What is data lake? Let's find out here! By M Salim Bupati Rembang

Have you ever heard the term big data, data lake, and data warehouse? All three are popular terms in large-scale data storage. As the name suggests, date lake is likened to a vast lake where unlimited data sets become water.

Data lake is not just a storage space for various types of data. For companies engaged in related fields, lake data is useful for finding relevant data.

In addition, smaller data sets can be analyzed to find solutions to various questions related to business, users, trends, and so on.

What is data lake?
Data lake is the center of gathering data in its original format and scale. You can store various types of data without the need to arrange them in a certain structure, grouping or hierarchy. In other words, the data contained in lake data is raw data that has not been processed or analyzed.

Lake data can be used to store data from various sources. The data in it also consists of various types and schemes. Various kinds of users from anywhere can access lake data and take data samples from it.

There are a number of components that make up lake data, namely:

Ingestion and Storage data that is useful for receiving data, either in real time or in groups. This component also allows users to store and access data.
Data Processing, namely the ability to work with raw data so that it can be analyzed through a standard process.
Data Analysis, which is a module with functions obtained the results of systematic analysis of data
Data Integration, or the ability to connect applications with platforms. However, first the data must be extracted first in the format as needed.

Why is Data Lake important?
The components of Data Lake provide various functions that help companies to get more consumers, increase productivity, and make decisions. Everything contributes to rapidly increasing business growth.
You can get these benefits through the following work methods:

1. Indexing data You can store various types of data and databases. These include operational data, data from business applications, or non-relational data such as data obtained from mobile applications and social media.

Even though this is raw data, you can understand the contents of the data by cataloging, crawling, and indexing data.

1. Machine learning
   Companies can obtain operational and marketing descriptions through data obtained from data lake. These data describe trends and patterns of consumer behavior. Then, companies can apply machine learning to make predictive models and estimates of these data.

2. Develop interactions with consumers
   Data lake is able to combine consumer data from the CRM platform with the results of social media analysis. The merger can also be done with a marketing platform that describes consumer purchasing history.

This is useful so that companies can identify which consumers are the most profitable, what is the background of consumer behavior patterns, and what rewards can increase consumer loyalty.

1. Analysis The existence of lake data allows Data Scientists, data developers, and anyone who deals in related fields to access data according to the framework and analytical tools they have. You can also do analysis without the need to move data from one system to another.

More and more companies are starting to use data lake to get information about businesses and consumers that can be easily accessed. However, the company still needs to establish a system, process and regulatory model so that the benefits provided are more optimal.