Gartner estimates that the worldwide end-user spending on cloud services will exceed USD 723.4 billion in 2025. When it comes to technology prowess, businesses in all sectors are rapidly moving their digital assets to the cloud for better agility, scalability, and resilience in addition to cost-effectiveness. Cloud can be the de-facto expansive infrastructure needed for emerging innovations like Generative AI. However, embarking upon new digital journeys does require enterprises to not just have a solid cloud infrastructure but also a highly responsive and flexible data infrastructure that supports innovations.
Data is the soul of every modern digital experience that an organization builds for all stakeholders. However, the scale and diversity of data that today’s businesses need to handle is exponentially high. For complex use cases like artificial intelligence applications, the data streams handled would be both structured and unstructured. From simple text to audio or video streams, leveraging data for cloud transformation is quite challenging with traditional data infrastructure. This is where the growth of data lakes and data warehouses plays a crucial role.
What are Data Lakes?
A data lake is a centralized enterprise data repository that stores raw data in its native format i.e. the format in which it was generated at any digital workflow within the business. It acts as a single source of truth for all data within an organization, regardless of its structure or origin. For an organization, the data lake is the no-nonsense dumping ground for all kinds of data without worrying about outrunning the storage. Data lakes serve as a base from which new data pipelines can be built and through which raw data can be sent to different processing or analytical systems for deriving insights.
What are Data Warehouses?
The concept of data warehouses is slightly different from data lakes. It is also a storage repository for enterprise data, but the key exception is that it houses only structured data. The data generated in an organization is processed and classified into different categories based on the intended use in analytics before being stored in a data warehouse. This key difference makes the data warehouse the core data supply channel for any analytical processing. The data is clean and standardized for ready consumption by business intelligence tools thereby facilitating faster decision-making from analytical insights.
Powering Cloud Transformations with Data Lakes and Data Warehouses
As organizations attempt to drive competitive advantage and unparalleled business growth on the cloud with data-driven innovations, it becomes extremely important to achieve a synergy between data lakes and data warehouses that are used in their operational landscape. Together these data stores can facilitate a comprehensive end-to-end data management approach that helps enterprises leverage the maximum value from their cloud investments.
Let us explore three ways in which data lakes and data warehouses can power cloud transformations:
Faster cloud innovations
Data lakes offer flexibility for organizations to play with their data, build new data models that can serve as the foundation for AI initiatives, create new data-driven workflows, and much more. On the other hand, a data warehouse can help organizations make decisions faster from their data as it has already been cleaned and standardized for consumption by business intelligence frameworks. In other words, they guarantee the availability of the right data at the right time for the right applications in a cloud environment.
Seamless data integration
For cloud-native applications to work successfully it is important for them to seamlessly exchange data and insights regularly. Data lakes and warehouses can facilitate this by automating core data operations, democratizing data acquisition, centralizing storage, prioritizing data pipelines for easy ingestion into other cloud systems, and much more. They can help translate data into meaningful opportunities irrespective of the complex integration hierarchies involved in business workflows.
Flexible data foundation
As explained earlier, data lakes and data warehouses offer a foundation of data on top of which enterprises can develop their own digital experiences. As for the cloud, data lakes and data warehouses can play the role of a flexible backend infrastructure that supports easy scale and adaptability for new services. Data ingestion, processing, storage, and exchange can be seamlessly orchestrated using cloud data lakes and data warehouses as the driving force behind the scenes.
The Next Dimension – Data Mesh
The role of data lakes and warehouses in enabling a better cloud ecosystem for businesses is critical. However, we are well past the initial days of resolving complex data problems with just these two solutions. As years passed and the complications within data management increased, there is a huge demand for bringing a productized approach to data management wherein data is treated as a product within different teams and each team is responsible for managing the intricacies and infrastructure of the data product they need. Ownership and responsibility of data products will be distributed across teams in the business depending on growth objectives. This architecture of using a decentralized data architecture which categorizes data as products based on the domain it will be used is called Data Mesh.
The domain oriented and self-service approach of data mesh ensures that the benefits of data lakes and warehouses can be enjoyed while ensuring better performance for teams handling different data analysis operations.
Building Resilience with Data
Building remarkable data experiences that can easily mingle with data lakes and warehouses and accommodate new dimensions such as data mesh will be pivotal for today’s businesses to thrive in the digital age. However, gaining such competence in complex data operations with an in-house team may not be the best possible idea. Instead, what businesses need is a technology partner like Wissen to fully realize their data ecosystem's potential. Get in touch with us to know more.
