Data Lake: What is it, the Need, its Architecture and the Benefits
Data Lake is a system where we can store unstructured and structured data and also a method helpful in organizing huge volumes of diverse data from multiple sources. Data lakes are vital for people in technology and business who want to discover and explore data by bringing all the data together in a single place.
With the emergence of modern technologies globally, many business owners are now eager to ensure that their organizational data is secured and organized. In this context, Data lakes help create a centralized place for management infrastructure that enables every company to store, manage, analyze and classify their respective data.
Due to the limitations of the data warehouses, data lakes were developed. Though data warehouses provide companies with highly scalable and performance oriented analytics they are very expensive and they can’t handle modern use cases comfortably. Mostly data lakes are used to consolidate the organization’s data in a centralized and single location. Data lake not only helps to convert different kinds and formats of data but also to discover insights and trends within the data. It ensures data is secured and readily discoverable, based on the needs and be accessed.
What is Data Lake Architecture?
A data lake is the repository of the information in the raw format in which it existed when it was actually collected and added to any of the storage pools. The data that is inside might be taking various forms and that is not arranged in any specific way. The architecture of the data lake refers to the features that are included in the data lake to make it simple and easy to work with. Though the data laws are not structured, it is vital to ensure that they offer the design and functionality features that your company requires to easily interact with the data they have.
The Need for a Data Lake
Companies gain competitive advantage in their respective industries by deriving value from the data. Data lake helps in transforming the business by enabling a singular repository for the organization’s data (external, internal, unstructured and structured data) which helps the business analytics and data governance team to mine the data. A data lake helps to store the data in the same format as it is imported from the source systems or can transform it before using it. One of the important purposes of a data lake is to make organizational data which is sourced from different ways can be accessible to various end-users such as data scientists, data engineers, executives, product managers etc to leverage insights that will help to improve the business performance.
Benefits of Data Lake:
No Data-Silos – Data is stored in multiple locations in different ways with no centralized access management creating silos. It is a challenge to have access to it and perform analysis. Data lake helps to break down these data-silos and provide access to the specific and required data for achieving meaningful insights.
Better Data-Governance – Data-lake enables you to receive both unstructured and structured data from multiple data sources and store it in a secure repository which is centralized at any scale. This helps to gain better data governance and total control.
No -Predefined Schema- There is no need to have a predefined schema with data-lake. Without having any information and depending on the type of analysis, you can process the raw data which might be required in the future.
Storing Data In Any Format – Data-lake helps to eliminate the need of data-ingestion and data modeling. You can store the data in data-lakes in any medium and format i.e time series databases, file systems, NoSQL Databases, RDBMS etc. Data can be loaded in its already existing format such as log, parquet, CSV, XML etc without any changes. Data-lakes are affordable when compared to the traditional data warehouses as it enables to store data without any predefined schema or format.
Real-time Decision Analysis – Data-lakes have the advantage of using large quantities of consistent data and deep learning algorithms to achieve real-time decision analysis.
Depending on the Automated Data Governance Platform the data lake makes things simple for you as it can handle multiple data structures such as multi structured and unstructured data and brings out the best value from the data.