ANNOUNCEMENT

Hexaware Positioned as a Visionary in the 2026 Gartner® Magic Quadrant™ for Custom Software Development Services

Gartner, Magic Quadrant for Custom Software Development Services, By Jaideep Thyagarajan, Ryan McKinney, Nathan Davie, 7 October 2026. Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Hexaware.

Data Lake

What is a Data Lake?

A data lake is a centralized repository that allows you to store structured, semi-structured, and unstructured data at any scale. Unlike traditional databases, a data lake retains data in its raw format until needed.

Why Do We Need a Data Lake?

We need a data lake to store diverse data types from multiple sources for advanced analytics, machine learning, and real-time insights. It provides flexible data lake storage that supports scalability and cost-efficiency, especially in cloud environments like AWS data lake solutions.

Data Lake vs. Data Warehouse

Data Warehouse: Optimized for structured data, high-performance queries, and BI reporting.
Data Lake: Handles raw, unstructured, and semi-structured data at scale. Ideal for big data and AI/ML use cases.

Data Lake vs. Data Lakehouse

Data Lake: Focuses on storage of all data types without schema enforcement.
Data Lakehouse: Combines the flexibility of a data lake with the reliability and governance of a data warehouse.

Benefits of Data Lake

A data lake offers scalable, cost-effective data lake storage that supports structured and unstructured data from diverse sources. It enables advanced analytics, machine learning, and real-time insights while integrating easily with modern data lake solutions like AWS data lake. With flexible data lake architecture, it supports big data use cases and empowers organizations to unlock value from raw data efficiently.

Back to glossary