A data lake is a repository that centralizes and stores a large amount of data of different types from various sources. The stored data can be structured, semi-structured or in its raw form, without any pre-processing or analysis. In a data lake, this data is only processed when it is used. Thus, the data can be reused and used several times for different purposes. This information can be used to feed the company’s systems, generate alerts or be used to build dashboards.
In the data warehouse, data is stored in a structured way following the relational model (structured in columns and tables) and ready to be used. In the data lake, on the other hand, the data does not follow a defined pattern and may or may not have undergone any data analysis or processing process. Most of the time it appears in its raw state. Implementing a data lake is cheaper than implementing a data warehouse, but a study of the company’s needs is needed to determine the best implementation option.
If the company works with a small amount of data that has standardized formats and needs previously structured data, implementing a data lake would be unnecessary and a good option would be a common relational database. A data lake is designed to store large amounts of data of various types. This makes it difficult or impossible for a conventional management system to handle this data. It is therefore necessary to use Big data tools to analyze this data. If your company works with a large amount of data (up to petabytes) from various sources and of different types, implementing a data lake is a good option. Activities where the data lake is an option are activities that generate real-time data, machine learning and companies that work with data analysis.
Improve your structure
Lower implementation costs.
Greater scalability.
Data available at any time.
The data can be accessed simultaneously.
The data can be reused for different applications.
Compatible with various types of data.
A cluster is a configuration of interconnected computers that work together as if they were a single cohesive system. Generally, the computers in a cluster are dedicated to a specific task, such as data processing, storage or running applications. They are interconnected via a high-speed network, allowing fast and efficient communication between the cluster nodes. The importance of clusters lies in their ability to provide high availability, scalable performance and fault tolerance. By distributing tasks across multiple nodes, clusters can handle heavy workloads more effectively than a single system. In addition, clusters offer redundancy, ensuring that if one node fails, the others can continue operating without significant interruption of service. This is especially crucial in critical environments such as data centers, where reliability and availability are essential. In short, clusters play a vital role in modern IT infrastructure, providing reliable, scalable and resilient computing resources for a variety of applications and workloads.
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