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Endeca allows the mapping of sales transactional records free dating beamie24 product details and employee information through a product ID and a sales rep ID. For the sales transaction record, it is possible to extend it with more information about specific products.

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Semistructured data from enterprise applications, various feeds, and XML sources can also be loaded as attribute and value pairs. One of the Dgraph processes is designated as the leader node, and all other Dgraph processes are referred to as follower nodes.

Data records generally correspond to traditional records in a source database, but differ in that they are standardized for consistency and classified with attributes.

Records and Attributes Records are the fundamental unit of data in a data domain on Endeca Server.

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Endeca uses the DEFINE statement to create views that can then be used to generate charts and other advanced visualization.

These attributes can be combined in the same record with the sales transactions, product details, and customer review information.

These services are used by other Endeca components to interact with Endeca Server. With Endeca, attributes become attribute and value pairs, and jagged records begin to emerge that look dissimilar, meaning the data sets do not have the same data model but have some commonality between them.

Dgraph maintains indexes of records that are searchable documents of domain data associated with attributes.

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Conversation Web Service This web service provides the primary means of querying data in the data domain hosted in Oracle Endeca Server. The way to accomplish this through Oracle Endeca Information Discovery is to take the textual fields into the records as new attributes.

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As data is loaded, or ingested, it is stored in data records. As an example, consider opinion data ingested from unstructured data regarding consumer opinions on automobiles.

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An example of such data is a sales transaction. The leader node handles all write and update requests, while the follower nodes allow only read operations.

Data Domains Data domains are the largest unit of data over which Endeca Server allows queries to be expressed. The Dgraph process of Endeca Server is the main computational module that provides the features of Endeca Server, such as search, refinement computation, and guided navigation.

With Endeca Server, unstructured data can be linked to records by any available key.

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Each transaction row becomes a record, and every element of the transaction record becomes an attribute. Nodes can be added to an Endeca Server cluster as additional processing needs emerge, ensuring the scalability of the Endeca Server cluster.

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Expressions are usually one or more attributes, operators, or functions such as summation or average, as you see in the previous example.

The current version of Endeca Server is 7.

This is an example of a simple EQL statement: Now unstructured, semistructured, and structured data is aligned and loaded and could be analyzed side by side.

The online reviews can also be mapped to the transaction records through transaction IDs that are captured. IT professionals have full access to the language for the purpose of building special formulas, metrics, and more that can be made available in discovery applications.

Some examples are documents, RSS feeds, Twitter and Facebook data, and data feeds from discussion forums. Users can specify meaningful names such as staffing, sales, or marketing to identify data domains.

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Here is a summary of the data sources in the examples we mentioned: Rather, it creates the data set as a temp table. The node hosting the leader node Dgraph process must also host the Cluster Coordinator service, which is responsible for intercluster communications between Dgraph processes and for notifying follower nodes when indexes or data has changed.

No data domains exist on an Endeca Server instance immediately after installation, so a server administrator must create each data domain.

Entity and Collection Configuration Web Service This web service, also known as sConfig, allows you to create and update collections and views for collections or for data sets. Configuration Web Service This web service is for updating the schema and configuring the records in a data domain.

Some of the most important EQL features include tight integration with search and navigation, rich analytical functionality, and processing efficiency, as well as a familiar development experience. This is a common cause of heterogeneous record structure.

Built on the core capabilities of Oracle Endeca Server, EQL extends the capabilities of Oracle Endeca Server with a rich analytic language that allows users to explore aggregated and pivoted views of large volumes of data.

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Each row becomes a record, and each column becomes an attribute. A shared file system is used for disk-based versions of indexes, and only the leader node has write access to the file system.

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When more processing capability is required than can be achieved on one Endeca Server instance, an Endeca Server cluster can be deployed.

In addition, unstructured elements can be stored as their own records for side-by-side analysis. EQL enables power users to define and create new metrics to compose their own discovery applications.

The central feature of the Endeca cluster architecture is a data domain cluster, which is a set of Dgraph processes that handles requests across multiple nodes.

In addition, users can create a whitelist text tagger to tag the employee first name or last name mentioned in the review text to an employee by full name. Endeca Server Version Information Be aware that Endeca Server version numbers do not coincide with the version numbers of the other core components.

The major services are briefly discussed in the sections that follow.

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For example, a road bike has different components than a mountain bike. Other EQL capabilities include joining, sorting, paging, and filtering. Endeca Server Clustering Endeca Server clustering can be used in production deployments to handle heavy workloads and is deployed across multiple servers.

With relational database technologies, this is difficult to implement, but the key-value pair data structure in Endeca makes it possible to implement and extend. The attributes that gowith each product could be very different depending on the product. The Dgraph is stateless, which facilitates the addition of Dgraph processes for load balancing and redundancy.