AI and Big Data Storage: Data Lakes or Data Pipelines
Data is the foundation of today’s digital world. Artificial intelligence (AI) and machine learning algorithms can help any business discover, create and capitalize on smart opportunities across the board, and these tools require large amounts of data to be truly successful. Thanks to the Internet of Things, it is easier than ever before to capture data, but before you put all that information to use, it is essential to secure and store it in the safest, most cost-effective way possible. Two methods for doing so are through the implementation of data lakes and data pipelines, both of which prove to be beneficial for storing and analyzing data collected by your company.
What is a data lake?
A data lake is a centralized place where you can store all data (structured or unstructured) at any scale. Data lakes are geared towards providing a broad spectrum of information to the user, but also accepting many different types of analytics to yield better and more developed information. Organizations who implement data lakes can apply almost any method of analytics, including machine learning, and can leverage data collection from varied sources like click-streams and social media.
What is a data pipeline?
A data pipeline is a system that helps filter data and formats it to more efficiently yield helpful insights without any extra irrelevant data points. The use of a data pipeline is to provide concise data, making it easier to report, analyze, and use. Data pipelines pave the way for more efficient business intelligence, since they deliver data tailored to organizational and divisional needs by reducing data noise and providing only the information necessary to achieve a specific goal.
How can data lakes and data pipelines support your AI projects?
While both data lakes and data pipelines are beneficial in supporting AI, each has its own unique points that help differentiate which data solution to use when.
Machine learning is a rapidly expanding field that requires large amounts of data to be sifted through to find the trends and tendencies that give the information meaning. This need for large amounts of data is why data lakes are so beneficial in the field of artificial intelligence. While this may sound very complex, many of us experience the concept of data lakes and machine learning every day when we use the FaceID feature on our cell phones. With the IPhone XS in particular, the more you use the FaceID feature, the more easily the phone will recognize you with the addition of hats or sunglasses. This process takes information from each time you use the feature and compares it with previous data sets over time to create a broader understanding of the solution to the task that it is trying to complete. Other examples of machine learning and data lakes in everyday life are virtual personal assistants, social media advertising services, and search engine result refining.
Additionally, the use of large data lakes benefits a business by allowing people from various roles in the organization to use their preferred analytic tools to sift through the data and help find the information necessary for their specific department/task.
Data pipelines are the backbone of every artificial intelligence-embedded application. While on the surface it may seem simple and straightforward, the data pipeline behind the scenes is the reason for success. Data pipelines are a fundamental piece in the process of running an application. If every time you wanted to unlock your phone, for example, it had to search through every byte of information in its memory, looking for things that looked like a picture of your face, FaceID wouldn’t be a very useful feature. You would probably opt to type in a password instead of waiting for the process to complete. A data pipeline narrows down the pool of data such that it only has to consult relevant previous information. But data pipelines do more than just reduce the volume of data; they can also pull data from disparate sources, all while weeding out conflicting information and duplicates.
If your goal requires many types of data from broad sources, and you want the freedom to creatively analyze and explore your information once it’s been consolidated, consider a data lake. If you know what kind of information you need and you want to support your application with a reliable stream of tidy data, consider a data pipeline. Regardless of which method you choose, if you want business efficiency and success, consider that big data is here to stay, in a big way.