A practical view of Big Data: it’s definition, applications and tools

Introduction

In today’s data-driven world, businesses are drowning in information. How much of it is truly valuable? Big data presents both opportunities and challenges. I’m Rodrigo Pasquale, Data Engineer at Customertimes, and today, I want to discuss how companies can navigate this complex landscape.

First, let’s define big data. The term refers to data characterized by three Vs: variety, volume, and velocity (which we’ll explain below). It encompasses larger and more complex data sets generated from new sources such as IoT devices, smartphones, and social media. With this data, the previously unsolvable business problems can now be addressed, given the right architecture.

Before we delve into the architecture, let’s define the Vs of Big Data mentioned earlier:

• Variety: Refers to the different types of data available, such as texts, numbers, videos, images, etc;
• Volume: The amount of data that is generated.
• Velocity: The speed at which new data is generated.

These three Vs can be expanded into four more other Vs:

• Variability: The inconsistency of data.
• Veracity: The quality of data, assessing how reliable and significant it is.
• Visualization: The ability to visualize data, typically through dashboards.
• Value: The ability to turn data into meaningful insights and value.

Applications

Let’s see how this definition translates into use cases across diverse industries where I have working experience:

Financial Industry

• Fraud detection: AI can be employed to identify fraudulent activities and transactions.
• Risk management: It assists in assessing and managing financial risks.
• Recommendation of financial products: Machine learning algorithms recommend suitable investment options or financial services.

Retail Industry

• Demand forecasting: AI models forecast demand for specific products, aiding inventory management.
• Customer segmentation: AI segments customers for targeted marketing by analyzing buying patterns.
• Product recommendation: AI suggests relevant products to customers based on their preferences.

Media and Entertainment Industry

• Content recommendation: Recommendation algorithms can suggest content to users based on their consumption..
• Personalized advertising: Targeted ads can be delivered based on user behavior.
• Sentiment analysis: AI gauges audience reactions to content on social media platforms.

Big Data lifecycle

Now that we have established what big data is and explored some of its use cases let’s talk about the lifecycle of Big Data software. Essentially, this lifecycle represents the stages through which data travels from its raw form to business insights. This process is illustrated in Figure 1.

Figure 1: a possible representation of the Big Data lifecycle.

The first step in the data lifecycle is Data Acquisition or Ingestion, where vast amounts of data are collected from diverse sources. These sources can include internal data, such as customer transactions and sensor data, as well as external data from public sources, social media feeds, and market reports.

Once this data is collected, it needs to be stored. When we talk about Big Data, we need to ensure the storage solution is robust — traditional data warehouses may struggle with the sheer volume. The solution for this problem is using data lakes, which are large, centralized repositories that store data in their native format. These also tend to be more cost-effective, scalable, and secure. Common cloud-native data lakes are AWS S3, GCP Cloud Storage, and Azure Blob Storage.

Raw data is usually not ready for analysis. To solve this issue, we have to process the data by cleaning, transforming, and organizing it, following, as always, our business needs. This processing can be done with a framework such as Apache Spark to handle massive datasets and/or dbt in conjunction with distributed data warehouses. Some cloud-native data processing tools are AWS Glue, GCP Dataproc, and Azure Data Factory.

After the data is transformed, it can be used for analysis and visualization. Analysis aims to extract meaningful insights, identify trends, and uncover hidden patterns within the data, while visualization presents complex findings in a clear and easy-to-understand format. Some techniques that can be employed to analysis are machine learning algorithms, statistical analysis and data mining. For data visualization, we can use market tools such as Microsoft Power BI, AWS Quicksight, Tableau, and GCP Looker.

The key takeaway here is that the big data lifecycle is not a linear process. Insights gained at each stage may require revisiting previous steps, refining data collection methods, and improving data quality. When dealing with this kind of data, an iterative approach should be taken.

Modern Data Stack and Big Data

As we’ve seen, there is huge potential for big data across various industries. For big data to have a solid lifecycle, it needs the right tools, which is where the modern data stack (MDS) comes into play. The MDS is a collection of integrated tools designed to efficiently handle data throughout its lifecycle. The key advantages of using the MDS include:

• scalability to accommodate growing data volumes;
• flexibility to integrate with various data sources;
• security to protect sensitive information, ensure compliance with privacy regulations and protect against unauthorized access.

Ideally, the modern data stack should be open source and agnostic, enabling integration with various tools used across different industries. This flexibility allows for customization to meet specific client needs. Figure 2 showcases some tools that can be used in the modern data stack.

Figure 2: some tools of the modern data stack.

Final considerations

When working with data, we learn that there is no silver bullet for real-world problems. This is true for both tools and solutions. Data Engineers should focus on solving business problems with the client at the core of their efforts. That’s the approach we apply at Customertimes.

Big Data is a powerful tool, but like any tool, it must be used in the right circumstances. Businesses and their technical staff should consider the context, goals, and available resources before diving into Big Data. If the data you’re working with doesn’t check the first three Vs, it’s probably “small data,” which is not a problem! “Small” data is still incredibly valuable, as it’s easier and faster to work with and can be used in the right scenarios.

Remember: the size of the data isn’t everything. What matters most is choosing the right data for the specific problem you’re trying to solve — that’s the job of a Data Engineer!