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?
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
- Complete control of the tech stack
- Strict governance and regulatory compliance
- Steady connectivity and no latency issues
- High availability
- Moving Toward Modernization – Hosting the Data Warehouse on Cloud
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.
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.
- Hybrid Data Warehouse – Leveraging the Best of Both
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
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?
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