In a modern storage system, a data lake is something that has created a huge buzz around everywhere. And NO, it’s not the same as a data warehouse. The term ‘data lake‘ could be new for many folks and they would be finding the answer for ‘what is a data lake?’. But people related to data practice must have come around this word earlier.

A data lake is a new tool used by companies dealing with generating and processing large amounts of data for operations and Machine Learning initiatives. It is being used to manage and organize an infinite amount of data.

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What is a Data Lake?

Here is an in-depth answer to the question you might be curious for – what is a data lake?

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. Its functionality is not limited to the size of the data.

Data gathering and usage are continuously increasing. Keeping a view with digital reports it’s set to reach 44 trilling gigabytes usage till the year 2020. But the problem is all the data is not structure. Almost 90% of the data is in unstructured or semi-structured form that poses a big challenge to data management. Here Data lake comes into action.

Data lake provides storage capability to big data in its raw format, without requiring any change, even a bit.

Data lake definition

In simple terms data lake gives free do easily hold and process any amount of data in its real raw, original form.

Why data lake is required?

The data lake is required for enterprise and businesses who needs unlimited storage for their unfiltered and unstructured data.

Data Lake Architecture

Traditional data lakes architecture was originally designed to store and process the data but with some limitations. The Traditional architectures e.g., Hadoop provides an on-premise environment without utilizing the cloud fully.

The first generations of data lake architectures were needed to do manual capacity planning, resource allocations, data performance optimization, and so on. Before data cloud lakes traditional architectures were the only available option left to enterprises.

Just like Twilio, data lake is a modern technology to work with and it offers multiple advantages over traditional technology. In a modern data lake, the repository is injected with data, later search with search engines, such as elasticsearch.

Once the search is done, the data is analyzed and the results are processed. Here the raw data in its structured or semi-structured form is utilized in its raw format. Moden cloud-based data lake architecture also help organizations maintain and divide the workload.

Data exploration activities can surely slow down serious data analysis. To preview this ad hoc, data lake isolate, and load, and provide support to important ongoing data tasks. The cloud architecture makes the data lake simple. For the optimum level of performance, the cloud-based data lake should possess the following features.

The ability to add users without performance degradation
A robust metadata service that is fundamental to the object storage environment
The right tools to load and query data simultaneously without impacting performance
Independent compute and storage resource scaling
Multi-cluster, shared-data architecture

Data Lake vs Data Warehouse

The term data lake and data warehouse is often chosen interchangeably. While there are major differences in terms. You can assume Data lake to be a large pool in which every type of data (filtered, unfiltered, or semi filtered) is stored. It is always in its native raw format that does not require any processing or filtration in order to store.

On the other hand, a data warehouse is also a term used for the storage of data but in only filtered and structured form. The only similarity between both is that they are used to store the data. Data lake architecture benefits one enterprise at a time, whereas, a data warehouse can be used depending on its need.

Structured and Unstructured data

Data Warehouse stores only filtered and process data. It did not deal with unstructured data. Data lakes deal with unstructured data in its raw form and do not deal with structured data.

Undetermined vs In-use

The goal behind the individual data bits in the data lake is unclear. Sometimes it’s used for future events to gain actionable insight and sometimes, just to store them. This means there is a less organized value of data.

Data warehouse with processed data clears its significance and use in the first go. This means that the data stored in the data warehouse is only for specific use and purposes.

Users and Data Analyze

Data lakes require the additional capacity to see and analyze the data. The reason behind it is the unstructured and raw data which is hard to understand.

Data warehouse with structured data does not have any kind of analyzing the problem at large. They can be understood by business people easily.

What is the advantage of storing data in a data lake?

Now that you have understood about ‘what is a data lake’, its time to move on to data lake advantages. The modern data lake structured has opened new doors for accessing and processing the reasonable and required amount of data without putting strain on resources. The main data lake advantages are:

It provides Unlimited ways to query the data
It provides the ability to store all types of structured and unstructured data in a data lake,
Ability to store raw data—you can refine it as your understanding later.
Democratized access to data via a single, unified view. This works across the organization when using an effective data management system.
Ability to create value from unlimited types of data
Application of a variety of tools to gain insight into what the data means
Elimination of data silos in one go.
It provides flexibility and make data analysis easy.