A data lake could technically be referred to as a central location where you can store all the data regardless of the source or format.
USUALLY, data lakes are built using Hadoop. The data which is present within could be structured or even unstructured.?
Data Lakes are pretty different from data warehouses as they allow the data to be in its raw form without the need to be converted or analyzed first.
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.
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.
Table of Contents
What are data lakes used for?
Data lakes present the data in its natural form. The reason for this is because of the advanced analytic tools or mining the software intake the raw data to convert it into useful insights.
Data Mining
Data Mining defines how data scientists are able to uncover the previously unseen patterns and truths due to different models.
A common example 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.
Big data analytics
Big Data Analytics is going to help you dive into a data lake for uncovering different kinds of market trends and customer preferences for the sake of helping out businesses and make predictions faster. It could be done through different analysis:
Descriptive Analysis – This type of analysis helps at looking where a problem occurs in a business. A lot of big data analytics now are quite descriptive as they could be generated quickly.
Diagnostic Analysis – This type of analysis helps at looking at why a specific problem occurs for a business. However, this approach is slightly more in-depth and uncovers problems more easily.
Predictive Analysis – With the help of AI and Machine Learning, this type of analysis could help in providing an organization that has predictive models to figure out what may occur next. However, it hasn?t been widely adopted yet because of its complexity.
Prescriptive Analysis – The future is prescriptive analysis. Why? Because it heavily influences and assists in different kinds of decision-making efforts and could even provide a set of answers to an organization.?
But, why Data Lakes?
One of the main objectives of building a data lake in the first place is to offer a very unrefined view of data before the data scientists.
There are a lot of reasons to use data lakes, but the most common ones of them are:
- Due to the increase in data volume, quality of the data and metadata, the quality of analyses increases twofold.
- With the onset of storage engines that involve Hadoop, storing disparate information is pretty easy and as such, there?s no need to model the data into an enterprise-wide schema with a Data Lake.
- With the help of AI and ML, data lakes could be used to make profitable predictions and offer businesses with a lot of agility.
- It also offers an advantage to implementing the organization competitively.
Benefits of using Data Lakes
- Data Lakes help in providing advanced analytics and product ionizing.
- These offer value from all kinds of data types and offer cost-effective scalability.
- Data Lakes reduce the long-term cost of ownership.
- It is adaptable to changes and allows the economic storage of files.
- It helps in the centralization of different types of content sources.
- It provides access to users all across the globe with authorized access.
[CONCLUSION] Why Data Lakes?
Despite a lot of challenges that are faced by data lakes, it?s safe to know that about 80% of the data is unstructured and as more businesses turn to Big data in the future, data lakes are going to get more users.
Unstructured data withholds information that can’t be kept in data warehouses. Even though they are strong in structure and security, big data just needs to flow freely into data lakes for better access to users without storing their information at all.