Data has become the most valuable asset for organizations. This makes its management and protection key. In this scenario, cloud storage experiences remarkable growth that enables companies to access files and documents from almost anywhere at any time.

However, there are several different types of solutions data storages based on the cloud, and choosing the right one is essential to ensure your security.

There are three main types of cloud-based data repositories: ‘data lake’, data warehouse and ‘data mart’. Each one has its own strengths and weaknesses, so it is vitally important to choose a solution that fits the specific needs of each business.

‘Data Lake’, a sea of ​​data

By data lake it is generally understood as the most basic type of cloud storage available. This type of repository allows you to store massive amounts of unstructured and unprocessed data. Like water flowing from a river to a lake, data flows from one or more sources into the data lake, which can contain much other data within its depths. The data may not be ordered, but it may all be contained within the lake.

According to IBM, ‘data lakes’ are useful for organizations that need to have and store massive amounts of information from multiple sources. They are currently used to house large amounts of raw data that is used to train machine learning models like ChatGPT.

Sectors such as energy make use of these ‘data lakes’ to analyze large amounts of data and thus optimize energy production; while the health one analyzes the data of patients and medications to predict the costs and diagnoses of medical care.

Organizations store data in data lakes if they haven’t decided how best to use it and need a place to keep it

Organizations also store data in these ‘lakes’ if they haven’t decided how best to use it and need a place to keep it. Given the difficulty of running analysis on this data stored in a ‘data lake’, it will be key to choose the best provider to be able to move information seamlessly between the repository and a dedicated analysis platform.

Data warehouses for better decision making

Unlike the ‘data lakes’data warehouses, or ‘data warehouse’, are specifically designed to generate reports and analyze structured data.

This is achieved through a process called ETL (Extract, Transform and Load), which involves extract, transform and load. That is, the data is first extracted from its original source and then automatically transformed to fit the parameters of the data warehouse.

This requires cleaning the data, combining data from different sources, and converting it to standardized formats. Finally, that data is uploaded to the warehouse and organized in its assigned location.

Data warehouses have a wide variety of business use cases across all industries that rely on data-driven decisions. For example, retail stores use data warehouses to store and analyze sales, inventory, and customer data. Through this analysis, stores can make better decisions about item pricing and inventory management.

Other companies that use data warehouses are financial institutions, which store and analyze customer data and financial transactions to identify patterns that can help establish better risk management strategies.

The specificity of ‘data marts’

Technically, a ‘data mart’ is actually contained within a larger data warehouse and is meant to serve very specific business functions. While a warehouse or ‘data lake’ normally contains all of a company’s data, a ‘data mart’ contains only the relevant data for your specific role.

Companies using data marts are generally looking to analyze a highly focused data set in a short period of time. According to IBM, they are often used by marketing departments at larger companies to track and analyze data related to campaign performance, including conversion rates and ROI, to better understand what can be improved for future campaigns.

The ‘data marts’, in addition to being faster and more focused on specific data, tend to be less expensive to maintain

In addition to being faster and more focused on specific data, data marts also tend to be less expensive to maintain, mostly due to their small footprint compared to other options. In addition, they are also more secure since access can be restricted to only the people in the company who work with that specific data.

‘Data harbor’, or data port

Along with these options seen so far, there are also several alternative storage types that offer similar services or improve storage capabilities. data repositories existing.

the signature calamu bills itself as the first provider of a new type of storage solution called a ‘data harbour’. According to its founder and CEO, Paul Lewis, it works as an additional layer of security to protect your most sensitive information.

Data stored on this ‘data port’ is fragmented into multiple parts, scattered across multiple repositories, and then re-encrypted. If an unauthorized intruder tries to access them, they are left with just a collection of meaningless numbers.

This way, if someone unauthorized accesses a repository, they will only get a piece of data that doesn’t make sense on its own. However, when it comes to authorized access, Calamu can put those pieces back together to make sense.

initial image | dallas reedy