Abstract
Big data, or massive data sets that can be used to make inferences and reveal patterns, has become an increasingly important part of modern business and can be leveraged in many different ways. There are a few different options for storing this data available, which the use case for the data will dictate. Here, we’ll evaluate whether a “data lake” or a “data warehouse” would better suit your needs.
To do so, we’ll compare the primary differences to be found between data lakes and data warehouses.
Data Lakes vs. Data Warehouses
How is Data Structured?
This difference is fairly evident in the name of each data storage type. Think about a lake, as compared to a warehouse: in a lake, its contents are all mixed together and everything is included. Warehouses are much more organized, with only that which is intended to be stored remaining. The same can effectively be said of the storage options you have.
A data lake is effectively a large catch-all repository for unprocessed data, while a data warehouse is typically used to store data that has been refined.
Who Tends to Use These Storage Options?
Largely due to the nature of the data stored within, data lakes and data warehouses hold utility for people with different use cases. As the contents are refined and explicit, business users will usually find data warehouses to be more useful, while the raw data found in a data lake is better suited to a data scientist, who has the skills needed to give the data a purpose. Furthermore, a data scientist is frequently more concerned with the big picture, while a business user has more specific applications for the data they’ve stored.
What is the Data For?
As data lakes are scaled to be so large, they are well-suited for storage needs, and their lack of structure can help facilitate big data analytics. Alternatively, structured and archival data warehouses are better suited for aggregating data and drawing out insights.
Can My Business Use Both?
Oftentimes, both are needed to effectively use the data that has been collected. Machine learning is benefitted by the largely unstructured format of the data lake, while business analytics are benefitted by data warehouses.
It also depends on the industry you operate in. Healthcare and education both produce vast amounts of unstructured data, making the data lake a good choice for their insights, while data warehouses are good for industries like finance, as its accessibility aids their particular operations.
Are you putting your data to good use? NetMGM can help you with this, as well as help you secure it. Reach out to us at 888-748-2525 to learn more.
ABOUT THE AUTHOR
Rafiq Masri
With over 25 years of experience in Information Technology, Rafiq is one of the most accomplished, versatile and certified engineer in the field. He has spent the past 2 ½ decades administering and supporting a wide range of clients and has helped position Network Management, Inc. as a leader in the IT Managed Services space.
Rafiq has built a reputation for designing, building and supporting top notch IT infrastructures to match the business objectives and goals of his clients.
Embracing the core values of integrity, innovation, and reliability, Rafiq has a very loyal client base with some customer relationships dating back 20+ years.
Rafiq holds a bachelor’s degree in Mechanical Engineering from the University of Michigan and has completed graduate programs in Software Engineering and Business at Harvard and George Mason University. Rafiq is a former founder and CEO of Automation, Inc. in Ann Arbor, Michigan as well as a valued speaker on entrepreneurship and technology at industry events such as ExpoTech and others.