Benefits Of Data Lakes For Technology and Business

For the past 7-8 years, big data and data lakes have been popular buzzwords in IT. And increasing amounts of evidence imply that its adoption has been a huge success. Businesses now define their internal company objectives and benchmarks using data. Additionally, it takes care of the externals as well, including the interaction between the business and its clients and suppliers.

Data Lake: What Is It?

A data lake is a central repository for all of your data, whether it be structured or unstructured. Simply stated, you may store data there without having to worry about organizing it or performing various types of analytics on it.

Data Lakes’ Business Benefits

1. Access to Data Everywhere

No of their position, all employees have access to data thanks to a data lake. We refer to this as data democratization. For instance, only the highest managers in your company may currently have the power to gather all kinds of data to get a sense of things before making important decisions. The essential data, however, is made available to all levels of employees via a Data Lake, regardless of their rank or title. Let’s imagine you are an administrator and have access to all administrative information, such as used and unused stationery. Additionally, you will have access to other data that you can choose to ignore. Simply stated, a Data Lake may be compared to services like LinkedIn. Similar to how LinkedIn allows you to choose who you want to connect with or not, Data Lakes allows you to select the necessary data to achieve various business goals.

2. Retrieving Quality Data

Businesses can easily access high-quality data thanks to the Data Lakes’ processing capacity and the technologies at their disposal.

Data Lakes’ Technological Benefits

1. Decision-Making in Real Time

Real-time decision analytics are achieved using deep learning algorithms and massive quantities of consistent data from data lakes.

2. Flexibility in Schema

Your data must be in a structured manner to use traditional schema. Standard data warehouses employ schema-based solutions. But since the data must be evaluated in its raw form, this could be a problem for analytics. You can create different schemas for the same data using Hadoop Data Lake, or you can work without any constraints. In essence, it enables schema and data separation, which is advantageous for analytics.

3. Enables The Use of Other Languages And SQL

SQL is supported by traditional data warehouse technology, which is sufficient for straightforward analyses. You need more options for data analysis for complex use cases. For analysis, Big Data Lake provides a variety of options and language support. It has SQL-supporting Hive/Impala/Hawq systems. Additionally, it has features to handle advanced requirements. For example, PIG can be used to evaluate data flow, and Spark MLlib can be used to perform machine learning.

4. Versatile

Both organized and unstructured data from many sources can be stored in a data lake. In other words, it can hold binary, people, sensors, XML, log, multimedia, chat, and social data.

5. Scalable

Data Leaks, in contrast to traditional data warehouses, not only enable scalability but is also very cost-effective.

A Data Lake Is Required

Organizations that could produce economic value from their data outperformed their competitors, according to research. The organization that integrated Data Lakes outperformed comparable companies by almost 9%, according to the survey. These firms used log files, social media, and internet-connected gadgets stored in the data lake to do novel sorts of analytics like machine learning. This has made it possible for organizations to see and seize opportunities, aiding in faster company growth in terms of productivity, luring and keeping customers, making wise judgments, and more.

Frequently asked questions:

What is the benefit of the data lake for business?

Both relational and non-relational data, such as that from social media, IoT devices, operational databases, and line-of-business applications, can be stored in data lakes. Through data crawling, cataloging, and indexing, they also enable you to know what data is in the lake.

What is a technology data lake?

Large amounts of organized, semi-structured, and unstructured data can be stored, processed, and secured using a data lake, a centralized repository. No matter how diverse or large the data is, it can be processed and saved in its original format.

How can data lake help companies do more valuable analytics?

It offers a single location to access multiple data sets simultaneously. Data scientists can evaluate specific information as needed because it typically retains raw data.