What is the need of data lake?

A data lake is a centralized repository that stores both structured and unstructured data of any size. We can use dashboards and visualizations to guide informed decisions. Also, we can run various types of analytics-from big data analysis, real-time analytics, and machine learning-without having to first structure the data.

Why do we need to use a data lake?

According to an Aberdeen survey, the organizations that introduced a Data Lake in their organisations have outperformed comparable companies by 9% in organic revenue growth.

Companies that are competitive in generating market value from their data would outperform their competitors by implementing the data lake and moving their data to the cloud.

By attracting and retaining clients, growing efficiency, proactively managing devices, and making informed decisions. They were able to recognize and respond on opportunities for faster business growth. The leaders were able to perform new forms of analytics, such as machine learning, using new data sources in the data lake, data from social media, click-streams, and internet-connected devices etc.

A database that is specifically designed for analysing relational data from transactional and line-of-business processes is known as a data warehouse. The data structure and schema are specified ahead of time to optimize for quick SQL queries, with the results usually being used for operational reporting and analysis. Data is cleaned, enhanced, and transformed so that it can serve as a reliable "single source of fact" for users.

By looking at the benefits of the data lake, organizations that are using data warehouses are trying to expanding their warehouses by including the data lakes in a very fast and rapid manner. It supports broad query capabilities, data science use-cases, and advanced capabilities for exploring new knowledge models. This evolution is known as the Data Management Solution for Analytics or DMSA, according to Gartner.

A data lake is very different from other databases because a data lake stores both relational and non-relational data that can be collected from mobile apps, IoT computers, and social media or any other data source.

In the process of the construction of the data lake, there are some points that must be kept in mind.

The Movement of Data

We can import any amount if real time data with the help of the data lake. Data is extracted from a number of different data sources and stored in its original format in the data lake. The method helps us to scale to any size of data while saving time on data structure, schema, and transformation definitions.

Data Security

Data lakes can store data from operational databases and line-of-business applications, as well as non-relational data from mobile apps, IoT devices, and social media, in other words we can say they can be both relational and non-relational data. Crawling, cataloguing, and indexing of data give us the opportunity to understand what data is in the lake. Finally, data must be guarded to ensure the safety of our data properties.

Machine Learning

Organizations would be able to produce a variety of insights using Data Lakes, including historical data reporting and machine learning, in which models are designed to predict possible outcomes and propose a series of recommended behaviour to achieve the best result.

Examples of Data Lake

Below are some major examples where the data lake has added value to the data.

Improve R&D Innovation Choices

A data lake will assist our R&D teams in testing hypotheses, refining assumptions, and evaluating results-for example, selecting the right materials in our product design for faster performance, conducting genomic analysis for more efficient drugs, or determining consumer willingness to pay for different attributes.

Increase Operational Efficiencies

A data lake makes it simple to store and analyze machine-generated IoT data in order to find ways to cut costs and boost efficiency.

Challenges to be taken care of in data lake

The key drawback of a data lake architecture is that raw data is processed with little control over what is stored. To make data accessible, a data lake must have specified processes for cataloguing and securing data. Data cannot be identified or trusted without these components, resulting in a "data swamp."

Data lakes must have governance, semantic continuity, and access controls in order to meet the needs of a broader audience.

Scalable Data Lakes

AWS is used by tens of thousands of customers to operate their data lakes. Today, there are a number of manual and time-consuming activities involved in setting up and maintaining data lakes. AWS Lake Formation automates these processes, allowing us to build and protect our data lake in days rather than months.

Because of its unparalleled 11 nine of durability and 99.99 percent availability, the best security, enforcement, and audit capabilities with object level audit logging and access control, the most versatility with five storage levels, and the lowest cost with pricing that starts at less than $1 per TB per month, Amazon S3 is the best place to create a data lake.

Purpose-built Analytics Services

AWS offers the broadest and deepest suite of purpose-built analytics services, all of which are tailored to our specific analytics needs. These services are all built to be the best in class, so we'll never have to sacrifice efficiency, size, or cost when we use them.

Spark on Amazon EMR is 1.7 times faster than Apache Spark 3.0, and we can run Petabyte-scale analysis for less than half the price of conventional on-premises solutions.