Within the past ten years, data, especially “Big Data,” has become an invaluable resource for any business. From the smallest vendor to the largest multinational enterprise, being able to store, organize, and draw insight from increasingly large amounts of data is a critical aspect of any system’s design.
In a nutshell, data lakes provide a solution, allowing for the rapid ingress of any and all data. They facilitate many ways to share, utilize, and transform data, and are a cornerstone to the rapidly maturing technology of Machine Learning.
What is a Data Lake?
A data lake is a repository designed for centralized storage of structured and unstructured data. Importantly, data lakes are created to handle massive amounts of data – enough to ingest and store all the data produced throughout a company. Their storage ranges into multiple petabytes and their design allows them to rapidly integrate nearly unlimited amounts of data.
Data lakes are flexible enough to incorporate structured, semi-structured, and unstructured data. They provide a central repository for all data across an enterprise and are scalable enough for any situation.
What Are the Benefits of Data Lakes?
Data lakes, based on their design, offer a myriad of benefits which are difficult to find elsewhere. Namely, they:
Provide a central authoritative source for all data
Store all data at maximum quality
Offer unparalleled versatility and scalability
Democratize data, allowing its use by all stakeholders
Support many data schema
Ignore traditional size limits
Most importantly, data lakes drive complex analysis, and are inherently designed with Machine Learning and predictive analysis in mind.
Data lakes, as an inherently flexible technology, have an almost infinite number of use cases. Some increasingly common instances include:
With their rapid ingestion capabilities and insight-driven model, data lakes are perfect to handle data from telemetry, equipment logs, and sensor readings.
Data lakes are flexible enough to adapt to changes in this data automatically and are well-designed to work with the complex analytical systems tied with IoT data.
Data mining, an already powerful way to drive insight in a business, is augmented by the inclusion of a data lake. Usually called data fishing, mining data from a data lake allows for more insight than could be achieved otherwise.
Data fishing can find and extract patterns from data that at first seem completely unrelated. Data lakes, since they contain all of an enterprise’s data, allow for data fishing to make connections which were otherwise impossible.
As with many aspects of the online ecosystem, marketing has become a data-driven science. Data lakes can collect data from marketing campaigns and present it in an easily accessible way, simplifying the otherwise complex process of aggregating the various forms of data. Applying machine learning to marketing, facilitated by the use of a data lake, can rapidly increase the effectiveness of marketing campaigns.
Ultimately, modern systems thrive off of an abundance of data, and data lakes are a great solution to collect and provide data for many applications. With their strong compatibility with ML and other complex analytical structures, data lakes are a key part of the big data revolution.