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.

As we are one of the leading providers of Training in Data Modeling , We have customers from:

Popular cities of USA, like:

• New Jersey, Los Angeles, Charlotte, Chicago, Dallas, San Jose, Washington, Houston, San Francisco, Oklahoma City, Las Vegas, Baltimore, Kansas City, Pittsburgh, Orlando, Connecticut, Irving, Richmond and other predominant places.

Data Modeling Training in New York

The City of New York, often called New York City (NYC) or simply New York, is the most populous city in the United States.New York City is also the most densely populated major city in the United States. Located at the southern tip of the state of New York, the city is the center of the New York metropolitan area, the largest metropolitan area in the world by urban landmass and one of the world’s most populous mega cities. Silicon Alley, centered in Manhattan, has evolved into a metonym for the sphere encompassing the New York City metropolitan region’s high technology industries involving the Internet, new media, telecommunications, digital media, software development, biotechnology, game design, financial technology (“FinTech”), and other fields within information technology that are supported by its entrepreneurship ecosystem and venture capital investments.

Data Modeling Training in Houston

Houston is the most populous city in the U.S. state of Texas and the fourth most populous city in the United States. Houston is recognized worldwide for its energy industry—particularly for oil and natural gas—as well as for biomedical research and aeronautics. Renewable energy sources—wind and solar—are also growing economic bases in the city.

Data Modeling Training in Chicago

The Chicago metropolitan area, often referred to as “Chicagoland”, has nearly 10 million people and is the third-largest in the United States and fourth largest in North America. Positioned along Lake Michigan, the city is an international hub for finance, commerce, industry, technology, telecommunications, and transportation. The city claims two Dow 30 companies: aerospace giant Boeing, which moved its headquarters from Seattle to the Chicago Loop in 2001 and Kraft Heinz.

Data Modeling Training in Dallas

Dallas is the most populous city in the Dallas–Fort Worth metroplex, which is the fourth most populous metropolitan area in the United States. The economy of Dallas is considered diverse, with dominant sectors including defense, financial services, information technology, telecommunications and transportation. It serves as the headquarters for 9 Fortune 500 companies within the city limits.

Data Modeling Training in San Jose

San Jose officially the City of San Jose is an economic, cultural and political center of Silicon Valley and the largest city in Northern California. San Jose is a global city, notable as a center of innovation, for its affluence,weather, and high cost of living. San Jose’s location within the booming high tech industry, as a cultural, political, and economic center has earned the city the nickname “Capital of Silicon Valley”.

Data Modeling Training in Hyderabad 

TekSlate is the leading training provider in Hyderabad. Hyderabad popularly known as the City of Pearls & is the capital city of Andhra Pradesh. The city popular for its Film City and Charminar, Hyderabad is also a growing metropolitan area of the South. The city has been a prosperous pear and diamond trading center for the nation from years. Alongside, many manufacturing and financial institutions entered the city with industrialization. Also the flourishing pharmaceutical and biotechnology industries in Hyderabad earned it the title of India&  pharmaceutical capital. The city is home to more than 1300 IT firms including Google, IBM, Yahoo, Dell, Facebook, Infosys, TCS, Wipro and more.

Data Modeling Training in Bangalore

TekSlate is the leading training provider in Bangalore. It is the capital of the Indian state of Karnataka. It has a population of over ten million, making it a megacity and the third most populous city and fifth most populous urban agglomeration in India.  Bangalore is sometimes referred to as the “Silicon Valley of India” (or “IT capital of India”) because of its role as the nation’s leading information technology (IT) exporter. Indian technological organisations ISRO, Infosys, Wipro and HAL are headquartered in the city.

Data Modeling training in Chennai

Madras is divided into four broad regions: North, Central, South and West. North Madras is primarily an industrial area. South Madras and West Madras, previously mostly residential, are fast becoming commercial, home to a growing number of information technology firms, financial companies and call centers.

Data Modeling Training in Pune

Pune is known as “Oxford of the East” due to the presence of several well-known educational institutions. The city has emerged as a major educational hub in recent decades, with nearly half of the total international students in the country studying in Pune. Research institutes of information technology (IT), education, management and training in the region attract students and professionals from India and overseas. Several colleges in Pune have student-exchange programs with colleges in Europe.

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

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.

As we are one of the leading providers of Online training, We have customers from:

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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