Cloudwick, a big-data solutions company headquartered in Newark, California, is working with Amazon to provide Amazon’s clients with the best possible database, data lake and other data storage services. More specifically, Cloudwick is working with Amazon Web Services (AWS), a dynamic and rapidly growing division with Amazon.
Defining a data lake
A data lake is a type of central repository where an organization can store information that is both structured and unstructured. Once data is a data lake, the organization can use it in multiple ways in order to make the best possible decisions in extremely rapidly. Traditional ways of analyzing a data lake include using dashboards or visualizations, and an evolving way is to use machine learning to rapidly evaluate and draw conclusions from the massive amounts of data in the lake.
There are important differences between a traditional data warehouse and a data lake. A data warehouse accepts data that has been structured beforehand and then is later queried. This worked fine for organizations in the past because the data was gathered through fewer types of platforms than today where data comes in through mobile apps, social media and other sources. Essentially, a data lake does the sorting and structuring for the user from the raw, unstructured data it collects.
Cloudwick and AWS
AWS clients can migrate a data lake to the cloud through Cloudwick technology and assistance. This partnership allows companies including Netflix and NASDAQ to use data lakes efficiently and inexpensively.
Organizations can analyze the data in the lake through the analytics of their choice, including open source tools and others. Furthermore, machine learning can build models, make projections and draw insights from historical data all without having to reformat data. Essentially, Cloudwick and Amazon are working together to streamline and speed up the process of data storage and analysis to where organizations gain insights in real time.