Mark McQuade, Practice Manager, Data Science & Engineering at Onica was recently featured in ITProToday for his insights on data lakes, their utility to organizations and how simplifying their deployment and management can help organizations save significant time and resources.
Simplifying Data Lakes Saves Organizations Time, Resources explores the complexity of data lakes, their construction, as well as how they are enabling organizations to leverage technologies such as machine learning which can guide decision making, resulting in greater business outcomes. The article posits that simplifying these data lakes can result in significant cost and time savings for organizations, and offers suggestions on how organizations can go about doing so while building their own data lakes infrastructure.
Mark McQuade finds that data lakes offer tremendous value to businesses in the form of repositories of data from where processes such as machine learning can help transform their accumulated data into sources of business insight. While their utility is high, data lakes can be challenging and time-consuming to build, often taking months at end. Once deployed, the responsibilities do not end, requiring organizations to resource their management, updating, and maintenance, over the course of their life.
Simplifying Data Lakes is an important step to ensure businesses are deriving maximum ROI. According to Mark McQuade, “By perfecting your organization’s maintenance of data lakes, you cut back on the in-house expertise and resources needed to keep everything running smoothly, freeing up IT teams to focus on more pressing projects, which saves your organization costs in the long run.
AWS Lake Formation, a data lakes set-up service recommended in the article, offers features such as Blueprints for data ingestion, Permission-based data sharing for security, and support for queries through AWS services such as Amazon Athena, that can significantly simplify the process of building data lakes and managing them over their life.
Mark McQuade also offers his ideas on what organizations should avoid doing to simplify their data lakes. Avoiding on-premise deployments that don’t offer the scalability of serverless data lakes, and require software and hardware upgrades, is one good step. It is also vital that organizations employ prudence in their data lake development strategy, factoring in scalability, and flexibility to accommodate future workloads.
To read the full article, visit ITProToday.
If you’d like to explore how you can simplify the deployment of data lakes in more detail, check out our in-depth guide. If you’re interested in setting up your own data lakes for machine learning or data analytics, get in touch with our team today!