The fast pace of the digital landscape in the 21st century has often raised questions about the future of the data warehouse. Many pundits and data experts have speculated that the data warehouse will become obsolete as companies shift to faster and more accessible alternatives. However, the extensive functionality of the modern enterprise data warehouse has superseded the technological changes as companies increasingly modernize their legacy data warehouse architectures.
The evolving data warehouse has incorporated great flexibility, agility and scalability for enterprises looking to extract value from big data. Information delivery systems in a data warehouse can now update data in real-time and have built-in connectors for seamless integration with the most popular BI platforms. In addition, modern data warehouses come with cloud compatibility, rendering large server rooms to store data redundant and excessive. Despite this, the transient nature of technology requires data warehouse engineers to constantly maintain their systems and adapt to the latest industry data trends.
This blog post will look at how companies can modernize their current data warehouse architectures to add functionality and improve the organization’s overall efficiency.
Legacy Data Warehouse
A legacy data warehouse is based on physical servers and contains single rows of data accumulated from various sources. Legacy data warehouses have unstructured data sources coming from different types of databases such as relational databases, network databases, spreadsheets, multimedia files and other file systems that might be relevant to the organization. A legacy data consists of traditional data stacks and is typically divided into three tiers:
- Bottom Tier: The bottom tier of the legacy data warehouse consists of different connectors and integrators required to source data from various channels. It transfers data from transactional databases and stores them into the data warehouse.
- Middle Tier: The middle tier consolidates data and structures it such that it can be used to perform queries and analysis. It consists of an OLAP server that maps different tables and relationships such that the data from various sources can be analyzed together.
- Top Tier: The top tier of legacy data warehouses is used together with the client tools. These provide access to the data warehouse to different BI tools, data analytics platforms and data mining software.
Due to the infrastructure of legacy data warehouses, they are often inaccessible and might be subject to data silos. In addition, it is often a challenge to integrate them with state-of-the-art BI platforms and other querying tools. Legacy data warehouses also need constant maintenance by a skilled in-house IT team and come with scalability and performance issues relative to modern cloud-based data warehouses.
The Modern Data Warehouse
Enterprises must modernize their existing legacy data infrastructures to leverage faster insights and implement a more streamlined analytics process. This involves restructuring data pipelines and making them compatible with modern data warehouse architectures.
The modern data warehouse is located on the cloud; it allows users and administrators to access data from any remote location in the world. According to a survey by Denodo Cloud, 56% of all data warehouses are now located in the cloud. This makes legacy data warehouses obsolete and unsuited for the changing needs of enterprises, as more and more companies adopt the remote working model.
In addition, cloud platforms are cheaper and come with maintenance services, so clients do not have to invest time and effort in keeping the data warehouse functional. Lastly, cloud platforms are scalable as per the user’s changing needs. This implies that users can change their storage plan to accommodate more data as their business grows.
Furthermore, modern data warehouse architectures are designed for seamless information delivery in querying tools and BI platforms. They can be easily connected with various analytics software to perform complex operations on data and visualize significant results from historically accumulated data. Therefore, a modern data warehouse liberates managers and business leaders of many challenges that prevail while using a legacy data warehouse system.
The modern data warehouse is a cheaper, faster, and more efficient alternate to a legacy data warehouse when considered holistically. It offers more functionality and better performance to shift focus from maintaining and collecting data to analyzing it and deriving actionable insights from it. Many enterprises have now realized this and modernized their data infrastructures.
Conclusion
Modern data warehouses are the need of the hour for most companies; the growing reliance on data worldwide to guide business decisions ranging from talent acquisition to supply chain management has made a modern data infrastructure vital for growth. Companies looking to expand and make strategic decisions need to consider how legacy data warehouses are inefficient and difficult to manage.
Many new business projects worldwide start with robust ETL pipelines that are designed for integration with modern data warehouses. These companies understand the need for data-based decisions and work with the most popular BI tools to leverage data. Such practices and considerations make these businesses more likely to succeed than their competitors that rely on intuitive decision-making.
