Whenever you’re adopting Big Data solutions on AWS, typically one of the first things customers look to adopt is a data lake. A data lake is a centralized repository that allows the storage of a lot of different types of data, both structured and unstructured, at any scale. It really gives you the ability to keep a good catalog of what you have in the cloud.
Typically the first step is injecting data into an Amazon S3 bucket. A data lake at this stage usually comprises a couple Amazon S3 buckets. Once the data is ingested, the next step for customers is to adopt it using some sort of reporting tool like Amazon QuickSight or do deep data analytics and exploration with their data scientists or other data engineers using tools like Amazon SageMaker.
At Caylent, what we do is to help customers understand what their data is and how to put it into a format that allows them to consume and action on it. A lot of customers have data out there that has just been sitting idle. We go in and understand what that data looks like and then how we can transform it to make it usable for business insights.
This involves building reports and dashboards or helping build models. Our data engineers can work closely with our customer’s data scientists. We can clean the data, put it through ETL and make sure that the data is in a storage format that makes sense for data scientists to consume.
Further down the process, once data scientists have started developing and building models, we can help them build pipelines so that they have continuous delivery of those models as well.
Learn more about how Caylent’s Cloud Data Engineering practice can help you turn your data into business insights!
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