What is Data Lake Architecture?

What Is Data Lake Architecture

What is Data Lake Architecture?

Without having a complete idea about a well-structured data lake architecture, it will be hard to implement it in your business. That’s why you must know what is data lake architecture to apply it successfully and to gain the desired outcome.

But before digging further into details, let us first know what a data lake is: a data lake is a storage depository for holding every kind of data without any constraints. Such as it can store both structured, semi-structured, and unstructured data. Moreover, you can store data in its original format with no size or file limits.

With the help of a data lake, you will be able to organize your data effectively. The more your data will be organized, the more you will find making the right decision, which ultimately will benefit your business.

The only way to gain the most out of a data lake is to build and design the correct data lake architecture. Otherwise, even by data lake implementation, you will not find the desired result. Keep reading to know further in detail.

What is Data Lake Architecture?

A data lake must be made flexible and business-specific to make the most use of the data lake. Usually, it has a flat architecture for holding all kinds of data in a single large storage location that can be accessed easily and quickly.

The design of the data lake architecture is very crucial for creating an impactful data foundation. As if there is any lack of architecture, it may transform a data lake into a data swamp, which you will indeed never want to happen.

Data flows serially from one layer to another layer. While the data moves from layer to layer, these tiers at the meantime do the processing on the moving data with no or slight delay. The following are the main layers in a data lake architecture that works in the following sequence:

  1. Ingestion Layer: This layer is on the left side, representing the incoming data sources into the data lake.
  2. Insight Layer: This layer is on the right side, depicting the research part based on which various visions from the system are used.
  3. HDFS: In this layer, all kinds of data are stored until needed for further use.
  4. Distillation Layer: From the HDFS layer, this layer takes the data and transforms it into processed data for more accessible analysis.
  5. Processing Layer: Here, it runs various algorithms and user queries with real-time and shared data for generating structured data.
  6. Unified Operations Layer: This layer manages and monitors the system. Moreover, it controls the audit, expertise, data, and workflow management.

The most effective way of creating a data lake is to build a business model by taking key factors in mind, such as the company’s information systems, data ownership, etc. So, create one based on your business requirements. Otherwise, your data lake will not be able to serve its purpose.


What Is Data Lake Architecture

ARC and Data Lake Architecture

ARC offers the best data lake solution for supporting and meeting any business’s requirements using the latest cloud-based architecture at a reasonable cost. In fact, with the help of ARC, you can make the most use of your data. If you don’t trust our saying, then check the below points, you will automatically understand it.

  • You will find all of your data in a single location in a more organized and systemized way.
  • All the functionality of the data lake tool will be made simple and easy for your use.
  • Our data lake architecture will make sure that all kinds of data complexity are round to zero.
  • You will be able to store and handle a large volume of data without slowing your data management software and any limits.
  • Your users will be able to run several simultaneous queries (equivalent to infinity) against the data lake without affecting the performance.
  • Your data will be safe and secure even when it exists in the current cloud data lake.
  • You can easily create and execute combined and extensible data pipelines to load, process, and unload all the data virtually into the data lake.

Uses of Data Lake

A data lake plays a key role in every business and thus is used for the following key reasons.

  • With the data lake’s use, the data are organized systematically, which ultimately increases data quality. The more your data maintains quality, the more precisely you can make your company’s right decision.
  • It is cost-effective, and thus every kind of business can implement it easily.
  • It stores data in a single storage location. As a result, the transfer of data becomes easier and faster to handle.
  • With the help of data lakes, a company’s data loss can be round to zero percentage.
  • It can store any kind of data in its original format without any conditions.
  • It saves a lot of time that would have been spent in collecting the data from various sources. This extra time can be used in doing other vital works.
  • It offers agility in the business.
  • Various profitable outcomes can be predicted by using Machine Learning (ML) and Artificial Intelligence (AI).


What Is Data Lake Architecture

The Bottom Line

Nowadays, data is known as modern money, and if you are a businessman, you definitely know its meaning and importance. Hence, what can be a better solution than implementing a data lake in your business.

Now that you have a full idea about what is data lake architecture, our advice is to implement it in your business without further delay. As the more quickly you implement it, the faster you will find your data and business more organized. Which in return will be profitable for you and your business.