If you are connected to the internet right now, you are producing data. This data can even seem harmless when we consider just one user performing an online action.
However, when the entire world and its billions of inhabitants are considered, the volume of data generated all the time gains a very large scale.
This large amount of data has been generated since the development of Industry 4.0. By users of social networks, browsing websites on the internet, or even machines in industries that are connected to the network through the Internet of Things (IoT).
And the term used to define this volume of data is Big Data. Big Data is a large complex of data that is not managed by traditional software.
Thus, it is necessary to have a suitable place for the storage and management of these data. With cloud computing technology the possibilities have expanded and it is possible to find good options for managing it.
This is how data is used strategically by companies to solve problems and reach more assertive conclusions than in the past.
Continue reading the text to better understand the possibilities of data storage, and decide which is the best option for your company or business reality.
Structured data vs. Unstructured data
In the Big Data world, there are 2 ways to classify data: structured and unstructured.
As the name already determines, structured data has well-defined structures, which are determined even before the data exists and is placed inside that structure.
So, if the data does not meet the requirements of the structure that was created, it will not be loaded. An example is the case of an Excel table that has a specific column for numbers. In this column, text information will not be loaded, for example.
Unlike the previous one, there is no well-defined and standardized structure. These data can contain several elements and will be accepted in the structure.
A landscape photo, for example, which has many unique and different pixels, is considered unstructured data.
The truth is that the vast majority of data that exist in the world is unstructured, after all, its existence is based on the use of certain applications and software.
Examples of unstructured data are text messages, audio via Whatsapp, photos, videos, among others.
And how to store all this? Is there any difference between storing structured and unstructured data?
Data Lake and Data Warehouse: How Data Storage Works
As the name itself determines, we are talking about warehouses, which are databases containing mainly structured data.
A Data Warehouse is usually used to store important company information, which is used in important decisions.
Since there is an organization and structure, the data that is available in a data warehouse results in highly accurate analyses.
Consequently, data warehouses are able to add value to the company, in addition to allowing an optimization of the data stored there.
Just like a lake, a Data Lake has a large reservoir of data, which can be filtered to fill smaller reservoirs.
Unlike what happens in a warehouse, the lake is a database that accepts structured and unstructured data.
Its importance is in enabling the storage of data on a large scale, and the ones coming from different sources and having different formats.
By using a data lake to store all this data, there is a facility for process automation in companies.
This is because as the data is not pre-defined, it is possible to have customization made for projects from different areas of the business.
Data Warehouse and Data Lake Differences
Types of Stored Data
The Data Warehouse is a central repository that contains the company’s most important information. This is structured and easy to use when making a decision. Some examples of areas that use this storage option are HR, finance, and sales.
On the other hand, Data Lake stores data of various types, such as files, images, sensor data, among others. These can be filtered to be used in different areas of the company whenever necessary.
When a Data Warehouse is created, you need to define how the data will be stored in this location before the data arrives for storage. At that moment, the tables, columns, and types of data to be stored are defined.
Data Lake is the opposite of this, as the storage of any type of data is done without any structure. You will need to determine a structure only at the time the data is used.
A data lake is a very large and inexpensive type of repository as there is no pre-prepared structure. Thus, it can be considered a flexible way to store data.
However, a data warehouse is more expensive and requires pre-structuring. You have to prepare, transform and structure a lot of data in one place.
Users of a data warehouse comprise business analysts and stakeholders. A data lake is mostly used by professionals such as engineers and data scientists.
But after all, what is the best option to choose?
As you noticed during the text, there are several characteristics for a Data Warehouse and a Data Lake. But the truth is that to decide which one to use in a company, it is necessary to consider the situation and objective of the company when using one of these databases.
Furthermore, it is possible to use the tools in a complementary way. If the company has big data projects but also needs quick access to data for analytical assessments, using both storage options is a good idea.
Baring in mind that Data Warehouses are used to store the company’s most important information, and require greater investment. That happens because you have to organize the data warehouse structures before the data is sent there.
On the other hand, for cheaper storage of information from different sources and formats, Data Lake is more suitable. In this case, the data will only be organized at the time they are used.
So, before deciding which type of storage best suits your business, consider these points above. This way, it will be possible to choose the best option and make the most of the advantages of each one.