Taking a Deep Dive into Cloud Data Lake Performance: Making Sure Your Gold is Counted
By Anand Kumar • Nov 15, 2021
Data analytics and data science are the buzzwords of the moment for life sciences organizations, and for good reason.
As use of both structured and poly-structured data has grown, so has the potential to transform development of drug, therapeutics, diagnostics and devices.
However, for the majority of companies, the potential to leverage Big Data for milestone discoveries has been elusive, in large part due to issues within their cloud data lakes.
By better understanding the opportunities and challenges that data lakes present, life science leaders can more effectively approach data lake management to keep their data lakes from becoming data swamps.
Data Lakes: The New Gold
Cloud data lakes offer enormous amounts of raw data—what I call “the new gold.” Unlike data warehouses, which just store processed data, these cloud data lakes contain raw wealth—data that is just waiting to be utilized.
The problem lies in the ability to process and effectively catalog that data to put it to use. Considering that a 2017 study found that at that time, data scientists were already spending more than half of their working hours cleaning and preparing data, it’s easy to see why the explosion of the availability of this commodity would put a strain on any organization’s resources.
With today’s data becoming much more disparate, in addition to the larger volume now being ingested due to the cloud’s elastic capabilities, this cleaning and preparing of data has become even more of a burden.
The second challenge leaders in life sciences organizations face in data lake management is how to organize the large volumes of highly diverse data from the multiple sources these lakes represent.
It is essential that every life sciences organization have a separate cataloged data lake to be able to locate their specific data at the point of need. If you treat data as precious as a vault full of gold, you should have an inventory of that resource so that you know how much you have, where it is, and how to access it when needed.
In the same way, you must know what data you have and where it is stored. Anything less just makes data unsearchable and useless, turning your data lakes into data swamps.
A cataloging of your data lake solves this issue, ensuring that the metadata you ingest is extracted and properly recorded so it can be searchable for later use.
DataEZ: Advancing Big Data to Realize New Opportunities
The good news is this: For organizations able to harness the explosive levels of data now being generated, opportunities abound. From the capability to diagnose serious illness though advanced predictive modeling, the use of AI and ML at an earlier stage (when treatment is far simpler and less expensive) to the ability to scale up research, amazing benefits can be realized for patients, providers and organizations.
That’s why DataEZ from Healthcare Triangle was created -to offer an agile end-to-end solution to managing cloud data lakes and preventing their deterioration into data swamps.
DataEZ’s, software-as-a service(SaaS) platform addresses the immediate and pressing challenges life science leaders face by:
- Curating, cleaning and preparing poly-structured data to reduce the burden of data scientists.
- Self-cataloguing all data and metadata to make data searchable and usable for current and future researchers.
By utilizing DataEZ’s cost-effective solution, life science organizations can start taking control of their cloud data lakes in order to leverage their benefits and count their “gold” within as little as half a day.
Anand Kumar is senior vice president, Healthcare Triangle.