Modern companies generate large volumes of data from their web-based and device based digital applications, along with core operational systems. This expanded volume and variety of data, however, does not necessarily provide immediate value. Instead, a business must derive value by bringing disparate sources of data together and process them into meaningful insights for better understanding of its market, customers, and operations.
To accomplish this, businesses are increasingly adopting data analytic solutions such as Modern Data Platforms (MDP). These cloud-based platforms integrate variety of data production components into an enterprise-wide analytics system.
Modern Data Platforms are cloud based equipped with advanced tool kit, a key to success in this data-driven world. Legacy databases require in-house servers that cannot flexibly scale to match the pace of growing data needs. In contrast, the cloud offers businesses near-infinite scal¬ability and storage, allowing economies of scale unachievable with an on-premises configuration.
Traditionally Business Intelligence (BI) technologies were all housed within on-premises data centers, where the operational data sources were fed into local data warehouses and reported on by local users.
The modern enterprise has grown beyond the geographic limitations of this on-premises and local model. An organization might depend on hundreds of internal and external sources supplied by third-party providers. Integrating this volume and variety of data into a traditional data center is an uphill task. It is both time consuming and, in some cases, a compromise.
The performance within an on-premises data center is physically limited to the hardware procured and configured. Any change in increase in capacity to be dealt with lead times and hardware limitations. Under this approach, business throughput suffers until capacity is expanded, causing internal backlogs, preventing customers from using your services, and creating missed deliverables. And, even if companies recognize the need to load data in-house from external sources, network capacity often creates bottlenecks, preventing the movement of modern data volumes into a local data center. Last, the wide varieties of different data sources used today typically require migration through expensive custom coded solutions. Often, leaving behind these data sources is not an option as they represent a critical part of the picture regarding your enterprise’s overall activities.
In place of on-premises data stores, cloud-based data stores remove the physical limitations and maintenance overhead with fully managed solutions possessing petabyte scale storage capabilities. Today the toolkit available on cloud is very advanced reducing the build time to great extant. The key capabilities are:
However, the challenge of building a data and analytics solution on cloud persists.
Modern Data Platform reference architecture on AWS
All of the challenges listed above are solvable with cloud-based Modern Data Platform (MDP). It can not only streamline your data workflows but also empower your business to effectively manage its complete end-to-end data analytics journey. The biggest investment will most likely be its cloud data warehouse. Focusing on getting the most value out of that cloud data warehouse should be paramount. Many of the industry-leading cloud data warehouses, such as Amazon Redshift, Google BigQuery, Azure Synapse and Snowflake, have substantial partner ecosystems that provide compatible third-party solutions that work with your existing cloud infrastructure.
In addition to the key component cloud data warehouse, one should adopt the right tool stack and solution approach for your business demands. Instead of one large solution it’s important to build multiple smaller solutions on the base refined data without creating multiple copies making it affordable, faster delivery and easy to manage. The notion of MDP can help bring together your solution including technologies for Analytics, Business Intelligence, as well as a Cloud Data Warehouse (CDW). Hosting these technologies together will create a MDP that not only meets your current requirements but provides an extensible solution that can be modified and expanded to meet ever-changing and complex use cases. This framework allows you to bring in Data Lakes, Data Vaults, Machine Learning, and Artificial Intelligence to further bolster your data analytics capabilities. Agility is key as such modern platforms are developed incrementally over time.