A data warehouse architect is responsible for designing and maintaining data management solutions that support a business or organisation. They analyse a company’s data needs, develop database management solutions, and deploy data management software for storing and retrieving data from the cloud or machine. 

Overview

Data warehouse architects work with conventional data warehouse technologies to devise plans that best support an organisation or business. They consider client needs or employer goals and work to develop a specific architecture or plan that will be maintained to serve the specifically stated purposes.

To become a data warehouse architect, one needs a bachelor’s degree in computer science, information technology (IT), or computer engineering and several years of experience working with data management or software architecture. Optional qualifications such as the Certified Data Management Professional (CDMP) certification can also be earned. 

According to Salaryexpert, experienced data warehouse architects may earn a yearly average of INR 22 lakhs per annum, and beginners earn Rs. 15 lakhs per annum. However, salaries may vary depending on location and experience.

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What are the skills required to become a data warehouse architect?

In addition to work experience and subject-specific knowledge, aspiring data warehouse architects should possess the following hard and soft skills:

Hard Skills:

• Data Warehousing
• Extract, Transform, Load (ETL)
• Data Modeling
• Informatica
• Requirements Analysis
• Database Design
• Business Intelligence (BI)
• SQL Server Integration Services (SSIS)
• Hadoop
• Databases
• Oracle Database

Soft Skills:

• Leadership skills
• Organisational skills
• Detail-oriented

The above image represents all the necessary skills required to become a data warehouse architect.

Data architects need to know a variety of programming languages and advanced technologies associated with big data, AI, IoT and the cloud. They must also possess strong written and verbal communication skills and display sharp business insights. The data warehouse architecture in data mining should enable effective techniques to drive business success.

Types of Data Warehouse Architecture

Data warehouse architecture comes in three different variants:

• Single-Tier Architecture: This architecture decreases the quantity of data kept in a data warehouse by producing a smaller data set. It contributes to improving data quality and reducing data duplication. The management structure should be straightforward, and the hardware and software platforms should be adaptable.
• Two-Tier Architecture: The database and application layers are separate tiers in this configuration. The first tier is the client layer, which comprises report writers and query tools for end users. The second tier is the database server layer, which houses all raw data.
• Three-Tier Architecture: This is a data warehouse’s most widely used architecture. It consists of three tiers – top, middle, and bottom. The database servers reside in the bottom tier, primarily a relational database system. Data is cleaned, transformed, and incorporated into this layer utilising back-end tools.

There are five components to a data warehouse architecture:

• Database
• ETL Tools (Extract Transform Load)
• Meta Data
• Query Tools
• DataMarts

(A data mart is a smaller version of a data warehouse that serves specific business units or teams such as finance or marketing)

Principles of Data Warehousing

These are a few guidelines to remember while creating a data warehouse.

• The first principle is that data quality is crucial. The data in a data warehouse must be trusted by the users who rely on it for decision-making. 
• The second idea is to create disconnected systems. This means that operational systems should be in two-way contact with the data warehouse to feed data in and out. 
• The third concept is to set up an immutable staging area where data may be kept before being modified and put into the data warehouse.

Additional concepts include employing schemes to logically group things, using consistent and meaningful names for objects in a warehouse, consolidating data sources, cleaning up poor data, and making it simple and consistent. A well-organised and dependable data warehouse may be built by adhering to these principles.

How to keep a data warehouse organised?

Keeping a data warehouse organised requires following best practices that ensure the warehouse meets the needs of stakeholders, is scalable, and can be easily maintained.

These are some best practices for organising a data warehouse:

• You should have a data model and a data flow diagram.
• Including data governance.
• Create user roles.
• Recognise data warehouse schema design
• Begin by implementing robust master data management (MDM) processes.
• Make time and effort to standardise your data.

You should have come across a few terms by now. Let’s get a clear idea about them.

Data models: are abstractions of real-world things, systems, or processes used to organise, describe, and analyse data. They are designed to assist users in understanding data linkages and structure and give a framework for designing information systems.

Data models can be conceptual, logical, or physical and can be used for various reasons, including data analysis, database design, software development, and business modelling.

Entities, characteristics, and relationships represent data pieces in a data model. 

• Entities represent real-world things or concepts, whereas attributes explain those entities’ qualities. 
• Relationships describe the connections between things and how they interact with one another.

ETL (Extract, Transform, Load): ETL is the task of extracting data from several sources, processing it into an analysis-ready format, and putting it into a data warehouse. As a data warehouse architect, you should understand ETL processes and technologies thoroughly and be able to design, create, and manage ETL workflows.

Database Design: A data warehouse architect must be familiar with database design ideas and techniques such as normalisation, indexing, and partitioning. This ability is required for creating and maintaining efficient and scalable databases capable of handling massive amounts of data.

Programming: Programming skills are important for data warehouse architects, as they often need to develop custom scripts, queries, and applications to automate ETL processes, optimise database performance, and build data models. Proficiency in SQL, Python, and Java is highly desirable.

Business Intelligence: Business intelligence (BI) is the process of using data analysis tools and techniques to extract insights and value from data. A data warehouse architect must understand BI concepts and tools well and be able to design and implement data structures that support BI reporting and analytics.

Communication: Data warehouse architects must be able to communicate effectively since they must collaborate with stakeholders from many departments and backgrounds. They must be able to successfully work with team members and external partners and convey technical concepts and solutions to non-technical stakeholders.

Data mining: It is the process of analysing data to discover patterns, correlations, and insights that may be utilised to guide business choices. The data warehouse architecture in data mining should include tools such as statistical analysis software, machine learning algorithms, and visualisation tools.

Salary of a Data Warehouse Architect in India

A data warehouse architect‘s income in India might vary based on criteria such as experience, skill set, geography, and organisation size. Yet, according to Payscale India statistics, the average annual compensation for a data warehouse architect in India is almost INR 1,530,000.

Due to the strong demand for qualified specialists in the field of data warehousing and business intelligence, data warehouse architects may anticipate better wages in major Indian cities such as Bangalore, Mumbai, Delhi, and Hyderabad. Professionals with particular abilities in areas such as ETL tools, data modelling, and database architecture may also command greater pay.

Properties of Data Warehouse Architecture

Data warehouse architecture is a useful tool for storing and analysing data since it has several features, including:

• Separation of operational and analytical systems
• Scalability to handle large amounts of data
• Extensibility to add new data sources and functionality
• Security to protect sensitive data
• Administerability for efficient management and maintenance

Job roles of a Data Warehouse Architect in India

Here are some job roles of a data warehouse architect in India:

• Designing data warehouse solutions and developing specific architecture or plans that support a business or organisation
• Maintaining systems for keeping raw data, metadata, and other data assets 
• Performing tasks within a greater infrastructure that supports activities like data mining to add value 
• Analysing all data warehouse architecture goals and identifying skill requirements for the same 
• Developing both physical and logical data models
• Preparing designs for database systems and recommending improvements for performance 
• Maintaining and developing various database scripts and tools to facilitate the automation process 
• Developing various ETL processes and preparing OLAP cubes 
• Providing support to all data warehouse initiatives 
• Analysing all data warehouse activities and identifying key resources for the same 
• Defining meta-data standards for the data warehouse, monitoring processes, and developing plans to capture and access all metadata. Developing and implementing various work plans for projects.

A Career in Data Warehouse in India

As rapid data influx is nowhere near seeing a decline, many opportunities for a career in data warehousing in India are awaiting skilled professionals in the coming years. Job portals such as Naukri.com and LinkedIn have thousands of job openings for data warehousing professionals already, while global opportunities are further anticipating the growth of skilled personnel to acquire valued talent from the nation. 

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Conclusion

To sum up, data warehousing enables the time taken to deliver a certain level of quality to be significantly reduced while adapting to dynamic user demands. A well-designed data warehouse architecture can help organisations better manage the vast amounts of data generated today. The best way to be a part of this revolution is to upskill, and upGrad extends you an opportunity to do the same!

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