“Datapedia” is our monthly column where we illuminate the meanings of terms often misunderstood and confused in the world of Business Intelligence (BI) and Data Visualization.
Many concepts may seem similar, but they have distinct objectives and applications. They are often used inaccurately, leading to confusion even among industry professionals.
In this article, we will explore four pairs of terms such as Data Warehouse vs Data Lake and clearly explain what they are and when to use them.
The text describes four pairs of concepts in the field of data, highlighting their definitions, objectives, use cases, and differences.
Data Warehouse vs Data Lake: storing data
When the need arises to analyze data, it’s crucial to first understand where this data is stored. Data, whether it’s corporate, healthcare, or any other type, is stored within structures to ensure its preservation and availability when needed.
For instance, data can be stored in a Data Warehouse, a digital storage system that connects large amounts of data from various sources. The Data Warehouse thus becomes the single, reliable source of an organization’s data. It can contain structured data (organized in tabular schemas) or semi-structured data (e.g., XML, JSON files), which are standardized and validated for reliability and quality. From this centralized repository, data can be extracted to meet common Business Intelligence needs: conducting analyses, setting up monitoring reports, and creating exploratory graphic visualizations.
However, when data is in an unstructured format (e.g., images, audio, video, emails), it cannot be contained within a rigid and standardized structure like the one just mentioned. In such cases, the capabilities of a Data Lake are utilized. A Data Lake is a centralized repository that can contain data of any source and structure, whether processed or not. The potential for analysis here is greater than that of a Data Warehouse and spans all possible fields, from healthcare to finance. However, this greater flexibility in analysis also brings higher risks related to data quality, as the data is not standardized.
SQL vs NoSQL: languages for data management
Once stored, data must be analyzed to add value to the organization that owns it. For this analysis to proceed, the data must be extracted from the repository where it’s stored. This is where the SQL (Structured Query Language) comes into play. This programming language enables both the storage and processing of data contained in a relational structure, where data is organized in tables with rows and columns representing the data’s attributes (as seen in the case of the Data Warehouse). SQL is easy to write and understand, allowing for the storage, modification, and retrieval of information within the database to facilitate further analysis.
However, when dealing with semi-structured data, as mentioned earlier, SQL is no longer suitable for manipulation, and the use of NoSQL language becomes necessary. NoSQL allows for accessing and managing data without the relational structure required by SQL. It is well-suited for applications in mobile or web environments, as well as in the gaming industry, offering much more flexibility and scalability in analysis. This is why NoSQL becomes the preferred language when dealing with massive volumes of data, such as in the case of Big Data.
Data Migration vs Data Mining: migrating and exploring Data
As mentioned earlier, once data is within a database, it can then be analyzed. However, there are cases where information needs to be moved to another structure, perhaps for legislative reasons (e.g., changes in data protection and processing laws) or logistical reasons (e.g., the need to centralize all data within a new repository). This is referred to as Data Migration. As the name suggests, Data Migration involves transferring data from one storage system to another. The migration can occur from one database to another or to the cloud, but in all cases, it remains a delicate practice where ensuring the proper preservation of information is crucial.
A similar term that can easily be confused but has a completely different meaning is Data Mining. Data Mining refers to the extraction of useful information from data. Data Mining involves identifying trends, patterns, and relationships to support decision-making processes. There are many techniques that can be used, from machine learning algorithms to neural networks, and the applications are just as varied, from marketing to demography. The goal might be, for example, to better understand the market demand for a particular product: these analysis techniques provide the tools to answer such questions.
Prescriptive vs Descriptive Analytics: analyzing data
We could say, then, that Data Mining provides useful tools for what is known as Prescriptive Analytics. This encompasses all practices that utilize hypotheses, trials, models, simulations, mathematical and statistical analysis to refine the decision-making process, indicating what actions to take for optimal decisions. The goal of these analyses is precisely to help decision-makers make correct choices based on data, i.e., according to objective criteria, with a focus on optimization and risk minimization.
On the other hand, Descriptive Analytics aims to describe processes, finding correlations between the past and present without explaining the causes of phenomena and thus unable to make recommendations on what actions to take, unlike the previously introduced analysis. Descriptive Analytics aims to identify how certain key parameters (KPIs: Key Performance Indicators) change over time, thereby identifying historical and current trends through statistical analysis. It is considered the simplest form of Business Intelligence because it seeks to answer the questions “what has happened/what is happening?” while Prescriptive Analytics aims to answer “what is the best action I can take?”
To summarize, we have retraced a good part of the data analysis process, first explaining the difference between Data Warehouse and Data Lake. Both are data repositories: the former has a standardized structure suitable for recurring analyses where data quality and reliability must be certain, while the latter is more flexible and scalable, designed to also contain unstructured data such as audio and video files.
For data to be truly valuable, it must first be extracted for analysis. This is where SQL language comes into play, necessary for inserting, modifying, or extracting information contained within relational databases (i.e., databases consisting of interrelated tables). However, when data is not contained within these structures, the use of NoSQL language becomes essential to ensure the correct use of semi-structured data.
Sometimes data needs to be moved to another repository, and it is in these cases that we talk about Data Migration. A different discussion applies to Data Mining, which encompasses all the techniques for extracting useful information from data.
It is this set of techniques that allows for Prescriptive Analytics, the type of analysis that seeks to answer the question, “How can I act?” This study indeed finds optimal solutions to the question at hand through efficient data use. On the other hand, if we simply need to describe the status quo of the phenomenon under analysis, Descriptive Analytics comes into play: it allows us to observe past and present trends of a phenomenon to study its behavior.
Become an Expert in BI and Data Visualization
If you’re looking to learn the fundamentals of key BI tools or become an expert in Visual Analytics, discover our training programs.
This article was written and edited by one of our consultants.
Share now on your social channels or via email: