Tekslate basically offers the online instructor-led training. Apart from that we also provide corporate training for enterprises. All our trainers come with over 12 years of industry experience in relevant technologies and also they are subject matter experts working as consultants.

Our trainers have relevant experience in implementing real-time solutions on different queries related to different topics of Secondary index dimensional data modeling training. Tekslate also verifies their technical background and expertise.

We record each LIVE class session you undergo through and we will share the recordings of each session/class.

If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers. The best part is that you can contact Tekslate even after completion of Dimensional data model training to get support and assistance. There is also no limit on the number of queries you can raise when it comes to doubt clearance and query resolution.

You will work on real world Data modeling Online Courses wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready. These projects could be in exciting and challenging fields like banking, insurance, retail, social networking, e-commerce, marketing, sales, high technology and so on.

Our Trainers will provide the Environment/Server Access to the  and we ensure practical real-time experience in data modeler Online by providing all the utilities required for the in-depth understanding of the course.

If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within 48 hours of initial registration. Please make a note that refunds will be processed within 30 days of prior request.

The Data Modeling Training Online Course Training itself is Real-time Project Oriented.

Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.

There are some Group discounts available on the  Certification Cost if the participants are more than 2.

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Online Data Modeling Training in USA:

• New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas, San Jose, Austin, Jacksonville, San Francisco, Columbus, Indianapolis, Fort Worth, Charlotte, Seattle, Denver, El Paso, Washington, Boston, Detroit, Nashville, Memphis, Portland, Oklahoma City, Las Vegas, Louisville, Baltimore, Milwaukee, Albuquerque, Tucson, Fresno, Sacramento, Mesa, Kansas City, Atlanta, Long Beach, Colorado Springs, Raleigh, Miami, Virginia Beach, Omaha, Oakland, Minneapolis, Tulsa, Arlington, New Orleans, Wichita, Cleveland, Tampa, Bakersfield, Aurora, Honolulu, Anaheim, Santa Ana, Corpus Christi, Riverside, Lexington, St. Louis, Stockton, Pittsburgh, Saint Paul, Cincinnati, Anchorage, Henderson, Greensboro, Plano, Newark, Lincoln, Toledo, Orlando, Chula Vista, Irvine, Fort Wayne, Jersey City, Durham, St. Petersburg, Laredo, Buffalo, Madison, Lubbock, Chandler, Scottsdale, Glendale, Reno, Norfolk, Winston–Salem, North Las Vegas, Irving, Chesapeake, Gilbert, Hialeah, Garland, Fremont, Baton Rouge, Richmond, Boise, San Bernardino.

Popular cities of Canada, like:

• Toronto, Montreal, Vancouver, Edmonton, Hamilton, Ottawa, Calgary, Ontario, Qubec etc

Online Data Modeling Training in India:

•  Online Data Modeling Training in Hyderabad, Online Data Modeling Training in Bangalore, pune, Delhi, Mumbai.

Along with it, we also prevail our valuable online training in the places of UK, Australia, India and other parts of the world

What is Data Modeling?

This is the process of structuring and organizing data. These data structures are then typically implemented in a database management system. In addition to defining and organizing the data, data modeling may also impose constraints or limitations on the data placed within the structure.

Managing large quantities of structured and unstructured data is a primary function of information systems. Data models describe structured data for storage in data management systems such as relational databases. They typically do not describe unstructured data, such as word processing documents, email messages, pictures, digital audio, and video. Early phases of many software development projects emphasize the design of a conceptual data model. Such a design can be detailed into a logical data model. In later stages, this model may be translated into physical data model.

Data Model

The term data model actually refers to two very different things: a description of data structure and the way data are organized using, for example, a database management system.

Data Structure

A data model describes the structure of the data within a given domain and, by implication, the underlying structure of that domain itself. This means that a data model in fact specifies a dedicated ‘grammar’ for a dedicated artificial language for that domain. Because there is little standardisation of data models, every data model is different. This means that data that is structured according to one data model is difficult to integrate with data that is structured according to another data model. A data model may represent classes of entities (kinds of things) about which a company wishes to hold information, the attributes of that information, and relationships among those entities and (often implicit) relationships among those attributes. The model describes the organization of the data to some extent irrespective of how data might be represented in a computer system.

The entities represented by a data model can be the tangible entities, but models that include such concrete entity classes tend to change over time. Robust data models often identify abstractions of such entities. For example, a data model might include an entity class called “Person”, representing all the people who interact with an organization. Such an abstract entity class is typically more appropriate than ones called “Vendor” or “Employee”, which identify specific roles played by those people.

A proper conceptual data model describes the semantics of a subject area. It is a collection of assertions about the nature of the information that is used by one or more organizations. Proper entity classes are named with natural language words instead of technical jargon. Likewise, properly named relationships form concrete assertions about the subject area. For example, a relationship called “is composed of” that is defined to operate on entity classes “Order” and “Line item” forms the following concrete assertion definition: Each “Order” “is composed of” one or more “Line items.” Note that this illustrates that often generic terms, such as “is composed of”, are defined to be limited in their use for a relationship between specific kinds of things, such as an order and an order line. This constraint is eliminated in the generic data modeling methodologies.

Generic Data Modeling

Generic data modeling has the following characteristics:

• A generic data model shall consist of generic entity types, such as ‘individual thing’, ‘class’, ‘relationship’, and possibly a number of their subtypes.
• Every individual thing is an instance of a generic entity called ‘individual thing’ or one of its subtypes.
• Every individual thing is explicitly classified by a kind of thing (‘class’) using an explicit classification relationship.
• The classes used for that classification are separately defined as standard instances of the entity ‘class’ or one of its subtypes, such as ‘class of relationship’. These standard classes are usually called ‘reference data’. This means that domain specific knowledge is captured in those standard instances and not as entity types. For example, concepts such as car, wheel, building, ship, and also temperature, length, etc. are standard instances. But also standard types of relationship, such as ‘is composed of’ and ‘is involved in’ can be defined as standard instances.

This way of modeling allows to add standard classes and standard relation types as data (instances), which makes the data model flexible and prevents data model changes when the scope of the application changes.

A generic data model obeys to the following rules:

•  Candidate attributes are treated as representing relationships to other entity types.
•  Entity types are represented, and be named after, the underlying nature of a thing, not the role it plays in a particular context. Entity types are chosen
• Entities have a local identifier within a database or exchange file. These should be artificial and managed to be unique. Relationships are not used as part of the local identifier.
•  Activities, relationships and event-effects are represented by entity types (not attributes).
•  Entity types are part of a sub-type/super-type hierarchy of entity types, in order to define a universal context for the model. As types of relationships are also entity types they are also arranged in a sub-type/super-type hierarchy of types of relationship.
• Types of relationships are defined on a high (generic) level, being the highest level where the type of relationship is still valid. For example, a composition relationship (indicated by the phrase: ‘is composed of’) is defined as a relationship between an ‘individual thing’ and another ‘individual thing’ (and not just between e.g. an order and an order line). This generic level means that the type of relation may in principle be applied between any individual thing and any other individual thing. Additional constraints are defined in the ‘reference data’, being standard instances of relationships between kinds of things.

Data organization

Another kind of data model describes how to organize data using a database management system or other data management technology. It describes, for example, relational tables and columns or object-oriented classes and attributes. Such a data model is sometimes referred to as the physical data model, but in the original ANSI three schema architecture, it is called “logical”. In that architecture, the physical model describes the storage media (cylinders, tracks, and tablespaces). Ideally, this model is derived from the more conceptual data model described above. It may differ, however, to account for constraints like processing capacity and usage patterns.

While data analysis is a common term for data modeling, the activity actually has more in common with the ideas and methods of synthesis (inferring general concepts from particular instances) than it does with analysis (identifying component concepts from more general ones). Data modeling strives to bring the data structures of interest together into a cohesive, inseparable, whole by eliminating unnecessary data redundancies and by relating data structures with relationships.

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