What Makes a Smart Data Lake Design for Data Integration?

Accurate business intelligence is going to make it easier for your company and employees to access relevant data that you have gathered from reliable sources and use this information to put your organization one step ahead of your competitors. In order for your business intelligence efforts to be effective, you will need a data lake that has been designed for ease of use and efficiency. In simple terms, data lake design equates to the building process that will offer your company a seamless solution for integrating the data you have collected from multiple sources and then use it for data analysis and reporting.

If your company has a badly designed data lake or data model, then you will likely be privy to incorrect and out-of-date source data, which can affect your organization’s growth and productivity. Ensuring your data lake project is efficient and reliable is going to be crucial to making the most out of the data you have gathered. Always make sure that the following criteria are met when designing your data lake:

Gathering Requirements
This is the primary and most important step of the design process, as it will allow you to pinpoint the necessary criteria you need to implement the data lake successfully. If you want your business to excel and grow, then you need to be looking at your long-term business strategy as well as your current requirements. During this phase, you will also be determining your technical strategy, including how you will back up and recover your data lake in the event of a system failure. Formulating and implementing a disaster recovery plan during the first stages of data lake design is going to set your organization up for success in the face of any challenges or threats to the data you will be storing in the database.

Setting up the Physical Environment
Mastering data lake design isn’t easy but should always feature separate physical environments. This will allow you to individually test each component before proceeding to the production phase, without disrupting the entire environment of the business processes. That means if any data becomes compromised, your IT staff can easily address the issue without shutting off the production environment as a whole.

Data Modeling Phase
Once the first two stages mentioned above have been defined, its time to move on to determining how relevant data structures are going to be accessed, processed and stored within your data lake, ready for analysis when required. Data sources are identified during this phase and it will allow you to know exactly where original data is held, which will be critical to the success of the data lake project and the data quality. Once these requirements have been established, work can commence on building the necessary physical and logical structures.

Keep Future Needs in Mind
Data lake projects require a financial and emotional investment equipped to produce results in the long term, as opposed to the short term. Therefore, you want to make sure you are not basing your design entirely on your current business needs.

An Intricate Metadata Layer
A data lake is not going to perform well if the metadata layer has been poorly designed. This can have major repercussions, as metadata is the key component responsible for integrating the different components of a data lake to ensure it functions seamlessly. If the business intelligence reports cannot be deciphered easily, then this will result in frustration and rejection and the problem can usually be traced to badly designed data models.

Visual Appeal vs. Speed
The reporting layer of a data lake should be designed with a primary focus on speed and ease of use. Never sacrifice speed in order to have fancy charts and colorful reports in place, as a quick response time is crucial to improving productivity and increasing revenue. Reports should always be accessible in a timely manner, regardless of how visually appealing they are.

Prioritize Performance
Both the designers and users of a data lake often overlook data performance in its initial stages. However, this is a crucial element to be taken into account right at the beginning—if your data lake is not performing as well as it should be after going live, it is incredibly difficult to rectify this oversight. Performance objectives are much easier to design at the beginning than they are to finetune later down the line. If you have spent all this time designing and building a data lake, then you want to make sure it is performing to its best ability as a matter of priority.

Final Thoughts

Designing an effective data lake is going to be an incredibly time-consuming project but, if done correctly, it can provide your business with the necessary tools it needs to succeed. Ensuring you provide proper training for all users to familiarize themselves with navigating a data lake is going to be an invaluable investment to your organization, as no one likes change or the unknown. If you spend an adequate amount of time planning and developing all of the requirements needed to make a data lake work smoothly at the very beginning of the project, the implementation of the new system will be much easier. Always ensure you consult professional and knowledgeable contractors to design your data lake, as we already know it is harder to rectify mistakes than it is to avoid them in the first place.

Data lakes are quickly becoming an invaluable tool for the modern business organization. It will ensure data consistency, allow you to make better data-driven business decisions, as well as set you up for success with regards to your future plans.