EIQ Adapters™ and sub-middleware form the core of SmartData Fabric® EIQ Products™, and include the tools and processes to discover data sources, profile data, classify and secure data, develop data quality transforms, build and maintain indexes and process queries.

EIQ Adapters™, in particular, EIQ SuperAdapters™, offer significant advantages over conventional federated adapters and these advantages are discussed in the following documents:

A detailed description of EIQ Adapters is available at EIQ Adapters in detail.

The three EIQ Adapters products and one sub-middleware product currently offered are:

  • EIQ SuperAdapters for data source access with standard data view mapping
    A superior alternative to conventional federated adapters, offered on a data source basis; includes basic data discovery and profiling, index and results data cleansing, transformation and standardization, standard data model mapping and associated query transformation. Results from an EIQ SuperAdapter are provided in the same standard data view format as results from other EIQ SuperAdapters™, each accessing individual data sources. WhamTech EIQ Federation Server™ sub-middleware and/or other third-party middleware products can combine results as needed.
  • EIQ TurboAdapters™ for data source access without standard data view mapping
    A query enhancer that resides between an application or a conventional federated adapter, and a data source system, similar to an EIQ SuperAdapter, but without the ability to transform queries and map results to a standard data view. Indexes and results data can be cleansed, transformed and standardized.
  • EIQ ConventionalAdapters™ for conventional data source access without indexes with standard data view mapping
    In the situation where none of the twelve change data capture (CDC) options are available to build and maintain indexes or perhaps data schema access is unavailable, EIQ ConventionalAdapters can be used and are similar to conventional federated adapters.  Queries are submitted to, and transformed by EIQ ConventionalAdapters; however, queries are submitted to, and executed by, data sources.  Results data can be cleansed, transformed and standardized.
  • EIQ Federation Servers for other EIQ Product integration - no direct data source access - with standard data view mapping
    A virtual data integration, sharing and interoperability sub-middleware query management system, to access multiple data sources each of which must have an associated EIQ SuperAdapter, EIQ TurboAdapter or EIQ ConventionalAdapter. EIQ Federation Servers do not have their own indexes, query processing or results transformation, but can distribute queries and combine results data from other EIQ Products, including other EIQ Federation Servers. EIQ Federation Servers can access other EIQ Federation Servers as though they are EIQ Adapters and can be independently configurable, allowing for high performance, scalability, load balancing, failover and redundancy.

The following table lists the major feature differences among the products:

Feature EIQ
Federation Server
Standard driver/Web/data services access yes yes yes yes
ANSI SQL (and other query languages with or without conversion) yes yes yes yes
Basic pass-through security yes yes yes yes
Works with multiple data sources (no)
only through ATTACH*
only through ATTACH*
no yes
through other EIQ Products™
Sold on a data source basis yes yes yes no
User-level data source access yes yes yes yes
through other EIQ Products™
Index cleansing and transformation yes yes no no
Standard data model and metadata yes no yes yes
Query transformation from standard data model and metadata to indexes yes no (yes)
to data source indexes; not adapter indexes
Results data cleansing and transformation (does not necessarily include standardization) yes yes yes no
Advanced security on standard data model and metadata (available soon as Security and Privacy Access Profiles - SPAPs) yes no yes yes

*ATTACH allows disparate data source indexes to be integrated as though a single set of indexes, similar to merging schemas, however, this is not a highly scalable solution and requires a higher level of cooperation among data source

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