If you are unfamiliar with data lakes, a data lake acts as a central repository where structured and unstructured data can be stored.
Structured data includes data that has already been organized and formatted. Typically, structured information is found in a database, and the contents can be processed and analyzed more effectively. On the other hand, unstructured data refers to large text documents, videos, audio files, webpages, and even social media posts.
A data lake ingests data from all the above sources, allowing flexible analysis of large amounts of information to occur. In the big data environment, this can be used for a variety of different applications.
Prior to exploring the power of data lakes further, it is important to understand the key difference between data lakes and data warehouses.
Data Lakes vs. Data Warehouses: What’s the Difference?
A data lake is a pool of raw data consisting of structured and unstructured data. The purpose of the data being stored is not pre-defined, and the use gets defined at the time of need. Data warehouses consist of fully structured and processed data for a specific use or purpose. A data warehouse tends to be less flexible, costlier, and more complicated to make changes to.
In short, if the information insight needs of a business changes, as they regularly do, a data lake is the ultimate storage option – as raw data continues to flow into the lake, ready for future use.
Key Features of a Data Lake
Although, the constant flow of raw data requires more data storage than a data warehouse, there are some undeniable features and value propositions that make data lakes attractive to corporations worldwide.
Firstly, any amount of data created in real-time can be moved into the data lake. All the data collected and ingested from the various data source remain in the original format, allowing for ultimate simplicity when it comes to scaling, transforming, and defining data structures.
Secondly, data coming from different business applications and databases are completely secure and ensure the protection of your company data. Understanding your data is also made easy through crawling and indexing.
Thirdly, data lakes do not discriminate based on role or department. Data lakes allow any user with the proper access rights to view analytics on your company data without having to move information or use third-party analytics tools.
Lastly, with the assistance of an insight engine, countless of different insights can be gathered from the data stored in your data lake. Machine learning is capable of extracting knowledge to predict likely outcomes using all the information in the data lake at its disposal.
Uses and Value of a Data Lake
Storing more data in less time gives businesses much-added value for different uses across their organization.
Customer experience and valuable interactions with customers is an essential area that a data lake can positively impact. By combining data from platforms that hold social media, marketing, sales, and support analytics, companies can understand more about their customers. The insights retrieved can set the foundation for future marketing campaigns and sales tactics to win customers and keep them happy.
In addition, data lakes can be the backbone to management’s decisions on R&D innovation. Data lakes help R&D teams analyze everything from their own hypothesis to study results. Insights can then refine their product and lead to the most effective solutions for their customers.
On top of this, the accessible data storage and analytics collection that data lakes offer reduces operational costs and increases the quality of an enterprise’s data management techniques.
No matter the industry, data lakes offer companies cost-effective storage and scalability for enterprises with limitless amounts of data.
Are you ready to leverage the power of your data lake?
