5 Key Benefits Of Building A Centeralized Data-Lake

No More Data Silos

A data lake provides you the seamless access to all your data for more meaningful insights

Usually, data in most organizations is stored in various locations in different ways with no centralized access management. It’s challenging to have access to it and perform any kind of analysis.

Data-lakes break down these data silos and provide seamless access to the required data for meaningful insights and faster innovation.

A centralized data lake eliminates data silos i.e data duplication, multiple security policies, and difficulty with collaboration. The data is consolidated, cataloged, and offers downstream users a single place to look for all sources of data.

Store Your Data In Any Format 

Build advanced analytics and predictive modeling capabilities

Data lakes eliminate any requirements of data modeling during the data-ingestion. You can store data in data-lakes in any format & medium i.e. RDBMS, NoSQL Databases, File Systems, and Time Series Databases, etc. Data can be loaded in its existing format like a log, CSV, XML, parquet, etc. without any transformation.

Data lakes are cheaper as compared to the traditional data warehouses as it allows you to store data without any pre-defined format or schema.

Since the data is stored in original or raw format, it is not contaminated. Therefore it’s always possible to fine-tune earlier analytics and develop new insights from the same historical data.

Data scientists can access the raw data when they need it using more advanced analytics tools or predictive modeling.

No Predefined Schemas

Maximize your  organization’s data value and security 

With data-lakes, there is no need to have a pre-defined schema. This helps to process the raw data without having any information on the type of analysis that might be required in the future.

Data lake empowers your organization with a cloud-based data intelligence capability that can maximize data value and security while minimizing your data liability.

It provides a low-cost scalable and secure storage solution with advanced analysis capabilities on a variety of data types.

Strong Foundation For ML & AI

Machine learning & AI-powered analytics

By having a centralized data repository in the form of data lakes, multiple data sets can be combined to train and deploy machine learning models to perform predictive analysis and data usage patterns.

Data in the data lake is stored in an open format, therefore It makes it easier for various ML/AI-based analytical services to process this data to generate meaningful insights.

Data lakes can process all data types with a very low latency including unstructured and semi-structured data like images, video, audio, and documents which are very critical for modern machine learning and AI-based use cases.

Handle Modern Use-Cases

Eliminate limitations of  the traditional data warehouse and  innovate more

Traditional Datawarehouse solutions are expensive, proprietary, and have many limitations to handle the modern use cases that most companies are looking to address.

The data lake concept was developed in response to these limitations of the traditional Datawarehouse solutions.

Advanced analytics and machine learning on unstructured data are the key priorities for organizations today. For this, the data lake offers the required massive scalability up to an exabyte scale.

Data lake uses a flat architecture and object storage to store data as compared to the old data warehouses which stores data in files or folders.