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What is Data architecture? Components, Frameworks, Characteristics & Salary

What is Data architecture?

Data architecture is a standardized process of an organization for the collection, storage, and management of data. It describes the organizational structure of data assets along with the resources of data management. Proper organization of the data will help those people who need the data. It comprises all the rules, policies, models, and standards to maintain the data in the organization. 

The data architecture lays the foundation of a business strategy with its aim towards the translation of business needs into data and system requirements. It also regulates the management and flow of data throughout the enterprise.

Earlier, the II system played the role of data supply. Any business strategist who would require the data would have to contact the IT department. The IT would then create a proper system for delivering the data. The process was quite time-consuming and tedious. Further, the strategist would receive data that seemed to be different than what has been requested. Therefore there was a limit in the business strategy due to the associated difficulties in accessing the right data. 

The present era has seen a shift in the growth of data. With the availability of data in real-time data through different sources, data analysis has become a crucial thing for business organizations. It is possible through the data mining architecture that assists in identifying essential data and analyzing it. The business strategists have started demanding more data to get a faster insight into the data which is possible through the proper storage and management of the data.

If the data is well structured and organized, the experts would know what information from the data is important for propelling the business growth.  One of the main goals of a data architecture design is that the business strategist and the technical expertise could work together into the data.

The development of the data architecture is the result of the development of cloud technology. It is through the development of cloud technology that big data has seen a shift towards the real world. 

Therefore,

  • Data architecture gives an idea of what is happening in a company.
  • The company’s data is better understood.
  • A proper process for movement of data from the source to analysis and decision making is defined.
  • Ensures the security of data.
  • All the teams in an organization have the ability to make decisions from the data.

Who is a Data Architect?

The mastermind who is behind the data architecture is the data architect. It’s the role of the data architect to translate all the needs of a business into the requirements based on the data and system.  To meet the objectives of the business, a roadmap defining the technical details is created by the data architect.

Multiple sources are required to collect the data, store it, and then distribute it to those people who need it. This is done by creating blueprints of the process. The role of the data architect is to define a data strategy and is can do this through:

  • Business requirements are transformed into requirements needed technically. 
  • The architecture of the data, which includes the standards used for the data models, security, metadata, reference data are defined. Reference data includes product catalogs and data where suppliers and inventory are mentioned. 
  • A structure to be used by decision makers for creating and improving data systems is defined.
  • Data flow through the enterprise is defined. It includes the information related to which part generates the data, uses that data, and how the flow is managed.

Components of Data Architecture

The several components of present-day data architecture are:

  1. Data Pipelines: It covers the process of data collection, its refinement, storage, analysis, and the flow of data from one point to the other. The entire process from where data is collected and transferred to and how it is moved is covered by the data pipelines. 
  2. Cloud storage: The cloud refers to an off-site location where the data is stored that can be accessed only through the internet. 
  3. API’s: The API enables the communication between the host and a requester. The communication is established through an IP address. Multiple types of information can be communicated to the user by the API like
  4. AI & ML models: AI and ML provide an automated system for the data architecture.  Calculated decisions can be made and predictions can be made along with data collection, labeling, etc.
  5. Data streaming: It refers to the process of a continuous flow of data from a source to a destination and which needs to be processed for their real-time analysis. 
  6. Kubernetes: It is the platform for computing, networking, and storage infrastructure workload
  7. Cloud computing: It refers to the process whereby the data is analyzed, stored, and managed through the cloud. The applicability of cloud computing provides benefits like low cost, secured data, and no requirement for managing the IT infrastructure as it is managed by the cloud.
  8. Real-time analytics: It involves the process of analysis of the real-time data to get an insight into the data. Based on this analysis, the organizations can make their decisions.

Frameworks

Several frameworks exist over which the data architecture of an organization is built up.

1. DAMA-DMBOK 2

This framework is specifically for data management and is known as the DAMA International’s Data Management Body of Knowledge. The framework holds the guiding principle for the management of the data and provides definitions for several terminologies which follow the standard definitions.

2. Zachman Framework for Enterprise Architecture

John Zachman in the 1980s created the Zachman Framework at IBM. Multiple layers are present in the column “data”. These layers include architectural standards that are meant to be important for the business, a semantic model, an enterprise/logical model of data, actual databases, and a physical model of data.

3. The Open Group Architecture Framework (TOGAF)

The framework is used for the development of software for enterprises. The architecture of the data and the roadmap is created in Phase C of TOGAF.

Characteristics of Data Structure

The modern-day data architecture follows certain characteristics which are listed below:

1. User–driven

The data architecture has the ability to provide the users with the data as they want it. Compared to the past, data was static and the decision-makers were unable to collect the required data. However, in the present scenario, due to the availability of modern data structure, the decision-makers are able to define their requirements and access them to meet the business objectives.

2. Built on shared data

The modern-day architecture demands shared data through the combination of data from different parts of the organization. The data is then collected in one place.

3. Automated

Earlier the delivery of the data and maintenance of the data was a tedious task. Also, the processes took months for their completion. With automated systems, these processes can be carried out within hours. Further with the availability of automated pipelines, the user can get access to different types of data.

4. AI driven

The automation of the data structure is carried out to the level of machine learning (ML) and artificial intelligence (AI). With the application of AI and ML, any type of quality error can be fixed along with the automatic organization of the incoming data into structures. Based on this the automated system can recommend related data sets and analytics.

5. Elastic

The organization might scale up or down as they need based on the data architecture. The elasticity property of a data architecture leads to problem-solving by the administrator.

6. Simple

An efficient data structure should have a simple structure for simple movement of the data, simple data platforms, simple frameworks for data assembly, and simple analytic platforms.

7. Secure

The modern-day data architecture ensures security as it recognizes emerging threats and delivers data on a need-to-know basis as defined by the business.

Best Practices

The following practices should be welcomed while developing a strategy for data architecture.

1. The process is driven by collaboration.

Collaboration between the business and the department of IT of an enterprise plays an important role in decision-making processes. Therefore good data architecture allows collaboration of goals shared between the departments and its outcomes.

It is the decision-makers that will determine which data is essential for making an impact in their organization. Based on this a path is built upon by the data architect ensuring that the data is accessible and sourced.

2. Prioritize data governance

For making effective decisions, the data should be of high quality. Also, data mining architecture involves the use of highly relevant data. Further, the data should target the specific needs of the business. Therefore the organizational data should be cleaned which requires the role of the data stewards. The internal experts in this case can become data stewards to enhance the quality of the data.

3. Attain agility.

As the present-day scenario demands newer technologies, the data architecture must have the ability to adapt to these changes. Therefore, the data architecture should not be based on a specific technology. As the data types might change with time along with the change in tools and the platforms, the data architecture should be able to accommodate these changes.

Data Architect Roles and Salary in India

A data architect in India has a national average salary of ₹19,50,000. A few popular job titles for a data architect along with the annual salaries have been listed below. 

  • Database architect: ₹ 95,090 
  • Senior Data Architect: ₹ 23,65,898
  • Data Modeler: ₹ 36,595
  • Data Warehouse Architect: ₹ 12,55,652

Read to learn more about data architect salary in India.

Conclusion

The article discussed the importance of data architecture in an organization along with the importance of a data architect. Also, several roles are offered to a data architect with a good salary. Pursuing the knowledge of data analysis, and architecture might be a future-changing opportunity for all those who are willing to work in this field. 

If you are eager to start your career as a data architect and want to learn more about data science, you can check out the course Executive PG Programme in Data Science, provided by upGrad and IIIT-Bangalore. The course is designed for entry to mid-level professionals and offers training from top industry experts.

With 60+ industry projects, hands-on experience over 14+ programming tools and languages, and live sessions, the course will provide job assistance with top firms. If you are willing to enroll and have any queries, drop us a message. We will provide you with the assistance ship.

What are the basic to advanced level skills required to become a data architect?

The most in-demand skills that every data architect should have under their belt are:
1. Proficiency in Applied Mathematics and Statistics skills to be able to perform data analytic techniques.
2. Good understanding of data migration and data visualization tools.
3. Strong database fundamentals including DBMS, RDBMS, NoSQL, and a basic understanding of cloud computing for managing the resources.
4. Good command in Machine Learning concepts, data modelling, and predictive analysis.
5. Proficiency in programming languages such as Python, Java, and C/C++.
6. Knowledge of operating systems, and system development life cycle including design, implementation, code, test, and debugging.
7. Non-technical skills include a business-oriented approach, creative thinking, problem, solving ability, and analytical skills.

What do you understand by cluster analysis? State its characteristics.

A process in which we define an object without labelling it is known as cluster analysis. It uses data mining to group various similar objects into a single cluster just like in discriminant analysis. Its applications include pattern recognition, information analysis, image analysis, machine learning, computer graphics, and various other fields.
Cluster analysis is a task that is conducted using several other algorithms that are different from each other in many ways and thus creating a cluster.
The following are some of the characteristics of cluster analysis:
1. Cluster Analysis is highly scalable.
2. It can deal with a different set of attributes
3. It shows high dimensionality.
4. Interpretability.
5. It is useful in many fields including machine learning and information gathering.

Name some popular cloud storage services.

Cloud storage is an essential component of data architecture. The following are some of the most popular cloud storage services out there:
a. Google Drive
Google Drive is arguably one of the most popular free cloud storage platforms that offer up to 15GB of free storage.
b. Microsoft Azure
Microsoft Azure is another cloud-based service that offers products like Azure Stack HCI, Azure Functions, Azure SQL Database, and Azure virtual desktop.
c. Amazon AWS
Amazon web services or AWS is a cloud storage subsidiary of Amazon that provides a wide range of web services like Amazon EC2, Amazon RDS, Amazon S3, Amazon Glacier, and many more.
d. Dropbox
Dropbox is an American cloud-based platform that offers client software, cloud storage, personal cloud, and file synchronization.

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