What is Data Lake?

A Data Lake is a large-scale storage repository for structured, semi-structured, and unstructured data. It's a place where you can store every kind of data in its original format, with no restrictions on account size or file size. It provides a large amount of data to improve analytical performance and native integration.

A Data Lake is a large container that resembles lakes and rivers in appearance. Structured data, unstructured data, machine-to-machine communication, and logs all flow through a data lake in real-time, much as tributaries do in a lake.

The Data Lake is a cost-effective way to store all of an organization's data for later processing that democratizes data. Instead of focusing on the data itself, research analysts should concentrate on discovering meaningful trends in it.

The data lake has a flat architecture, unlike a hierarchical Data house where data is stored in Files and Folders. In a Data Lake, each data entity is assigned a unique identifier and tagged with a collection of metadata.

 

Data Lake Architecture

So, how are data lakes able to store such large and varied quantities of information? What is the architecture of these vast repositories underneath the surface?

A schema-on-read data model is used to build data lakes. A schema is a database's skeleton that defines its model and how data will be organized within it. Consider a blueprint.

Because of the schema-on-read data model, you can load data into the Data lake without worrying about its structure, giving the organization a great deal of freedom.

On the other hand, Data warehouses are schema-on-write data models, which are a more conventional method for databases. 

In a schema-on-write data model, every set of data, relationship, and index must be specified in advance. This limits flexibility, especially when adding new sets of data or features to the database that could potentially create loopholes.

 

Why are Data Lakes Used?

Structure and clean data are essential for data warehouses, but data lakes enable data to be in its most natural state. This is because advanced analytic tools and mining applications take raw data and transform it into actionable information.

Big Data Analytics

Big data analytics will dig deep into a data lake to discover patterns, industry dynamics, and consumer tastes. These analytics assist companies in making more accurate predictions. Big data analytics is obtained by the following studies-

Descriptive Analysis : It is a retrospective analysis of a business's "where" a crisis might have originated. Most big data analytics is descriptive these days, as they can be generated quickly.

Diagnostic Analysis : Another retrospective analysis aimed to identify "why" a particular issue arose for a company. This is a step forward from descriptive analytics.

Predictive Analysis : This research combines both AI and machine learning applications and provides an enterprise with predictive models of what will happen next. It isn't widely used yet due to the complexity of producing predictive analyses.

Perspective Analysis : Prescriptive analysis not only aids in decision-making but may also be able to provide an enterprise with a series of solutions. Perspective analysis has been proven to be the future of big data analytics. These analyses use a significant amount of machine learning.

 

Why do Data Lakes Matter?

Data lakes came about as a result of the reduced prices of high storage capacity. Data was previously both costly and time-consuming to obtain, so it made sense to only collect data that deemed to be "business essential" and stored it in a centralized data warehouse. Even though this meant losing out on possible information lost in the data discarded during the extraction process.

The cost of storage has dropped dramatically in recent years, while bandwidth has skyrocketed and data volume has expanded. Storage can be sourced on-demand, scaled up and down to meet company demands, and exploited with low management overhead using cloud technologies. All of this allows companies of all sizes, to not only archive everything but also to hold data over prolonged periods. It increases the probability of finding actionable market opportunities that would otherwise go unnoticed.

Data lake technology makes it easier to handle and manipulate data regardless of its format, quality, or location. The data lake is a good development in cloud technology considering the rapidly increasing data volumes that companies have to deal with.

Consider precision farming, where gigabytes of data from the smart field and machinery sensors must be gathered and fed into Big Data analytics, AI, and process automation software to determine the best way to optimize yields. When you consider the various sources of data, different equipment suppliers, farm management systems, and regulations, doing all of that in real-time using a data warehouse will make precision farming both difficult and expensive. However, with a data lake, it becomes a much simpler and more viable approach.

Data lakes, in turn, allow for the development of more flexible and cost-effective data-driven applications. However, this isn't the only advantage; here are a few more:

Data lakes don't have to be centralized- they can be decentralized and located closer to the source of the data. As a result, applications can be pushed to the network's edge for faster processing and lower latency.

Data lakes are more open and transparent than traditional data warehouses, allowing analytics teams to broaden their reach while also facilitating the creation of line-of-business applications and the delivery of self-service access to useful business insights and data-driven tools.

Instead of fitting the data to the applications and analytics, data lakes enable applications and analytics to suit the data. Both internally and externally – for example, after a new company acquisition or spin-off – applications and their users can be allowed to discover and adapt to new data sources, as well as mix, match, and merge data sources far more easily.