Data warehouse vs. data lake. Lately, there have been many talks about data lakes, with many arguing that it’s the same thing as a data warehouse. But the fact is that the two data solutions are optimized for two entirely different purposes, and our plan today is to draw the distinction and help you make a well-founded decision on how to manage your data best.
Worth noting is that the two data solutions are not a replacement for each other but supplemental technologies with two different use cases and some minor overlaps. In which case, most of the organizations usually keep both.
This article will answer the question about the differences between data warehouse vs. data lake.
A data lake, also known as a cloud data lake or a data lakehouse, stores data in its rawest form, with no hierarchy or organization in the individual pieces of the data. It holds or stores unstructured data without analyzing or processing it.
If you were to think about bottled water, then a data lake is the large water body – and not the cleansed and packaged water in a bottle.
Additionally, a water lake restricts what type of data it accepts and retains and from what data source. It’s known to support all types of data or schema (how data is stored).
A data warehouse tends to be more organized. It does not just store data; it ensures the data is well-organized, archived, and ordered. During the development stages of a data warehouse, a significant amount of effort goes into analyzing the data sources and understanding the business processes involved. Data warehousing is also known to be beneficial to a data scientist rather than a data lake.
Decisions also have to be made concerning what type of data can be included and excluded from the warehouse. That’s to say: a cloud data warehouse will only accept data when its use has been identified.
Data warehouse vs. data lake can be confusing to some people, because they both handle data. What’s the difference anyway? We laid out those differences according to several factors.
Data lakes are not fussy. They accept and retain all types of data – unstructured, raw, semi-structured, and structured data. In short, it accepts data, be it semi-structured data or not, from multiple sources. It’s, therefore, more likely that only a small portion of the data that it accepts will be used.
It’s also known to store all the data it accepts without spitting any of it out. Unprocessed data will find its way to a data lake, the same as relational data or any other data type for that matter.
A data warehouse, on the other hand, only accepts refined or already processed and structured data. Moreover, it only accepts data necessary for an identified use or for answering a specific business question or a specific purpose.
A data lake has no structure. It’s as such, relatively easier to make necessary calibrations to its models and questions.
They’re also more flexible and can easily be configured depending on the job at hand.
On the other hand, a traditional data warehouse is much more cumbersome to configure, given the many business processes, it’s involved with. Data analysis would be a task to do in the data warehouse.
Data scientists are the primary users of data lakes, and that’s because their skill set allows them to analyze data deeply. Technically, data lakes are a preferred choice to many, since it supports almost all users.
However, data warehouses tend to be very specific. Therefore, they’re suited for specific business users, especially those that want to extract a specified meaning as defined in the developmental stage of the warehouse.
They’re also known for being too restrictive, making them less conducive to scientists who want to push past the boundaries and glean more data for their analysis.
Data warehouses have more mature data security. However, storing all data in one repository makes their data more vulnerable.
On the upside, it makes compliance and auditing easier to manage when there’s just one store.
Data warehouses and data lakes are two entirely different tools serving two different purposes. In most cases, if you’ve already established a data warehouse, you might also want to implement a data lake on the side to solve the constraints you experience in a data warehouse.
To determine which between a data warehouse and a data lake best fits your needs, you have to start by writing down your goals. This should help you figure out which data repository works best for your goals.
Will one of these data technologies drive the other into extinction?
We rule out the possibility.
Here’s our take on everything: As the amount and value of unstructured data continues to rise, data lakes will increasingly become popular. However, data warehouses will continue to hold an important place in the datasphere and data science in general. A data analyst and a data engineer would still favor a data warehouse.
Organizations will always keep structured data, despite moving their raw and unstructured data to the cloud, where it’s relatively easier to move it around when needed.
You may have heard some rumblings about your organization setting up a data lake and that they might be thinking of migrating your data warehouse to a data lake.
You need to understand that while both a data lake and data warehouse are storage facilities, a data lake is not version 2.0 of a data warehouse. Nor is it here to replace a data warehouse. Anyway, these two are still connected to business intelligence, and you still need a good BI tool that will do data analytics for you, among many other tasks.
Whether it is a data warehouse or a data lake, you need something that can help you easily generate reports and dashboards for your users. Data warehouse vs. data lake, DashboardFox can help you with this.
Out of the box, DashboardFox allows you to connect directly to either a data warehouse or data lake. However, with DashboardFox, you do not have to combine data or data warehouse for the sake of reporting, thanks to its real-time live query that it does against each data source and data blending capabilities. You can immediately get the data needed from where it is stored.
What’s more, DashboardFox can give you the power to limit who can access what kind of data for security and safety purposes, regardless of the location of the data. DashboardFox can also allow you to choose what roles they can take, whether a viewer or a composer, along with the level of security they must have.
And best of all, instead of getting locked into a subscription-based reporting tool which requires a lot of effort and paid services to implement, DashboardFox provides a one-time cost model and is designed with ease of use and low cost of operations in mind.