Understanding Cloudwick’s Data Lake

To effectively run business, an organization requires having some insights to gather more information and be able to use it more. This is where a data lake comes in to help. A data lake can store a large amount of data both structured and unstructured format until the company is ready to use the data. This characteristic of data lake allows companies to accumulate almost any use case. It is challenging for an organization to gain insights swiftly from its data because of the velocity, variety, and volume of data increases. To restructure the process, lower costs of gathering analyzing content, and scale the capacity; companies need to update their information warehouse solutions.

Cloudwick is a sophisticated consulting partner for AWS (Amazon Web Services). The company’s consulting experts can take one through the process, hastening one’s time to systematic insight by safely and swiftly architecting a current information store on AWS. In addition to this, the team of professionals can lead the exploitation of data foundation. Moreover, they can control their considerable data skills to incorporate a wide range of AWS resources including tool vendors and third-party ISVs.

Cloudwick has collaborated with AWS to bring into the market a discounted as well as a time-based offering to restart one’s journey to a modern data store. The jumpstart provides starts which includes an onsite kickoff. Additionally, the jumpstart uses a session of case requirements collection and encompasses current end-to-end information warehouse implementation. Jumpstart allows users to deploy, pilot, and move into production a complete-functional data lake operating on AWS. The Amazon SageMaker platform with the Machine Learning of Cloudwick that are on AWS allows business users and developers of all skillsets control the Amazon SageMaker’s power to discover the real world use cases as well as understanding how the whole machine learning takes place.

Cloudwick’s data lake applies to any sector vertical and mostly uses an ETL strategy as compared to other common ETL used in traditional information warehouse. Traditional data warehouses usually were defined before loading data. However, with the data lake, one does not have to think through all the use case that will be used. All one requires is a data catalog.