The strengths and uses of a Data Lake

The Data Lake, is a location for the storage of raw big data, which is analyzed and organized only afterwards. It allows you to store data without worrying about storage capacity. It offers great flexibility and a significant economy. Data Lakes can be set up on-site or in the cloud.


What is a Data Lake?

A Data Lake is a location where Big Data of all kinds is stored and analyzed. It can store both structured and unstructured data. The collected data can be stored for future use. They are therefore stored raw and in bulk, unlike traditional Warehouses. The Data Lake was created by Pentaho’s Chief Technology Officer, James Dixon.

The Data Lake configuration takes place on a standard cluster server. It allows data to be stored without being held back by the location's storage capacity. Clusters can be used on-site or in the Cloud. Incoming flows appear on the Data Lake without having undergone a strict pattern. The information collected is then easily processed and transformed.

A Data Lake relies primarily on a file management system, shared on a network. It takes advantage of the resources of the destination database. Raw data of any type is then analyzed to identify areas that require more attention.

The strengths of a Data Lake

Big Data are usually too heavy and complex for traditional locations. Businesses are generating more and more data. One of the main strengths of a Data Lake is that it can quickly load all the data and then make it available for use. Its flexibility allows you to organize the different data yourself according to your needs. Real-time data allows applications to interact directly with them.

This flexibility saves a lot of time, since it is the definition of a scheme that usually slows down the process, because data of any type is immediately stored. Data re-organization has to be defined later if necessary. This gives analysts time to review and analyze the data more easily. They have the freedom to access the data they are looking for according to the different use cases.


Easier organisation

Even if data is unstructured at first, it is easy to organize it afterwards. It can be catalogued quickly to obtain metadata. Since there was no initially imposed pattern, the original data is kept and organised according to your objectives. Information is centralized and it becomes possible to replace older data infrastructures. This accelerates innovation.

The advantage is enormous provided you have a good command of the technological tools that make it possible to make good use of information. Indeed, the fact that the data is collected in its raw state implies that it must be organized and exploited by an expert who will understand and identify the links to be made between elements of information.

Computing power

Another important advantage of a Data Lake is that computing power is directly associated with storage. It becomes easier to process data and adapt it for specific applications and actions.  If you need more storage capacity, the Data Lake cluster adapts to changes. Experts can also use Data Lakes to build effective predictive models that can be applied to inflows.

Indeed, with the increasing number of metadata, it is no longer possible to continuously migrate information to free up space. That's why it's necessary for companies to integrate their data into a new system without worrying about storage. Systems like Hadoop and the Cloud are built to be linked to Data Lakes.

The uses of a Data Lake

Because it collects raw data of any type, Data Lakes have multiple uses for the business. It is particularly useful in the area of customer relations because you can collect data from your interactions with them. Based on the data collected, it becomes possible to apply predictive models or algorithms in order to anticipate future consumer reactions or solve problems..

It is still difficult to imagine all the possible applications of Data Lakes given the flexibility of the system and the ability to link multiple data. You can take advantage of different elements such as manufacturing environments and the use that is made of products.

Data Lake and Cloud

Many experts believe that the cloud is the best foundation for building a Data Lake. It is easier to adapt to demand, no matter how many resources accumulate. The interest is of course economic above all. It requires less storage, and assessment of requirements is not a concern.

With Google Cloud Platform or Microsoft Azure, you have access to a multitude of ways to develop applications. The cloud makes it possible to optimise the operation of Data Lakes.

Of course, the cloud raises some security concerns. Many believe it is risky to store data there, even though the security of data lake in Cloud has improved. And setting up Data Lakes in the Cloud is faster than on-site. It's also simpler. With a billing model, the system can be deployed at a low cost. And when the needs increase, the Cloud allows you to adapt to speed of growth.

Data Lake on-site

The on-site Data Lake is often adopted for the security it guarantees.  However, the infrastructure to be created requires a lot of space, which is more complicated and more expensive on site. The installation and configuration are also more complicated in this case. It can take weeks or even months to set up, even if the long-term benefits are worth the time you spend on them.

There is also the issue of increasing storage requirements. Again, increasing storage takes time. In addition, this increase in on-site storage requires managers' approval, which means even more time to wait. It is important to properly estimate hardware requirements before implementation. This assessment is difficult as changing needs can be unpredictable.

Whether it is the Cloud or on site, both scenarios are justified. Many companies opt for combinations of the various options. It is possible to bet everything on-site, just as you can use multiple Clouds. It is also possible to mix Cloud and on-site.

In conclusion, a common solution to setup a Data Lake is Hadoop. It is a free framework that helps create applications related to data storage and processing. It allows you to build a large Data Lake. It also absorbs a large volume of data and facilitates their processing.

Finally, if you too want to make the most of your data, go through a specialist like Ryax, who will take care of everything.

The Ryax Team.