Data Lakes: Migrating and Organizing Your Data Efficiently

Based on a 2017 Aberdeen survey, organizations that implemented a Data Lake into their infrastructure outperformed similar companies by 9% in organic revenue growth. By organizing information into Data Lakes, leaders of these companies were exposed to new types of analytics such as machine learning. As a result, companies were able to identify opportunities for business growth faster by retaining customers and increasing productivity.

Effectively use and understand your data

The average company is seeing the volume of their data grow at a rate that exceeds 50% per year. Additionally, these companies are managing multiple data sources for analysis. Having this amount of rapid growth can cause problems with efficiency for companies that heavily rely on data. Implementing a data lake can alleviate any problems associated with extreme growth in data.  

What is a Data Lake?

A data lake is a centralized repository that allows you to store your company’s structured and unstructured data. Data can be stored as-is, without having to re-structure any information, while running different types of analytics. You can store your data as-is, without having to first structure the data, and run different types of analytics such as: 

What are some of the key AWS Data Lake tools used in the process?

The AWS Cloud provides many of the building blocks required to help businesses implement a secure, flexible, and cost-effective data lake. These include AWS managed services that help ingest, store, find, process, and analyze both structured and unstructured data. Here are some of the tools our team uses and key benfits in building Data Lakes on AWS:

Here is an example data lake architecture using these tools and services:

AWS Data Lake Architecture

Why should your business consider implementing a Data Lake?

There are several reasons why organizing information into a data lake should be something to consider at your company.

1. Efficiency of data capture

Effective analyses rely on different sources and applications. Top companies spend less time finding and gathering data, and allocate more time analyzing their information.

2. Data accessibility

Once companies have gathered the right data from a variety of sources, they are then able to hand-off information to data professionals and decision makers with ease.

3. Timeliness of information

Users are able to get information fast and efficiently within their set window of time.

What is the Future of Data Lakes?

Over time, the use of data lakes will exponentially grow and will continue to protect company data. Once data is processed and in the cloud, it becomes easier to move information into Artificial Intelligence or a machine learning model to get the relevant information out of the data that exists, while protecting future data. In the long-run, data lakes are about turning data into insights that drive value for businesses in the future.

Interested in learning more about how you can implement a data lake in your company? Contact us to speak with a cloud architect or software engineer. We would be happy to provide an assessment of one of your workloads.


Jurel Castillo

Software Engineer

Jurel Castillo is a Software Engineer at EagleDream Technologies. He is a Rochester Institute of Technology Alum with a background in data lake infrastructure and building APIs. Jurel holds a Cloud Practitioner certification in AWS and is using his industry knowledge and skills to work with clients in order to design and build web applications while working to deliver exceptional customer experiences.

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