Data analysts love structure; It comes with the territory. However, that cliché is ready to be flipped on its head. With the digital universe expanding every year, approximately 90% of data is unstructured. So, how do we deal with this? This is where data lakes come in. They allow all data formats to flow in and be stored until they need to be used and analyzed. Then, the data can flow out for analysis. This is a cost-effective way to dump organizational data without bothering about storage capacity and whether the data is useful – until it is.
Data Lake vs. Data Warehouse
The terms Data Lake and Data Warehouse are sometimes used interchangeably but they are not the same. Both are storage repositories that create a one-stop data hub that can feed into various applications. However, that is where the similarities diverge.
- Data lakes are designed to collect data in real-time and be stored in their original format. This is more efficient because only the data that is relevant will be processed and prepared for analysis. Data is periodically structured and uploaded into a data warehouse, making it less agile. Since it uses a columnar data store, only structured data is viable.
- Data lakes rely on low-cost storage options so they can be highly scalable and support all types of data, even if that data is not currently being used. A data warehouse, on the other hand, eliminates what it considers to be irrelevant data due to limited storage.
- Lastly, the amount of data management involved makes a data warehouse much more expensive to maintain compared to data lakes.
Which one is more suitable is a debate that will go on for some time to come, but they serve different purposes. If a company has a large amount of operational data, a data warehouse can be the way to go. However, if Predictive Analytics, Machine Learning and AI is the path to follow, then a data lake storage system is a must.
Data Lake Architecture
Let’s go back to the lake analogy to explain the abstract complexity of data lake architecture. Have you ever been to Lake Michigan? The vast stretch of water is a great place for people to paddle board, fish or just enjoy a boat ride. It is also a major source of drinking water. In short, it is multi-purpose. Similarly, a data lake caters to many purposes and to multiple stakeholders and can be scaled up to do even more.
Data lake architecture components make this scalability possible. It allows structured and unstructured data to flow in, either in real-time or in batches. This creates a core raw data store with no structure transformation. Then comes an analytical sandbox that is used to work with the data and make connections for how it can be utilized. Finally, a batch processing engine processes the data into a usable format.
Data lakes are ideally suited to be deployed in the cloud. AWS claims that their portfolio of services is the most secure and cost-effective, among other things. Microsoft Azure Data Lake and Google Cloud Storage are some of the other major players.
Why Big Data Needs Data Lakes
Just because it is a cheaper way to store and manage data is not the only reason to consider using a data lake. Modern capabilities, including Predictive Analytics and Machine Learning, are enabling organizations to use external data from social media and the Internet of Things to make data-driven decisions. These insights can drive changes to existing business processes. AI-based analytics platforms consume all kind of file formats including XLS (X), CSV, XML, SQL, CDC and HL7. Unstructured data from social media, videos, emails and text as well as semi-structured data from weblogs, clickstream analysis, and sensor data are a few of the sources. Software and the combination of multiple technologies can transform these raw data formats into predictive insights in just a matter of minutes.
The Challenges
Data lakes might be scalable, fast and flexible, but they do come at a price. A data lake can easily turn into a data swamp since it allows all data to be stored. Just because space is available for storage doesn’t mean that all data will be useful. If the data lake isn’t managed, it could quickly get out of hand. Additionally, unique metadata tags must be added to this vast amount of data streaming in so that it can be retrievable later. Without these tags, the data can dissolve into a murky mass which is of no use to anyone. Since a data lake accepts data from many sources, security is another factor that is of primary focus. These kinks are rapidly being ironed out as data lakes continue to develop.
Though businesses are beginning to realize the benefits of data lakes, sources are recommending leaving their use up to data scientists. However, Sertics eliminates the need for data scientists through an easy to use drag-and-drop interface and understandable data visualizations. Don’t wait to implement a data lake. Schedule a Sertics product demo today.
