The digital universe spans trillions and trillions of gigabytes. However, almost all of this data is unstructured, making its storage a major challenge for the computing world. This is where the Data Lake comes in .
What is a Data Lake ? It’s a storage repository that collects big data from a multitude of sources in a raw, granular format. This allows the data to be fully structured, semi-structured , or even completely unstructured.
This means your data will remain flexible until your next visit. Want to learn more? Then keep reading…
What is the Data Lake for ?
This is the digital age, so technology plays an important role in our daily lives. This leads to exponential growth in information… Information that is extremely valuable and useful in decision-making . The key is: What to do with this volume of information? How can it be managed?
At first, the data you have may not seem useful. However, it’s better to have it because you may need it in the future.
Storing this data is only possible through the Data Lake . This tool allows you to store it indefinitely.
Once the information stored in the Data Lake begins to be analyzed and processed , various actions can be taken, such as discovering new consumption patterns or analyzing user characteristics to develop marketing campaigns.
The Data Lake can even be used in scientific research!
Benefits of a Data Lake
- This tool is very agile, allowing developers and data scientists to more easily configure data models, applications, or queries on the fly.
- Handling large amounts of data can be daunting for the less educated… even though it’s a very practical tool.
- Since there is no inherent structure, anyone can access the data.
- The Data Lake offers support for users with different investment levels. Those who need to go back to the source to retrieve more information, those looking to answer new questions with that data, and those who simply require a daily report. Access is available for each.
- It’s economical to implement. It only requires low-cost hardware , and its administration is possible through open source.
- Resource-intensive schema development and data cleansing are postponed until an organization identifies a clear business need for the data.
- It allows for a wide variety of different analysis methods to interpret data, such as big data, real-time analysis, machine learning, and SQL queries.
Data Lake vs Data Warehouse
Data lakes and data warehouses have similar goals and objectives, which is why people often confuse them. Both are repositories that consolidate data extracted from a previous collection process. The goal of both is to create a single data warehouse that will then be used for practical purposes.
However, there are some fundamental distinctions between a Data Lake and a Data Warehouse that you should be aware of. We’ll review them below:
- Read schema vs. write schema: A data warehouse ‘s schema is defined and structured before storage. Therefore, in a data warehouse, most data preparation typically occurs before processing. In contrast, in a data lake , it happens when the data is used. Another aspect is that it doesn’t have a predefined schema, allowing data to be stored in its original format.
- Complex vs. simple user accessibility: Since data isn’t organized in a simplified form before storage, a data lake often requires an expert. This expert needs in-depth knowledge of the various data types and their relationships to read them. In contrast, a data warehouse is highly accessible to both tech and non-tech users because it has a well-defined and documented schema.
Intelligent Data Lakes
All types of companies know that this tool has enormous potential for many of their areas. From marketing to production and sales…
That’s why there are currently many platforms that offer these services where users have access to data at any time.
However, a latent concern is that sometimes the amount of available information doesn’t match its quality. When this happens, all the benefits of the Data Lake lose some of their impact.
What’s the solution to this challenge? Artificial Intelligence.
The Intelligent Data Lake systematically transforms unprocessed big data into a viable data set. This enables them to quickly and repeatedly convert big data into trusted information assets, which provide sustained business value. No more junk data!
Below we show you some characteristics that define these smart warehouses:
- They discover data through an automated process based on machine learning , which converts correlated data files into intelligent recommendations.
- They effectively analyze the repositories where the data is stored.
- Allows you to quickly prepare and share data.
