Implementing a well-designed data lake architecture is essential for effectively managing the growing need to handle large volumes of data. This approach allows businesses to collect, store, and analyse diverse data types within a single platform. However, to fully leverage the benefits of a data lake, it is crucial to design and manage the architecture efficiently.
Thanks to its hands-on experience, Nimbus Intelligence knows the ins and outs of this process and is well aware of the pitfalls to avoid, taking into account resource utilisation, costs, and IT security.
What is meant by data lake architecture
The term data lake architecture refers to a model for managing and storing large volumes of data. In other words, a data lake is a centralised repository for storing structured, semi-structured and unstructured data in its raw and native format. This means that, in addition to tabular data, you can also store images, videos, JSON files, and more.
Key features of a data lake include scalability, which allows for the efficient management of large amounts of data, and flexibility, as it supports various data types and formats. Additionally, a data lake provides direct access to data for real-time or batch analysis.
A data lake’s main components include storage, data ingestion tools (i.e., data collection and import), and a cataloguing system that uses metadata to describe and organise the data.
The 7 Tips for setting up a Data Lake Architecture properly
Implementing a data lake architecture effectively makes a significant difference. It enables quicker data retrieval, ensures that incoming data is clean and functional, and enhances the organisation’s data-driven activities.
1. Set clear goals
The first step in properly setting up a data lake architecture is to understand the objectives: Will it be used as a backup, with data processed through a data warehouse? Or will data analysis be performed directly within the data lake architecture? Additionally, what types of analyses will be conducted?
These are essential questions that will influence the configuration.
2. Choosing the most suitable platform
Another crucial piece is a platform that supports the data lake architecture. Nowadays, every cloud provider offers a dedicated platform for this purpose.
Snowflake is the platform chosen by Nimbus Intelligence. It is a versatile solution: you can have a data lake and warehouse on a single platform. There are no silos, and the platform centralises all data in one location while leveraging major cloud service providers.
3. Organising data efficiently
Data lakes can be seen as large empty boxes. If data are entered disorganizedly, retrieving and using them for the company’s operations becomes much more complicated. Conversely, if the data are organised—such as into folders and subfolders—the operations will be more streamlined, and the activities will be more orderly and, therefore, more effective.
A typical example, however trivial it may seem, is a ‘year > month > day’ hierarchy. This hierarchy allows for quick data retrieval, making querying the data lake easier.
4. Data quality first
Similarly, data quality is essential for properly setting up a data lake architecture. This means the organisation must ensure that the information is accurate. To this end, it can be helpful to implement cleansing processes to transform data into valuable information.
5. Security should not be underestimated.
Another essential point concerns security. A data lake contains precious information for the company and its clients. Therefore, it is crucial to set up a comprehensive access system to ensure that only authorised personnel can access the most sensitive data and no one else.
Limiting access reduces vulnerabilities and potential data breaches.
6. Automation and monitoring to save costs and resources
Furthermore, organisations should implement systems that automate the most frequent operations, such as data ingestion and data cleaning. These are repetitive tasks, and automating them allows staff to have more time for analysis.
Monitoring, on the other hand, allows the company to check the functioning of the data lake architecture regularly and even in real-time. This includes the costs and the amount of information stored in the data lake and the data catalogue.
Not monitoring data lake usage and costs is a grave mistake and could negatively impact the implementation of a data lake architecture.
7. Training the staff
Training the staff is crucial for implementing a data lake architecture. Beyond hands-on experience, staff need a solid understanding of data ingestion techniques, best practices in data management, and fostering a data-driven culture to interact effectively with the data lake.
Data lake architecture: “hot” or “cold”
A further aspect of a data lake architecture concerns its utilisation. The company needs to consider which data will flow into it, how often it will be used and how
From this point, two approaches can be distinguished:
- hot: Data access is daily, and movement is widespread;
- cold: Data access is less frequent, and the data are old. They need to be stored but will not be used, for example, for reports.
The difference between the two cases also impacts the cost of the service.
Data lake architecture: better not to make mistakes
Therefore, properly setting up a data lake architecture ensures that organisations achieve the expected results during selection.
From platform setup to security, data quality, and cost monitoring, Nimbus Intelligence’s advice helps businesses manage data-driven scenarios effectively and stay competitive with a scalable and versatile tool.
