Enterprise data lake is technically referred to as a storage repository that’s able to hold enormous volumes of raw data in its native form.
This data includes structured, semi-structured, and unstructured data.
However, the important thing is that the data structure and requirements aren’t defined up until the data is needed for some purpose.
Data lakes could involve the use of a variety of storage and processing tools for the sake of extracting value as soon as possible and inform the key organizational decisions.
Why enterprise data lake?
Due to the growing variety and volume of data in general, data lakes are emerging approaches, especially since IoT and cloud-based applications have become common for big data.
But most importantly, they play a major role in some of the common factors of our technological sectors.
In an organization, the most powerful impact of data lakes is the adoption of innovative measures.?
An enterprise data lake could provide a foundation for clearing away the enterprise-wise data access problem. New measures like data mining and exploratory analysis could be adopted which allows some of the newest possibilities.
A lot of million-dollar enterprises struggle to establish such a culture that’s based on data-driven approaches and innovations. The ill-management of data into divisionally-divided data stores and issues with ownership causes a lot of disruption in the smooth functioning of such organizations.
In today’s world where everything is based around data and numbers, the new data consumption requirements and use cases are emerging at a fast rate.?
If a single approach doesn’t work out for the users, say the preparation of a requirements document, the users are more likely to settle for new approaches.
For enterprise data lake, when the source data happens to be in a particular central lake without any control structure or schema embedding, supporting a new case could be far easier and better.
Data mining revolves around the concept of allowance to data scientists to uncover the previously unseen patterns and truths due to different models.
One of the most straightforward examples of data mining is clustering analysis where it could be applied to different sets within a data lake. It helps in grouping volumes of data together only because of their similarities.
Through the data visualization tools, data mining also helps in clearing up the chaotic nature of raw forms of data.
Enterprise Data Lake Maturity
Data Lakes are a new and modern concept. Without much availability of awareness about the same yet, there definitely are a few more things to learn about it.
Data Lakes are pretty useful to illustrate some of the maturity stages that you are likely to observe.
Data Puddles are single-project data that are built using Big Data. In fact, one of the first steps to adopt big data technology is data puddles.
Data that are present in a data puddle is loaded for a single project or a team that is usually well-defined.
This illustrates why big data is used rather than traditional data warehousing because it reduces the costs and provides a lot of better performance.
The collection of data puddles is called a data pond.?
Data Ponds aren’t that organized to be termed as data warehouses, but they aren’t so badly-structured either. Data Ponds could be termed as a collection of data marts or an offload of an existing data warehouse.
Data Ponds have a few extra advantages, like better scalability, lower costs, and obviously, attractive benefits.
However, such ventures still require a high level of IT participation. The data ponds could also limit the data for a single project only and use the same for only such a project that needs it.
However, the costs for IT management of such data, it’s only safe to assume that small business ventures might have second thoughts on relying upon data ponds.
Enterprise Data Lakes
Data Lake could be compared to a large container that’s able to contain enormous volumes of data that could be converted and used as per the need.?
enterprise data lake have a significant difference from data ponds.
Not only does it support self-service, which actually allows the businesses to find and use different data sets they want to use without help from the IT depts.
But it could also contain data that business users might want their hands on in case no project has the need for them at that very time.
Finally, data ocean could prove useful when you have to combine all the factors of self-service data along with the data-driven decision making to your whole enterprise data.
It doesn’t matter where the data is or whether it was loaded in the enterprise data lakes or not, you can put such an approach to your disposal.