The global data lake market size is anticipated to reach $59,89-billion by 2030 and is projected to grow at a CAGR of 23,8% from 2024 to 2030, according to a new report by Grand View Research.

The rise of data lake house architectures is a significant trend in the global market. These architectures combine the flexibility and cost-effectiveness of data lakes with the structured governance and performance of data warehouses, offering a unified platform for data storage, processing, and analysis.

Data lake houses aim to provide the best of both worlds, allowing organizations to leverage the strengths of traditional data management approaches while addressing the evolving needs of modern data-driven enterprises. This convergence of data lake and data warehouse technologies simplifies the data management landscape, reduces complexity, and enables organisations to extract maximum value from their data assets.

As the Internet of Things (IoT) and edge computing continue to gain traction, data lake solutions are evolving to integrate and process data from these distributed sources seamlessly.

Data lake platforms are developing capabilities to ingest, process, and analyze data generated at the edge, enabling real-time insights and decision-making closer to the point of data generation.

This trend helps organisations harness the value of IoT data and make more informed decisions, especially in time-sensitive or mission-critical scenarios. By extending the data lake’s reach to the edge, organisations can unlock the full potential of their IoT investments, optimize operational efficiency, and drive innovation through enhanced data-driven decision-making.

On-premises data lake solutions are converging with on-premises analytics and business intelligence (BI) tools, providing a more integrated and comprehensive data management ecosystem.

This integration allows organizations to perform advanced analytics, generate interactive visualisations, and derive insights directly within the on-premises data lake environment, without the need for separate BI platforms.

This trend helps bridge the gap between the data lake and the business users who require actionable insights. By seamlessly integrating data lake capabilities with on-premises analytics and BI, organizations can empower their teams to derive maximum value from their on-premises data assets and make more informed, data-driven decisions.

Further highlights of the data lake market report include:

* Based on type, the solution segment led the market with the largest revenue share of 56.15% in 2023. Solutions that enable advanced analytics and data visualisation are becoming a major selling point for data lake vendors. These tools empower businesses to gain deeper insights from their data and make data-driven decisions.

* Based on deployment, the on-premises segment led the market with the largest revenue share of 45,62% in 2023. Data security remains a top concern for enterprises, especially in regulated industries like finance and healthcare. On-premises data lakes offer greater control over data security and compliance, making them a preferred choice for these sectors.

* Based on vertical, the retail segment led the market with the largest revenue share of 18,65% in 2023. Retail organizations are adopting data lake solutions to integrate customer data from various touchpoints, including in-store, online, mobile, and social media, enabling a comprehensive understanding of consumer behavior and delivering personalised experiences.

* North America dominated the market with the revenue share of 36,32% in 2023. With the growing awareness of data privacy regulations in the North America region, organisations are placing a greater emphasis on data security and compliance in their data lake deployments. Data lake solutions providers are offering features and functionalities that help organizations to meet these requirements.

* Serverless data lake architectures are gaining traction, enabling organizations to focus on their data and analytics needs without the burden of managing underlying infrastructure. This approach can lead to improved cost efficiency and enhanced agility in responding to changing data and processing requirements.