A Data Lake (DL) is a centralized repository of raw data in its native or original form, without a defined structure or pre-processing, making it highly flexible.
A DL can receive data in different formats, from spreadsheets, images, and documents to sensor data and server logs, and store them without pre-defined rules. It is not necessary to know when or for what these data will be used.
The DL is especially useful for data scientists, machine learning engineers, and other professionals related to the field of Artificial Intelligence (AI) and Machine Learning (ML). However, it is not so suitable for the area of Business Intelligence (BI) and data analysis. This is because the DL does not provide the same organizational structure and data aggregation needed for the analytical purposes of these areas, such as the creation of reports and visualizations.
Why Does a Company Opt for Having a Data Lake?
The Data Lake was developed to deal with the limitations of the Data Warehouse (DW). Unlike the Data Warehouse, the Data Lake stores all types of data, regardless of their origin or format, which is not allowed by the business model and structure of the Data Warehouse. This data storage in its native form is centralized in the Data Lake, allowing access to various information in an organized manner.
Additionally, the Data Lake is highly scalable, meaning it can be adapted to the company's needs as the number of data increases and the company adds more resources. This is related to the concept of Big Data, which we will discuss later.
The Data Lake is extremely important for data scientists, as the flexibility of the data is essential for the creation of machine learning and artificial intelligence models.
Big Data
The use of Big Data is increasingly common in various areas, from marketing and finance to health. But what is Big Data?
Big Data is a term used to refer to a set of numerous and complex data, collected and processed at high speed. The term describes the massive amount of data generated at every moment in the world, and its analysis and interpretation generate advances and improvements in various sectors.
The data come from various sources and are stored in specialized databases for this need, these databases are the Data Lakes, capable of dealing with the high amount of data and the speed of its arrival.
The analysis of Big Data is commonly done by artificial intelligence technologies, machine learning, and predictive analysis, in addition to NoSQL databases, distributed processing, among others. Within companies, the biggest advantages are in predictions about future events or customer behaviors, in addition to helping to identify market opportunities, optimize internal processes, improve operational efficiency, and reduce costs.
Hadoop
Hadoop is a widely used open-source software tool for creating Data Lakes. Its use is so common among professionals that there is often confusion between the terms. But, in fact, it is very simple: Hadoop is a tool for implementing the Data Lake.
Hadoop consists of two main components: the Hadoop Distributed File System (HDFS) and MapReduce.
HDFS is a distributed file system used by Hadoop to store large data sets in server clusters. It is designed to handle hardware failures and is highly fault-tolerant, ensuring that data is stored and replicated on multiple servers.
MapReduce is a programming model used by Hadoop to process large data sets in parallel in a cluster. MapReduce is highly scalable and can process large volumes of data in parallel in a server cluster, making it a powerful tool for big data processing.
Data Lake vs Data Warehouse
Within a DW (Data Warehouse), data are organized according to a pre-determined system, following rules defined based on the needs of the company and its business rules. Thus, a DW does not accept all types of data, only those already structured. The data stored in a DW go through a process called ETL (Extraction, Transformation, and Load), which means extracting data from various sources, transforming them to fit the rules of the DW, and then loading them into the system. This organization makes the DW a secure environment for BI and data analysts, allowing access to already treated and organized data to obtain useful and strategic information to support decision-makers in the company.
In the Data Lake, the data go through a process of ELT (Extraction, Load, and Transformation), which means that the data are extracted from various sources and loaded into the Data Lake environment in their raw format. The transformation of the data is done later when users access the Data Lake to perform analyses. Its access is more complex and therefore is more used by data scientists, in addition to other reasons already mentioned, such as flexibility and the possibility of accessing data in their original form. In terms of costs, a Data Lake is less costly than a DW.
Data Lakehouse
A DL has some challenges, which include the reliability of the data stored there, slowness in its performance, and difficulties in data governance and security.
Thus, the Data Lakehouse emerged, which is a data architecture that combines functionalities of the Data Lake and the Data Warehouse. The Data Lakehouse can be used by data scientists and data analysts with the same efficiency, offering the simplicity and structure seen in DW with access to the raw data present in a Data Lake.
Some of the popular tools for building a Data Lakehouse include Amazon Web Services (AWS), Microsoft Azure, Databricks, and ETL (or ELT) tools like Kondado.
The choice of the ideal tool should take into account the needs of the company, its financial resources, and its team.