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Understanding and using big data correctly

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June 1st, 2026, reading time: 7 minutes

What is big data?

Big data refers to massive, complex and rapidly changing volumes of data that can no longer be efficiently processed, stored or analyzed using traditional data processing methods. However, with the help of modern cloud infrastructures, artificial intelligence (AI) and machine learning, these data sets can be decoded in order to make informed business decisions, optimize processes and establish innovative business models.

The 5 core characteristics (The 5 Vs) of big data:

  • Volume: Huge amounts of data that are in the tera, peta or zettabyte range.
  • Velocity: The high dynamics with which data must be generated and processed in real time.
  • Variety: The range of formats - from structured tables (SQL) to semi-structured emails to unstructured raw data (images, videos, sensor data).
  • Veracity (credibility): The challenge of ensuring the quality, accuracy and trustworthiness of data sources.
  • Value: The actual economic benefit and knowledge gained through data analysis.

Modern data management via cloud & data architectures: Companies use centralized systems such as data warehouses (for structured data) and data lakes (for raw data in any format) to flexibly manage huge volumes of data. These architectures are scaled via high-performance cloud infrastructures.

The STACKIT advantage for data-driven companies: STACKIT enables fully sovereign, GDPR-compliant processing of Big Data. Thanks to exclusive data storage in certified German and Austrian data centers, companies from regulated industries (such as finance, healthcare or retail) retain full control over sensitive data - combined with maximum scalability, cost efficiency and the seamless integration of modern analysis tools.

What is big data?

Big data refers to huge, complex collections of data that can no longer be stored, processed or analyzed efficiently using traditional data processing methods. These data volumes come from a variety of different sources, such as the internet, social networks or company applications. Big data is characterized by the so-called 5 Vs: volume (the high volume of data), velocity (the high speed of data generation and data processing), variety (the diversity of data formats and data sources), veracity (the quality and trustworthiness of the data) and value (the added value generated by the data analysis).

Big data offers valuable potential for companies and organizations, as the analysis of these extensive data sets can provide important new insights that enable well-founded business decisions, innovative business models and more efficient processes. Modern technologies such as artificial intelligence and machine learning help to identify correlations and patterns in big data and make them usable in a targeted manner, as traditional software is no longer sufficient for the analysis methods and processing of data volumes.

The most important definitions of big data in this article

The advantages of big data with STACKIT at a glance

STACKIT stores and processes data records exclusively in certified data centers. STACKIT offers attractive, future-proof cloud solutions, especially for companies that attach great importance to security, data protection and compliance with European and regional regulations. The STACKIT infrastructure is regularly independently audited and certified according to international standards. This ensures maximum security and availability for sensitive company data.

Data sovereignty and data protection

STACKIT ensures that all data is stored and processed exclusively in data centers located in Germany and Austria, thereby meeting the highest data protection standards in accordance with the GDPR. This allows companies to retain full control over confidential and sensitive data at all times.

Secure cloud infrastructure

The STACKIT cloud platform provides a secure and reliable environment for big data applications and analytics, specifically tailored to the needs of regulated industries. Thanks to its compliance with strict European data protection and security standards, STACKIT is particularly well-suited for use in sensitive sectors such as telecommunications, healthcare, and finance.

Scalability

With STACKIT, companies can design their big data projects flexibly and scale the necessary resources as needed without having to invest in their own hardware.

Integration of modern tools

STACKIT enables seamless integration and use of modern ETL, monitoring, and analytics tools, allowing companies to efficiently monitor, process, and analyze their data.

Cost efficiency

Using STACKIT's cloud solutions eliminates the need for costly investments in your own IT infrastructure. This means you only pay for the resources you actually use.

What exactly is big data?

Big data comprises extremely extensive and complex data volumes that come from a variety of different sources and can no longer be handled efficiently using conventional processing techniques. Such data is generated by social media, sensor technology, digital transactions and mobile devices, among other things, and is subject to continuous growth.

The so-called "5 Vs" are characteristic of big data:

Volume

Amount of data

Velocity

Speed at which the data is generated

Variety

Different sources from which the data information originates and the variety of formats - from structured tables to unstructured text, audio, video and image files

Veracity

Reliability and quality of data information

Value

The actual benefit that arises from the real-time evaluation of huge amounts of data and which ultimately forms the basis for better decisions, more efficient processes or new products for a company.

Types of Big Data: what is unstructured and structured data?

Data sets can be divided into three categories based on their structure and how easily they can be searched and indexed.

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Structured data

Structured data is data information that is particularly easy to organize, search, and analyze, such as accounting data, machine data logs, or demographic data. You can think of structured data as an Excel spreadsheet with clearly defined rows and columns. The individual elements of this data can be clearly assigned and categorized, allowing database developers and administrators to implement targeted algorithms for searching and analysis. Even though unstructured data exists in vast quantities, it is not necessarily considered big data. This is because, due to its clear organization, it is relatively easy to manage and therefore does not meet the classic definition criteria or present the challenges associated with big data. Database systems based on Structured Query Language (SQL) are traditionally used for managing and querying structured data.

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Unstructured data

Unstructured data includes, for example, social media content, audio files, images, and open-ended customer comments. This type of data cannot be easily represented in traditional column- and row-based database systems. Until now, companies that needed to manage and analyze large volumes of unstructured data had to rely on time-consuming and laborious manual processes. Although the value of insights gained from data analysis is undisputed, the high costs and enormous time investment stood in the way of cost-effective utilization. Furthermore, the lengthy analysis process meant that the insights gained were often already outdated by the time they became available. Today, such unstructured data is typically stored in data lakes, data warehouses, and NoSQL databases, as these systems enable flexible data storage and processing.

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Semi-structured data

Semi-structured data is a combination of structured and unstructured data. A classic example is email: while the actual message body is unstructured, emails contain clearly defined fields such as sender, recipient, subject line, date, and time. Devices that capture geotags, timestamps, or other metadata also generate semi-structured data.

How big data works

Big data provides valuable insights that reveal new opportunities and innovative business models. Once the data has been collected, three measures are important:

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Integration

With big data, information from many different sources and applications is brought together centrally. Traditional methods of data integration such as ETL (extract, transform, load) often reach their limits and are usually unsuitable for analyzing extremely large amounts of data in terabyte or even petabyte sizes. Specialized technologies and innovative approaches are therefore required for the analysis of big data. The data must be recorded and processed during integration. At the same time, it must be ensured that the information is converted into a suitable format so that it can be used for business analyses.
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Management

Big data requires a very large amount of storage space. It can be stored either in a cloud, locally in your own data center (on-premises) or as a hybrid solution (combination of both storage options). The data records can be stored in different formats. Companies and organizations often base their choice of storage strategy on the current data location. Data lakes are becoming increasingly important in the big data context, as they enable flexible adaptation to changing computing requirements and also allow resources to be scaled easily if necessary.
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Analysis

The benefits of big data unfold when you regularly analyze your data and use it as a basis for decision-making for your company. By visually analyzing the extensive data information, companies gain important insights and create transparency. Methods such as machine learning and artificial intelligence can be used to continuously develop data models.

Tips, tricks & important information on Big Data with STACKIT

  • Define clear goals: Think carefully about what you specifically want to achieve with Big Data. Clear objectives help you to make success measurable.
  • Ensure data quality: Ensure that all data is correct, complete and up-to-date. Unreliable data leads to incorrect analyses, so regular checks and data cleansing are important.
  • Identify necessary information: Analyze which data is actually relevant to your objective.
  • Carry out regular backups: Automated data backups protect against data loss.
  • Use a scalable IT infrastructure: Use cloud solutions or distributed systems so that your data platform can flexibly keep pace with growing data volumes.
  • Ensure security and data protection: Protect your data with encryption, access regulations and continuous monitoring. Observe the GDPR data protection requirements.

FAQ - frequently asked questions about big data

What challenges are associated with big data?

Big data offers many advantages, but also challenges. Companies need to store and process large volumes of data securely while ensuring the highest levels of data protection and data quality. The selection of suitable technologies and qualified specialists is complex. In addition, the integration of different systems and the scalability of the solutions require careful planning.

Does STACKIT offer big data solutions?

Yes, STACKIT offers scalable and secure big data solutions for companies that want to store, process and analyze large amounts of data. STACKIT provides GDPR-compliant cloud services specifically designed for data-intensive analytics and artificial intelligence solutions.