – the ability to transfer messaging functions to another server if one should fail during the data exchange.

EMB – Enterprise Message Bus

DSOA – Data Service Oriented Architecture

GTDC – Get the data to the customer

Messaging Layer

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