In today’s data landscape, Data Lakes are popular tools to tackle the challenges of Big Data, enabling the storage of large and heterogeneous data sets quickly and cost-effectively. They offer flexibility, cost-efficient storage, and the ability to retain various data formats in their native state. However, these benefits also present numerous challenges for data analysis.

Challenges of Data Lakes

Data quality and consistency can be compromised when integrating data from diverse sources and formats. Without stringent data quality control and governance strategies, a Data Lake can quickly turn into a “Data Swamp,” where data is centrally stored but difficult to access and unusable. Managing and ensuring compliance with privacy and security regulations is complex with heterogeneous datasets. Finding relevant data within the jumbled structures of various data models in a Data Lake often proves challenging. Integrating data from different sources requires extensive ETL processes, which are time-consuming and resource-intensive and may still not achieve a unified and normalized data model. Performance issues, especially when analyzing large datasets or meeting real-time requirements, are common. The open nature of Data Lakes can create security gaps that require additional protective measures. Analyzing raw data in its native format demands specialized knowledge and tools. Uncontrolled growth of the Data Lake can lead to rising storage and processing costs. But the biggest issue is that the various data sources distribute the meaning of the data collected in the Data Lake across different processes, applications, and programs. Some of the data's semantics are embedded in the process and program logic, and simply consolidating the data in one place or even in a model where duplicates are uncovered does not alleviate this issue. As a result, understanding the origins of the data is always necessary to use it effectively in analysis or to answer specific questions.

Exemplary Overview of a Data Lake Architecture:

Advantages of Graph Databases

Graph databases offer solutions to some of these challenges. Their ability to efficiently model and query complex data relationships makes them excellent for integrating heterogeneous data and improving data discoverability through targeted queries. Powerful query capabilities enable complex relationship analyses, and the efficient processing and querying of connected data enhance performance. The explicit modeling of relationships not only helps to derive new insights from the data but also to identify and rectify inconsistencies.

The most important characteristic of a semantic graph—or a knowledge graph—is the following abilities of the graph model:

Combining Data Lakes and Graph Databases

Organizations that already have a Data Lake are well-positioned to also populate a semantic graph. Investments made in the Data Lake can be preserved while simultaneously leveraging the enhanced analytical capabilities of a graph.

It should be noted that very large datasets can also be stored and efficiently analyzed in a graph. However, this requires specific extensions of graph databases into a semantic data platform. This offers more than just the basic graph database, which is also available as an open-source solution, and includes features like linear scalability, de-duplication, handling of super nodes, historization for temporal querying within the system context, and much more. Such semantic data platforms are, with very few exceptions (currently estimated to be only two providers globally), typically proprietary developments of leading internet companies like Google, Alibaba, Meta, or Amazon, forming the technological foundation for their data-driven business models.

When to Supplement or Replace a Data Lake with a Graph Database

In future data landscapes, semantic graphs will hold significant advantages over Data Lakes. This is not only supported by their widespread use among companies whose business operations rely on data-driven models but also by the fact that statistical simulations, forecasts, and analyses can be implemented much more easily within a graph. Consequently, it is anticipated that more and more applications will be developed against a graph data structure, leading to a gradual yet steady transition.

It is crucial to reflect the development of new features and the maintenance of existing structures within a solid lifecycle management framework, ensuring that all investments are protected while a technological and conceptual upgrade takes place over time.

Criteria for Using Graph Databases and Data Lakes

The decision between a graph database and a Data Lake should be based on various criteria, including the intensity of data relationships, query complexity, real-time requirements, and the flexibility of data models.

Graph databases often require less data preprocessing compared to traditional data warehouses, reduce latency, improve data quality, and offer greater flexibility. They are particularly well-suited for applications that demand fast, flexible, and high-quality data analysis. Another significant advantage is that data modeling in a graph database is intuitive and often aligns more closely with the real world, especially when it comes to representing relationships.

For companies at the beginning of their digital maturity, a combination of a Data Lake for quick storage of diverse data and a graph database for analysis and evaluation is ideal.

Companies with advanced maturity, aiming to operate data-driven business models, can consolidate and optimize their data storage in the sense of a data operating system within a semantic data platform. This platform is characterized not only by a graph database but also by the ability for linear scalability, efficient handling of super nodes, effective memory management, de-duplication to avoid unnecessary data redundancy, inherent system security, historization for temporal querying within the system context, audit logs with real-time effectiveness of permission changes, and high speed in complex queries or transactions within the graph.

An architecture that meets these requirements is the semantic data platform Bardioc by Almato AG:

Conclusion: The Combination as the Optimal Start to a Solution

Graph databases and Data Lakes each have their own strengths and should be seen not as competing but as complementary technologies. While Data Lakes offer an excellent solution for integrating various relational models and providing tools and processes for doing so, graph databases enable efficient data analysis and real-time queries. The combination of both technologies within a semantic data platform provides organizations with a robust, scalable, and flexible infrastructure that optimally supports both the storage of large data volumes and the efficient analysis of complex data relationships.