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Cloud Data Warehouse vs. Cloud Data Lake: Overall Resource Efficiency and Productivity

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Since the COVID-19 crisis began, IT budgets have become tighter, driving technology leaders to figure out ways to do more with less. Data-driven enterprises cannot simply afford to sunset data modernization or data analytics projects. In times like this, several industry verticals (insurance, financial services, and healthcare) are generating more data than ever before, and this data has to be processed and analyzed with fewer resources.

Modern IT leaders have been using cloud data lakes and cloud data warehouses as the technologies of choice to store, manage, and analyze data. Cloud data warehouses, for a long time, have been the technology used to store highly structured data for specific BI use cases. Alternatively, cloud data lakes are a much newer technology that allows enterprises to work with much larger volumes and varieties of data in a more agile, natural manner.

Although data warehouses can handle semi-structured data, they are not the most optimal option to do so. At the speed and volume that data is generated today, it is not cost-effective to store all data in a database or a data warehouse. Additionally, there is a significant effort of data processing and preparation that takes place before storing it in a data warehouse, and this process is slow and expensive. Given their ability to store structured, semi-structured, and unstructured data, and their adaptability for future analytics needs, cloud data lakes have become the go-to answer to address today’s wide-ranging data challenges.

Everything we do today generates data, from the electronics we use and even wear to the cars we drive to the supply chains we manage. As a result, data is growing exponentially and is coming from more sources faster than ever. While deemed in the past as incredibly costly and inefficient, today cloud data lakes enable enterprises to establish a single source of truth by keeping all their data in a centralized repository, accessible by many teams for many purposes. This gives companies the ability to gather insights from their data that otherwise would be impossible to discover.

Doing more with less applies to the resources and budget available for data projects in your organization, as well as the time spent working with the data. Cloud data lakes outperform cloud data warehouses because they enable organizations to gain faster value from their data, and from more of their data. The open architecture of a cloud data lake allows for the deployment of resource-efficient, best-of-breed processing engines that help to accelerate exploration and insights while keeping costs down.

The highly structured nature of cloud data warehouses requires decisions to be made on what data to include or exclude from reports. In today’s environment, the disposal of unused data is more expensive than retaining it. The flexibility and open architecture of the cloud data lake allow enterprises to retain all of their data as it has the potential to provide valuable insights not yet discovered.

The monolithic architecture of cloud data warehouses is famous for locking users into proprietary data formats that are hard to migrate from and makes it difficult to leverage the best-of-breed technologies that open cloud data lakes offer. Also, the time and money involved in developmental processes to make changes in a data warehouse is not a good investment of available resources. Copying and moving data into a cloud data warehouse is a complex and slow process that degrades data freshness and results in lost business opportunities. In contrast, cloud data lakes enable enterprises to answer business questions with high agility at the user’s pace.

Cloud data lakes afford enterprises complete control over their data at all times with a much lower risk of vendor lock-in. Its open architecture allows users to leverage storage technologies such as Amazon S3 and Microsoft ADLS, and best-of-breed processing engines like Databricks. It also provides easier access to data, so data engineers, architects, and data consumers can make the most of their data using the tools that they already are familiar with.

Cloud data lakes offer enterprises the ability to pursue new and more complex data projects in order to take advantage of the cost savings, accelerated time to insight, efficiency, and productivity that they deliver.

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