5 Incredible Ways to Use Your Hadoop-Based Data Lake

When a new concept comes along, the first thing it has to prove to the marketplace is why it is a valuable thing and what it has to offer that previous ideas can’t. Such is the case with both Hadoop and the data lake. Neither is easy to master, and both come with considerable investments of time and expense. What does your organization stand to gain for these investments? What can you actually get done with a data lake built on Hadoop?

1. Amass Enormous Repositories of Data

There is no actual definition of ‘big data’ in terms of how much data it takes to call it ‘big data’ as opposed to just a really big data warehouse. What, then, is the amount at which you need to begin preparing for ‘big data’ with something beefier than a data warehouse—like a data lake? That would be the point at which your current infrastructure is stressed or inadequate for housing the data you wish to store. Data lakes are also excellent for organizations that know they have lots of data they want to leverage, but haven’t yet determined the exact uses or methods and tools for analysis. Data lakes do not force you to format the data until you’re ready to do so.

2. Store and Analyze Varied Data from Disparate Sources

Do you have lots of historical data and log data that you’d love to hang on for the purpose of analysis? Data lakes can collect and store data from various and disparate sources, including legacy systems on the mainframe, specialized systems like CRM or ERP software, website metrics, spreadsheets, text documents, email systems, and much more. Later you might discover a purpose for correlating the data from your website and that from your email systems—if so, you’ll have all of it, stored in its original format, for analysis. Hadoop-based data lakes can collect data that is unstructured or semi-structured, so that you can collect these types of data no matter how much you need to gather.

3. Collect and Process Data Extremely Rapidly

The future of big data is headed strongly in the direction of real-time analytics. This area of data analytics is growing much faster and stronger than other types of analysis that takes hours or days or even months. If you need a way to gather large quantities of data in real-time or huge batches of data that come in all at once, a Hadoop-based data lake is ideal.

4. Eliminate the Dreaded Data Silos

There is a lot of talk nowadays about the ‘data silos’ that disrupt the open use and sharing of organizational data. Each department’s systems hold critical data on operations, finances, projects, customers, and more. Eliminating the silos gives the business a way to delve deeply into business intelligence, a holistic picture of all the customer touchpoints with the organization, and much, much more. A data lake can replace all of those silos, making the data readily available both to the department that produces and uses the data, as well as others with a need for access, all without changing operations or the flow of the data.

5. Delve into Predictive Analytics and Machine Learning

Of course, one of the most powerful uses for Hadoop is the ability to use historical data to predict future trends. Taken even further, machines can be programmed to take in and remember new information in relation to what they already know, building a knowledge base for themselves to allow them to actively ‘learn’. Predictive analytics is powerful in terms of determining future market trends, and machine learning is valuable for an entire host of applications, from medical science to self-driving cars. A data lake is perfect for housing the enormous repositories of data necessary to drive predictive analytics and/or machine learning initiatives.

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