x

    Understanding Data Warehouse As A Service And Its Benefits

    • LinkedIn
    • Twitter
    • Copy
    • |
    • Shares 0
    • Reads 1309
    Author
    • Ali KidwaiContent Architect
      The goal is to turn data into information, and information into insights.
    Updated 01-January-2025
    Featured
    • Data Warehouse
    • Data Management
    • Telecom

    Editor’s Note: Are you looking to implement the data warehouse in your organization but are being held back due its large investment requirements? Then Data Warehouse as a Service (DWaaS) is what you are looking for. We will look at its data management, break down its architecture, key benefits, and the steps that one should follow migrate into one.

    The Need for Data Warehousing in a Competitive Business Landscape

    As you grow, so does your data soruce- CRM, ERP, POS, SCM, and that to various verticals and making it difficult to make sense of it. Sounds familiar? This is the story of many organizations that are struggling with data silos where unrefined data unsuitable for analysis is scattered across departments, systems, and applications. This will not only hamper your decision accuracy but also makes it a challenge to foster collaborations between the teams.

    31% of organizations want to be able to quickly increase their analytics investments to handle more users and more data.

    Here’s where data warehousing comes in. It acts as a one stop solution by gathering data into a centralized repository, transforming it to ensures that everything is neat and analysis ready.

    Sounds good. But there’s a catch…

    Building and maintaining these on-premises data warehouse can be very costly affair and will require significant commitment for investing in infrastructure, expertise, and ongoing management.

    Then what’s the solution?

    Answer- Data Warehousing as a Service (DWaaS).

    Think of DWaaS as data warehouse but in clouds. You still enjoy all the perks of data warehouse but don’t have to make any upfront investments on physical infrastructure. And that too without any compromise in its capabilities- automated scaling, high-speed querying, and reduced maintenance overhead- helping you focus on strategy rather than infrastructure.

    What Is Data Warehouse as A Service (DWaaS)?

    The 'As-a-Service' model focuses on providing various services to clients through the Internet. Salesforce is a famous example of such a service. Along with these services, clients are provided with the benefits of low cost and scalability. This model includes SaaS, PaaS, and IaaS and has become a means to enable technologies through digitalization, analytics, and automation.

    A data warehouse is a repository that stores data, which can use accessed for analysis if and when needed for better decision-making. The increasing demand for accessing real-time data that's high on volume, velocity, and veracity is one of the primary reasons why DWaaS has been emerging rapidly.

    DWaaS brings in multitude of advantages for your data management proficiency-

    1. Data Accessibility to Business Users

    The visibility into data is achieved by resource pooling to share storage space, networks, and computing power in cloud data warehouses. It results in providing data marts for new users without limiting the storage or processing power. Also, ad-hoc queries can be performed on the same dataset by stakeholders from different locations.

    2. Enhanced Performance and Speed

    The ever-increasing data from numerous resources demands an effective data management solution. This has given rise to DWaaS that offers cloud data warehouse service to improve the overall performance, efficiency, and speed of data analysis and processing within an organization. Typically, DWs present on the cloud emphasises various servers that accelerate the processing speed. Furthermore, it can seamlessly integrate with new data sources.

    3. High Data Storage Capacity

    Earlier, IT teams were purchasing huge storages that cost them a fortune. Simultaneously, the cloud data warehouse allows them to decide the storage limit according to their present needs. However, organizations can easily scale up anytime with an additional cost which is quite economical. What's more? It offers agility and flexibility that allows firms to quickly deploy a change without affecting the organization's architecture or budget.

    4. Quick Disaster Recovery

    The conventional recovery methods are way too expensive and defenseless, whereas the cloud data warehouse gives sheer backup security. The good thing is it doesn't even need any additional hardware as it supports data duplication naturally. It instantly snapshots the processes and saves them automatically. With a data warehouse in place, it holds the data in different nodes to make the duplicate data seamlessly accessible. DWaaS service providers provide specialized networks to improve backup security further.

    5. Shared Cost of Ownership

    Unlike the mighty cost involved in the on-premise DWs, cloud data warehouses are economical, which is the main reason for their popularity. It does not require maintenance, expensive hardware, or continuous updates as everything is taken care of by the service provider. The management can purchase just the necessary amount of storage and compute power, which can be extended as required. Besides, DWaaS does not demand networking or server rooms.

    Let’s look at the architecture through which they integrate into your system.

    Underlying architecture behind DWaaS

    Applications of Pharmaceutical data analytics
    Source: The basic components of data warehouse and DWaaS are the similar

    1. Data Source

    These are the internal and external sources from where data can originate-

    • Operational Databases: The daily operations repository, like CRM or ERP systems.
    • Flat Files: Simple files like text documents, CSVs, or Excel sheets holding structured or semi-structured data.
    • External APIs: Third-party services, such as social media feeds or market data, that provide additional insights.
    • Data Lakes: Massive reservoirs that store raw data in its original format, ready for deeper analysis later.

    2. Staging Area

    Do we directly store the data that is collected? Answer- no. The staging area acts as a middleman between data storage and collection layer. After the data is collected, it is cleaned and prepared before it is finally moved into the main storage. For this purpose, staging area uses the process of ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform).

    Key Functions-

    • Data Cleansing: Fixing errors and inconsistencies
    • Data Transformation: Structuring data in an analysis ready format.
    • Integration: Creating a consistent dataset integrating data from multiple sources.

    Under the intricate details about the data integration and how it can be used to achieve data synergy- Read more!

    3. Storage Layer

    After the data has been cleaned and transformed, it is stored in the storage layer—a structured environment within the data warehouse. This is where the heavy analytical work happens by acting as the actual database for the deep-dive queries and reports.

    Key Components-

    • Data Warehouse Database: The central hub, where historical and current data are organized using schemas like star or snowflake designs.
    • Data Marts: Tailored mini warehouses created for specific business units or departments to serve their unique needs.

    4. Presentation Layer

    The presentation layer is the user-friendly front door to the data warehouse. This is the gateway through analysts, managers, and decision-makers interact with the data, performing analyses and generating reports through intuitive tools.

    Key Components-

    • Business Intelligence (BI) Tools: These are apps like Tableau or Power BI that helps visualize and analyze data in human understandable.
    • Reporting Tools: Systems built to churn out standardized reports based on predefined metrics and KPIs, ensuring everyone stays on the same page.

    Moving to Cloud-Based Data Warehouse

    Now that we know the different benefits of DWaaS, the first critical point in your migration journey is to opt for the discovery phase. This is a critical preliminary step in any software development project and DWaaS is not different. Undertaking this workshop helps stakeholders clarify their vision and which ensures that all their business requirements are accurately captured, leading to a more effective and efficient development process.

    Below is the step-by-step approach to our discovery workshop:

    1. Business Assessment

    In this phase, we ensure to engage with business stakeholders to understand their current data landscape, challenges, and more. This step involves identifying roadblocks, use cases, needed capabilities, etc.

    Read more about cloud warehouse

    2. Recommendations

    This is the review phase we'll evaluate existing data and analytics practices. With your organizational processes and infrastructure related to data storage, integration, and usage, you can expect smoother source-to-target mapping.

    3. As-Is Analysis

    This is where we’ll do roadmap development, we’ll create a step-by-step plan which aligns with your organizational goals, resources, and timelines. It is essential to define KPI, calculation methods, visualization techniques, and develop a logical architecture for migration.

    4. Outcome

    Our discovery Workshop ensures alignment across stakeholders, addressing migration challenges with a structured, business-first approach. It delivers a clear action plan and tailored solutions for transitioning to a cloud-based data warehouse, enabling real-time insights, enhanced data accessibility, and scalable analytics capabilities.

    Wrapping Up

    As the focus shifted to data-driven decision-making, the DWaaS market significantly rise. One estimate projected the DWaaS market is expected to reach over USD 41 billion by 2033.

    This growth can be attributed to innovation in the cloud technology. For example, with the ability to use analytics tools right at the cloud without having to deal with the management of on-premises infrastructure, makes it quite lucrative for switching. Further, as with artificial intelligence (AI) and machine learning (ML) being integrated into DWaaS platforms, it is expected to further enhance data processing capabilities, saving addition time. Our expertise suggest that the trend grow towards multi-cloud and hybrid deployments, which will offer greater flexibility but also give the necessary control over their data and resources.

    To learn more about DWaaS, you may get in touch with our cloud data warehouse experts.



    About Author

    Data Warehouse Solutions
    Ali Kidwai

    Content Architect

    The goal is to turn data into information, and information into insights.

    Generally Talks About

    • Data Warehouse
    • Data Management
    • Telecom

    Related Blog