Are you looking for data solutions but don’t know whether to go for data lake or warehouse. After all, selecting the right data storage, like kDrive, is the first thing you should do. Both basically have the same purpose but they couldn’t be more different than each other.

However, when it comes to providing big data solutions the two can work together perfectly in synergy, specially if a data protection service like venyu.com/cloud/ becomes a part of it.

Let’s dig into the details. But first take out a few minutes from your precious time and go through these well written and fun articles about goals of software development team and IoT for smart cities. 

Data Lake Vs. Data Warehouse

Remember how we said the two do the same thing but are different. In this section we are going to discuss their differences. 

State of Data

Data lake is used for storing all kinds of data, which includes structured, unstructured and semi-structured data. Just like how lake receives water from all kinds of tributaries. Contrarily, data warehouse is mostly used for storing structured data. 

Data Storing Approach

In the data warehouse since the data needs to be first transformed before being stored, it stores data according to the schema-on-write approach. The data must be in unified form in order to be suitable for storage in warehouse. 

In data lake, the schema-on-read approach is taken to store the data. Raw data is uploaded to the data lake so it doesn’t require much effort.

Architecture

Data lake has a flexible structure and mainly consists of three elements, which are: staging zone, landing zone and analytics sandbox.

While data warehouse has more of a rigid infrastructure with very structured elements. They are obligatory as well as they are used for business processes.

Cost of Storage

It is true that big data services are expensive. In order to load data on a big data warehouse, you must be ready  put in effort and time since the data needs to be in a specific structure. Not to mention that the process itself is costly. On the other hand, storing data in data lake is not hard at all and less expensive. 

That’s why it is best to integrate a data lake with the architecture of data warehouse as a less expensive alternative. 

Security

Storage of big data always has its challenges. No matter what you go for always pay close attention to the access control. Make sure that access to user is based upon their role. This measure will not allow leakage of sensitive data.

In this regard data warehouse is safe compared to data lake since it stores all kinds of data. But since only limited number of users are allowed to access the data it is as a whole well-protected.

The Big Synergy

To the businessmen and data analysts who are not sure about whether to go with data warehouse or lake. Let us say this. Data lake alone is not enough to provide you with a big data analytic solution. Combining warehouse with lake will be a good option instead. 

Big businesses that need to store large amounts of raw data in order to carry out experiments in order to come up with intelligence for decision makers will benefit from this synergy. The elements of data lake and warehouse will work in sync and allow you to enjoy the benefits of both storages.

Conclusion

The jest of the whole article is as is stated in the title, you don’t necessarily have to choose between data lake and data warehouse. Both can work perfectly well in synergy. But first find out the following:

• Your purpose for data storage
• Data quality
• Speed for data flow and the requirement for experiments with data
• Users of the data

Find the answers to the questions above and you may just be able to find the direction in which to go. Power BI Training can also be beneficial for companies looking to launch, organize, and/or grow their business intelligence efforts.