More and more organizations are investing in data lakes for their business intelligence needs. However, approaching a data lake as another version of a data warehouse leads to big issues. What are the differences between a data warehouse vs data lake? Keep reading to find out.
You’ve likely heard the benefits of data lakes: storage cost-effectiveness, no need to manage schemas, and the ability to generate value from flexible data input sources. When used properly, a data lake allows analysts to accelerate business growth with unique data sets simply not attainable with a data warehouse.
However, many businesses that invest in data lakes don’t use them to their full potential. A data lake is not a “data warehouse 2.0,” and using a data lake in the same way as a data warehouse leads to poor performance and lowered business value for your firm.
Let’s talk about some common mistakes organizations make when investing in and implementing data lakes.
Data Warehouse vs Data Lake: Common Mistakes with Data Lakes
1. Using Queries That Are Not Open-Ended Enough
A key advantage of data lakes over data warehouses is their ability to store unstructured data from a variety of disparate sources.
However, this also means it’s typically slower to use a data lake to look for small, specific sets of data. You should avoid this use case when leveraging a data lake—use a data warehouse instead. Many organizations make the mistake of investing in a data lake but continuing to approach their querying strategy as they did with a data warehouse.
Instead, organizations should look to data lakes for large, open-ended queries. The use of a data lake empowers analysts to ask bigger questions than they normally would with a data warehouse.
For example, instead of asking:
“What is the correlation between our marketing spend and total revenue over the last 2 years?”
“Which marketing campaigns in the last 2 years have led to higher social media activity and exposure for our product?”
Data lakes allow businesses to ask “bigger” and more abstract questions to gain more compelling insights.
2. Having a Poor Data Governance Procedure (or Lacking One Entirely)
The underlying structure of your data lake will be the main determinant of whether your data lake is useful or muddled.
Because data warehouses are usually relational and highly structured, organizations are not used to the different upkeep required for a data lake to stay organized and useful for analysis. Your organization should implement a strategic data governance procedure that carefully determines what data is being stored in the data lake, how the data is being stored and structured, and what metadata will be maintained.
When data is simply dumped into a data lake, it makes data difficult to use, track, and find.
“Data swamps” are hugely detrimental to analysts’ ability to gather insights from data. Not only does this decrease the speed and productivity of analysts, but it also makes them less trustful of the information. Poorly managed data could be used inappropriately due to missing context or dismissed due to lack of context. This danger of misusing data lowers confidence from both the users and stakeholders in the results being gathered.
Maintaining metadata greatly helps mitigate these issues. It ensures that analysts can understand the context around data and whether it’s usable or not. Additionally, it helps keep the data lake itself organized and more easily searchable. Defining what makes data usable and creating a proper and well-thought-out data management policy will alleviate all of the aforementioned risks.
3. Lacking a Business Strategy Regarding Your Data Lake
After spending the time and resources to invest in a data lake, you should establish a clear data lake utilization strategy. Data lakes are capable of generating more complex insights than data warehouses, and planning around your data lake allows your organization to use and discover these insights more effectively. This takes effort from multiple functional departments of your business:
- Executives need to ensure that the overall business strategy is responsive to insights gained from the data lake and that there is constantly a plan to extract new business intelligence. This is key to actually gaining business value from the data lake. Your data lake should be used to generate new information, not just basic reports on KPIs.
- Developers own the gathering and maintenance of raw data in the lake. This means developers must make an effort to be mindful of the data lake when designing new applications, as well as re-purpose older applications for compatibility. Developers must oversee that raw data and metrics are being properly gathered and usefully stored by the entire data stack, which includes strategic management that requires cross-functional input from other teams in the organization.
- Analysts are responsible for extracting useful information from the data lake. As mentioned earlier, this entails using ambitious queries and asking the right questions. They must not approach the data lake in the same way they would approach a data warehouse and understand the different use cases that accompany each solution.
An organization’s approach to data as a whole should operate with the strengths of data lakes in mind.
Because of their schema-on-read structure, data lakes are faster and more adaptable than data warehouses. Users can quickly access raw data instead of waiting for it to be transformed. Furthermore, analysts aren’t confined to the meticulously designed nature of the data warehouse and can explore data in imaginative and creative ways.
Encouraging analysts to work in an agile and versatile way will generate more insightful business intelligence at a faster rate compared to a conventional data warehouse.
See how you can optimize your data lake with Kloudio.