Understanding Data Warehouse Deployment: The 3 Models, Common Hurdles, Benefits & Best Practices

The modern business world runs on Big Data, and how efficiently an enterprise can manage, process, and analyze this large volume of current and historical data determines its success in the industry. Extracting valuable business insights from this gold mine of information not only fuels customer-centric services but also helps in achieving operational excellence – imperative for sustaining the stiff competition that businesses face today. Additionally, the escalating need for real-time data amplifies the data challenge. This underscores the growing focus on creating a unified data environment that supports business intelligence (BI), analytics, and AI (artificial intelligence) – all of which is made possible through building a robust data warehouse, serving as a central repository for data for analysis, prediction, and data-driven decision-making. And, following an appropriate data warehouse deployment model, which is aligned to the business requirements and goals, is the key here to achieve the desired results. 

What is a Data Warehouse?

A data warehouse is an advanced data management system designed to enable businesses to consolidate and centralize large volumes of data from multiple source systems, relational databases, processes, and systems. Its primary purpose is to facilitate business intelligence and analytics. A typical data warehouse optimizes raw data taken from various sources such as supply chain processes, CRM systems, and ERPs for analytics using various ETL (Extract, Transform, and Load) tools.
With all the data stored in one place, a data warehouse processes the data using the online analytical processing (OLAP) approach specifically designed for handling complex queries, making it efficient for analytical tasks.
Over time, as this assimilated data matures in the data warehouse, it becomes an invaluable historical record of information such as customer data or business process metrics. This data can be analyzed by business analysts, data engineers, and data scientists using the data warehouse’s built-in analytics tools or using various BI, predictive analytics, and machine learning platforms to identify key trends, patterns, and customer behaviors.
The global data warehousing market is projected to reach the $51.18 billion mark by 2028 (Allied Market Research)

3 Data Warehouse Deployment Models: Pros & Cons

With the basic requirements and benefits of building a robust data warehouse discussed, let’s now understand the different options that enterprises have in terms of deploying a data warehouse and how each deployment model has its pros and cons.
  • Building a Data Warehouse On-premises – The Traditional Approach
Here the enterprises deploy their data warehouse on their own on-premises infrastructure. An on-prem data warehouse can be configured to run on commodity hardware, in which it can be set up using MPP (Massively Parallel Processing) architecture for horizontal scalability or SMP (Symmetric Multi-processing) architecture where multiple processors share a common Operating System and memory. Alternatively, this traditional type of data warehouse can be set up through a standalone, purpose-built appliance format that does not impact other servers and runs its own backup-related workloads.
Pros: Organizations in sensitive or highly regulated industries often take this traditional route for data warehouse implementation as it offers –
  • Complete control of the tech stack
  • Strict governance and regulatory compliance
  • Steady connectivity and no latency issues
  • High availability
Cons: The cost of deploying a data warehouse on-premises can be quite high and it also requires continuous support and maintenance. More importantly, this approach demands meticulous planning, including the planning of the server requirements for future loads.
  • Moving Toward Modernization – Hosting the Data Warehouse on Cloud
Gartner expects 50% of new system deployments in the cloud to be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions by 2024.

With digital transformation gaining momentum and cloud technology becoming more mainstream, organizations are also increasingly modernizing their data warehouse management system by taking it to the cloud. Cloud-based data warehouses are hosted in the cloud environment by cloud providers, often also as a fully managed SaaS (Software as a Service) offering.

Leaders in the Cloud Data Warehousing Space
Amazon Redshift, Microsoft Azure Synapse Analytics, Snowflake, Oracle Autonomous Data Warehouse, and Google BigQuery are some of the notable players in the DWaaS (Data Warehouse as a Service) market which is growing exponentially and is expected to reach USD 11.30 billion by 2028 (Data Bridge Market Research). These market leaders are constantly upgrading their offerings and unlocking new levels of innovation every day.
Pros: Cloud data warehouses allow easy access to data via the Internet and are low-maintenance, highly scalable, and cost-effective, emerging as a popular option for those organizations that want to save on the significant initial set-up cost of an on-premises data warehouse.
The end-to-end data warehousing platforms available in the market today fulfill the increasing demand for self-service data warehousing, as companies look to empower all their business users with the ability to extract insights from their data.
Additionally, these also meet the growing need for real-time data processing and increased use of AI technologies to automate data processes.
53% of organizations have an on-premises data warehouse, and 36% have such a solution in the cloud. (TDWI)
Cons: Bandwidth limitations can impact data transfer processes and accessibility in a cloud data warehouse. It also needs continuous monitoring and careful control to avoid unexpected expenses. Another challenge is finding experienced and highly skilled resources, since cloud-based solutions are relatively new, although rapidly advancing. Besides, many organizations still have their reservations about dependency on a single vendor and not owning their data platform.
  • Hybrid Data Warehouse – Leveraging the Best of Both
In the hybrid approach, enterprises maintain an on-premises data warehouse for certain data and analytics needs while using cloud-based data warehousing in parallel for other projects.
Pros: The hybrid approach allows a lot of flexibility in terms of how an enterprise can leverage the cloud’s virtually unlimited resources to handle peak workloads while still maintaining on-premises infrastructure for regular operations. For organizations seeking a gradual shift to the cloud, the hybrid approach can become a great stepping stone. It provides a good middle ground to organizations wanting to not depend on one vendor.

Cons: The fragmented model may pose some data integration and synchronization-related challenges and data pipelines and ETL (Extract, Transform, Load) processes may become more complex owing to the two different environments. The inter-cloud connections and some cloud-to-on-premises bridges are still in the developing stage.

Common Hurdles that Organizations Face in Deploying a Data Warehouse

Implementing a data warehouse can be complex and organizations often encounter numerous technical glitches, particularly if they lack the right expertise and domain knowledge.
In the case of traditional data warehouses, there can be several issues. For instance, manually processing large volumes and a variety of data can lead to data inaccuracies. Besides, meeting the increasing analytics needs of the market and AI compatibility can also be a challenge in addition to other roadblocks around data silos, data performance, change management, and cost.
Additionally, the schema-on-write approach in a data warehouse calls for intensive maintenance effort whenever there is a change in the data structure. In cases where the data changes frequently, maintaining the data warehouse can be challenging.

While moving the data warehouse to the cloud alleviates many of the complexities of traditional data warehousing, it is also important to know when a data warehouse may not be the right fit:

  • Unstructured Data Use: The data warehouse’s structured, tabular format does not work with unstructured data effectively. Here, specialized tools and platforms like Databricks, specifically Databricks Delta Lake, that can handle both structured and unstructured data, become a more suitable option.
  • Real-time Data Ingestion and Analytics: Data warehouses are designed specifically for batch processing. For real-time data ingestion and analytics, an organization must consider specialized real-time streaming platforms, such as Confluent.
  • Schema-on-read: Where schema-on-read capabilities are required, a data warehouse that uses a schema-on-write approach may not be suitable. Here data lakes and some NoSQL databases become more relevant.
  • Data Exploration: While a data warehouse does support reporting and analytics, it may not be the best choice for data exploration. Instead, specialized tools and platforms designed for data exploration and discovery, such as data visualization tools, self-service BI tools, or data discovery platforms, might be more suitable. Alternatively, organizations using a data warehouse can leverage platforms like WSO2 to facilitate the movement of data from a data warehouse to other systems when the need for data exploration and analysis arises.
  • Data Science and AI Workloads: Traditional data warehouses may not be able to support the advanced analytical and machine learning capabilities required for data science and AI workloads. For data science and AI, you typically need specialized platforms and tools that support model training, deployment, and experimentation.

Moreover, the success of a data warehouse depends on a powerful data warehouse strategy. A positive data warehouse implementation is backed by a well-defined strategy that demarcates the objectives of deploying a data warehouse in alignment with the long-term business goals of an organization and establishes a clear roadmap for continued success.

Some Questions to Ask Before Setting Out on the Data Warehouse Journey

  • What are the long-term and short-term goals of the organization?
  • What is the purpose the data warehouse must serve? Is it for analytics, data mining, reporting, or operations?
  • What is the volume and variety of data that will be stored?
  • At what frequency does the data structure change?
  • What are the data sources to be integrated?
  • Does the data need to be leveraged for real-time insights or historical analytics?
  • What are the security and compliance requirements that must be met?
  • What is the budget available for the data warehouse project?
  • Are the resources and expertise necessary to build and maintain the data warehouse available?

Looking for Guidance on Streamlining Your Data Management Processes?

Our data management consulting and advisory services have accelerated the data transformation journeys of our clients across industries spanning banking and financial services, healthcare, and manufacturing.
Our strategic partnerships with leaders in the data warehouse solutions industry such as Microsoft, Snowflake, Databricks, WSO2, and Confluent give our clients a competitive advantage in building a sustainable data infrastructure.
Speak to our experts to discover all about data warehousing – data warehouse best practices, modern data warehouse architecture, migration strategy, data warehouse maturity assessment, latest industry trends, and more.

Priyanka Kochhar
Priyanka Kochhar is an accomplished Data Architect. She holds a master’s degree in Computer Science from the University of North Carolina with Big Data as her major. Priyanka specializes in designing and managing data solutions and building robust data platforms. She is a certified professional in Data Science & Business Analytics and AWS Cloud Practice. At KANINI, she handles data migration projects successfully involving multiple applications and databases.
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