Data Warehouse vs. Data Lake vs. Data Mesh: Key Differences
*With the collaboration of Blanca Mayayo.
Data Warehouse vs. Data Lake vs. Data Mesh. Data architecture specialists are familiar with these three concepts. Data Lake and Data Warehouse refer to different formats of data storage, analysis, and queries, while Data Mesh encompasses a series of concepts related to data management in a decentralized and large-scale manner.
According to a June 2020 Gartner study, 57% of executives responsible for data or analytics had invested in Data Warehouse and 39% used Data Lakes. According to the consultancy, data hubs, Data Lakes and Data Warehouses “are all important areas of investment for data and analytics leaders to support increasingly complex, diverse and distributed data workloads.”
These architectures are helping to democratize the use of data within the enterprise. They also make it possible to manage data more flexibly than in the past. Each of them has its own particularities and advantages over the others. In this post, we take a look at all of them.
What is a Data Warehouse?
A Data Warehouse is a structure created to sort huge amounts of unfiltered data from various sources. In this case, the data is only structured and can be analyzed. This architecture allows several people to access it at the same time with high performance.
Advantages of a Data Warehouse
With the Data Warehouse, data is not only stored, but also structured. This architecture is recommended when large amounts of already processed data are needed for queries. In this case, productivity is higher for certain user groups, such as data analysts, or for integration into analytical applications (e.g. Business Intelligence).
Challenges of a Data Warehouse
The Data Warehouse stands out because it only processes structured data. This prevents unstructured data from being used for Machine Learning applications. On the other hand, since it is mostly proprietary software, it can be difficult to link it with external open source tools, although integration solutions already exist for many systems.
What is a Data Lake?
A Data Lake is a data repository where, in an initial phase, data is stored raw and without a unified schema. In this way, the data is made available for future use. If necessary, added layers in the Data Lake could process the data and convert and translate it into a corporate schema.
Advantages of a Data Lake
Because the data is stored in raw form for use at any time, it makes a Data Lake the ideal structure when it is known that it will be reused in the long term and by different systems and parts of the company. Other advantages of a Data Lake are:
- Speed of use for creating and analyzing new models, which is particularly appreciated by data scientists.
- Low-cost hardware and (in many cases) use of open source technologies.
- Reduced waste of resources, which are mainly consumed when using the data.
A related concept is the Lakehouse, a combination of Data Lake and Data Warehouse that merges the best elements of both architectures. As we have seen, it is difficult to integrate open source tools into a Data Warehouse, so bringing these two philosophies together is ideal to take full advantage of what both offer.
Challenges of a Data Lake
Among the challenges to think about before implementing a Data Lake are the following:
- The complexity of its deployment and management: a growing database, maintenance of what is already stored, possible redundancies…
- The need to update the database in cases such as requests for the right to be forgotten.
- Although it is ideal for storing and managing data, it is necessary to go further to obtain value from it. In this sense, the Data Lake is a complement to the Data Warehouse.
- It is necessary to keep the history of data versions or to be alert to make merges, updates, deletions…
- People with less professional knowledge may have problems analyzing unstructured data.
What is a Data Mesh
The Data Mesh emerged as a new socio-technical and organizational approach to data to respond to the complexity, scale, and growing needs in data management. In this case, Data Mesh systems and equipment are decentralized, interconnected, and managed on a large scale. A data mesh could benefit from Data Lake or Data Warehouse systems if the granular and decentralized nature of data management is respected.
Therefore, a path to the Data Mesh could involve leveraging existing Data Warehouse or Data Lake structures, but changing their purely centralized approach and organizing the teams and capabilities of these technologies into specific parts of the data architecture, so that they can be used in a decentralized manner. In other words, you can build on previous experiences using Data Warehouse and Data Lake.
Advantages of a Data Mesh
The Data Mesh is an ideal structure for distributing data among the different departments of a company. In other words, they go beyond the Data department so that all employees can take advantage of the opportunities of the information collected. In fact, the goal is that the data analysis makes it possible to obtain metrics with which to make corporate decisions: finding new business opportunities, correcting past decisions, etc.
In a LinkedIn article, Oracle’s VP of Products, Jeffrey T. Pollock, explained that Data Mesh is ideal for applications such as application migrations to the cloud; real-time integration between these, IoT and analytics, or the analysis of data flow in motion.
Data Mesh and Sidra Data Platform
If you want to know more about Data Mesh, our colleague Blanca Mayayo gave a talk about this data platform and its linkage with Sidra Data Platform, a data management productivity tool that provides a set of tools and accelerators developed by Plain Concepts to ingest, catalog, and manage data in Azure:
Do you want to know which data architecture is right for your business?
As you have seen, Data Warehouse, Data Lake, and Data Mesh have very different approaches. Now you just have to choose the most suitable approach.
We will help you choose the best data architecture for your business objectives. We look forward to hearing from you.