In recent years, “Data Lake” and, more recently, the more advanced “Data Lakehous” have established themselves, which have progressively assumed a central role in the optimization of data-based business strategies and this is due to the fact that storage and data querying are extremely important capabilities for companies that today manage large amounts of data and rely on it to make important business decisions.
In an increasingly data-driven society, in fact, infrastructures capable of enabling the use of advanced technologies, such as intelligence, are needed through increasingly sophisticated tools to store and extract value from big data.
In this article we will therefore describe the difference between these architectures, their implementation and the benefits they can bring to organizations.
Data Lake and Data Lakehouse in Data Management
Data Lakes have been the foundation of many corporate data management architectures in recent years, essentially allowing companies to store vast amounts of data in a raw format, preserving their original structure. This flexibility is a significant advantage, as it enables companies to store various types of data, both structured and unstructured, without the need to transform or model them in advance.
However, with the exponential increase in data and the need to analyze it more quickly and efficiently, the concept of Data Lake alone has proven insufficient. Companies have, therefore, started to seek more advanced solutions to optimize access and analysis of these vast data reserves.
In the last years, Data Lakehouse has emerged as a response to the need for greater speed and efficiency in data analysis. In practice, a Data Lakehouse merges the concept of Data Lake with that of a data warehouse, integrating the advantages of both. It retains the flexibility of the Data Lake in handling raw data but also introduces the ability to perform fast queries and advanced analytics, typical of a data warehouse.
This hybrid approach has proven particularly effective in addressing the challenges of large-scale data management. The introduction of organized data structures, such as tables and schemas, allows companies to perform more complex analyses without losing the versatility typical of traditional Data Lakes.
Key Advantages of the Data Lakehouse
The advantages of Data Lakehouses are::
- Speed in data analysis: thanks to the integration of typical data warehouse features, the Data Lakehouse enables the execution of complex analyses in real-time, speeding up the decision-making process.
- Versatility and scalability: The flexibility of Data Lakehouses allows companies to easily scale their infrastructure in response to changes in data size and complexity.
- Greater data control: introducing organized data structures, the Data Lakehouse provides greater control over the quality and integrity of data, reducing the risk of errors in analyses.
- Integration of structured and unstructured data: a Data Lakehouse allows companies to work with different types of data, both structured and unstructured, providing a more comprehensive view of business information.
- Cost efficiency: Data Lakehouses are built with modern infrastructure that separates computation and storage, allowing for easy addition of storage without increasing computing power;
- Support for a wider variety of workloads: Data Lakehouses provide direct access to some of the most widely used business intelligence tools to enable advanced analysis.
The platform developed by Revelis that enables the analysis and monitoring of Big Data, forecasting phenomena, and explaining decision-making models.
Rialto™ can be used to populate data lakehouses starting from the integration of heterogeneous information sources
Practical Implementation of the Data Lakehouse
To successfully implement a Data Lakehouse, companies must adopt a methodological approach involving architecture design, data governance, and advanced security practices. It is essential to integrate artificial intelligence and machine learning tools to further optimize data analysis and obtain more accurate insights.
The mail activities of this methodology are the following:
- Architecture Design: It is essential to define a structure that can handle both raw and structured data, ensuring scalability to manage the increase in data size over time.
- Data Governance: Crucial for ensuring the quality, security, and integrity of information within the Data Lakehouse. Companies should clearly define access policies, security procedures, and metadata management processes.
- Advanced Security: A top priority, especially considering the increasing complexity of cyber threats. Companies must implement advanced security protocols, including end-to-end encryption, granular access controls, and continuous activity monitoring.
- Integration of AI and Machine Learning Tools: Essential to get the most value from the Data Lakehouse. These tools can automate data analysis, identify hidden patterns, and provide predictive insights.
- Training and Staff Engagement: Essential to ensure that users are proficient in using Data Lakehouse tools and understand data governance policies. Actively involving employees in the Data Lakehouse adoption process contributes to creating a data-centric corporate culture.
Conclusion
In conclusion, multidimensional analysis of large data sets has become crucial for business success in the digital era. While Data Lakes provided a fundamental first step in managing this data, the transition to Data Lakehouses represents a significant advancement in optimizing data-driven strategies.
With their ability to combine the flexibility of Data Lakes with the analytical power of data warehouses, Data Lakehouses emerge as the ideal solution for companies seeking to maximize the benefits from their vast data treasures.
However, the challenge in this context and for achieving these objectives is to have specific skills, either through a comprehensive in-house IT department or an external partner capable of offering support. This is crucial not only for managing all different types of data files ending up in a data lake and supporting various query tools but also for creating queryable datasets, in the form of tables, from raw files.
Revelis is a reliable partner for the proper implementation and use of cutting-edge technologies, enabling your company to successfully address the challenges of large-scale data management, positioning itself competitively in an increasingly data-oriented business landscape.
Investing in a Data Lakehouse is not just a step forward; it is a fundamental strategy for anyone looking to drive innovation and growth through intelligent data analysis. Relying on a competent and reliable partner is the best way to achieve the best results with maximum security.
Make your company innovative and maximize your data potential – Contact us to discover why Revelis can be the best partner for your business.
