Understanding and using big data correctly

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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

Volume: In the context of big data, volume describes the huge amount of data that is generated every day, often measured in terabytes, petabytes, zettabytes or more.
Velocity: This refers to the high speed at which data must be generated, transferred and processed. Big data often requires real-time or near real-time processing.
Variety: Includes the diversity, the wide range of different data types and sources, such as structured data records (e.g. tables), semi-structured data records (e.g. XML) or unstructured data (e.g. text, images, videos or sensor data).
Veracity: This term describes the trustworthiness, accuracy and quality of the data, as it can be incorrect, contradictory or incomplete, which makes data analysis more difficult.
Value: Describes the benefits (added value) that arise from the analysis and evaluation of big data. Only through meaningful data evaluation can companies and organizations make better decisions or set up new business models.
Data sources: Origins or locations from which information originates, such as databases, social media, log files, sensors, transaction data, etc.
Data analysis: The systematic evaluation of large amounts of data in order to gain patterns, correlations or new insights.
Cloud Computing: Provision of IT resources such as storage, computing power or applications via the internet. The flexible and scalable use of cloud services enables the storage and processing of large amounts of data.
Data warehouse: Central storage system for structured and unstructured data records.
Data lake: Storage location for large volumes of raw data in any format (structured, semi-structured, unstructured). The data records are stored in their original form and can be used flexibly for various analysis purposes.
Data protection: Measures and legal requirements to protect personal data from misuse, loss or unauthorized access. The aim is to safeguard people's privacy and protect sensitive information (e.g. GDPR).

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 guarantees that all data is stored and processed exclusively in German and Austrian data centers and thus meets the highest data protection standards in accordance with the GDPR. This allows companies to retain full control over confidential, sensitive data at all times.

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Secure cloud infrastructure

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

Scalability

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

Cost efficiency

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

Integration of modern tools

STACKIT enables seamless integration and use of modern ETL, monitoring and analysis tools so that companies can efficiently monitor, process and analyze their data information.

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.

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

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.