Data Analytics

Data Modernization & Analytics

Learn about some of the fundamental steps involved in actioning your data on AWS to produce insights and guide business decision making.

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! 

Data Modernization & Analytics
Jim Rosser

Jim Rosser

As a Principal Customer Solutions Architect (CSA), Jim partners with Caylent Account Executives to help set clients up for success in their AWS journey. When he's not working directly with clients, Jim is busy maturing our internal sales automation or mentoring peers on the sales and pre-sales team. Based out of Colorado, Jim can often be found enjoying the nature and greenery of the Rockies or enjoying a craft brew in downtown Colorado Springs.

View Jim's articles

Learn more about the services mentioned

Caylent Services

Data Modernization & Analytics

From implementing data lakes and migrating off commercial databases to optimizing data flows between systems, turn your data into insights with AWS cloud native data services.

Caylent Catalysts™

Serverless Data Lake

Rapidly implement a foundational low-code data lake with Caylent's data engineering experts who will also enable your teams for no-code exploratory data analysis.

Accelerate your cloud native journey

Leveraging our deep experience and patterns

Get in touch

Related Blog Posts

Best Practices for Migrating to Aurora MySQL

Aurora MySQL is a high-performance, fully managed database with Amazon RDS benefits, simplifying infrastructure for business focus. Learn migration best practices and essential components for a successful journey toward Aurora MySQL that can lead to increased scalability, resiliency, and cost-effectiveness.

Data Modernization & Analytics

re:Invent 2023 Data Session Summaries

Get up to speed on all the data focused 300 and 400 level sessions from re:Invent 2023!

Cloud Technology
Data Modernization & Analytics

Differences Between GenAI and AI

While GenAI has gained significant attention in recent times, businesses have long used AI for vital tasks like fraud detection and personalization. Learn the distinctions between GenAI and Analytical AI and how you can unleash the potential of AI in your business.

Artificial Intelligence & MLOps