Glossary

→ Private Cloud
→ Public Cloud
→ PaaS (Platform as a Service)

Cloud computing is the delivery of computing services (like storage, databases, software, and processing power) over the internet (the „cloud“), instead of using your own servers or computers running locally. Instead of buying and maintaining physical hardware, you „rent“ what you need from cloud providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud.

This shift changed everything.

Cloud computing has been one of the key reasons why tech companies grew so fast. It allowed businesses and entrepreneurs to build, scale, and launch digital products without huge upfront investments. What used to take months and large IT teams 20 years ago can now be done in hours, with just a credit card and a laptop.

Today, cloud computing is the standard for most digital services. But with increasing political and regulatory uncertainties, questions around where data is stored and processed have become more important again. Companies are now paying closer attention to data privacy, sovereignty, and control, especially when data crosses borders.

This is especially important for any company which has digital assets representing the core value.

→ Data Warehouse
→ Data Lakehouse
→ Data Lake

Big Data refers to extremely large and complex sets of information that are too big or too fast for traditional tools to handle. It’s not just about the amount of data, but also about the speed, variety, and complexity.

For example: online shopping, sensors, GPS, and apps create transactions every second, resulting in very large amounts of data. Big Data technologies help businesses store, process, and analyze this flood of data to find useful patterns, make better decisions, or even predict future trends.

It’s called “big” not just because of size, but because of the challenge and opportunity it brings.

→ Data Lake
→ Data Lakehouse
→ Structured Data
→ Unstructured Data

Semi-structured data is a mix between structured and unstructured data. It doesn’t fit perfectly into rows and columns like structured data, but it still has some form of organization (like tags or labels) that helps make sense of it. Examples include JSON files, XML, or data from online forms. It’s more flexible than structured data and easier to process than fully unstructured data.

→ Data Lake
→ Data Lakehouse
→ Structured Data
→ Semi-structured Data

Unstructured data is information that doesn’t follow a fixed format, making it harder for computers to organize or analyze right away. Examples include emails, videos, images, PDFs, or social media posts. It’s often rich in content but needs extra processing before it can be used for reporting or analysis.

→ Data Governance
→ Data Culture
→ Data Mesh

A Data Product is a trusted, easy-to-use piece of data that’s built and managed like a real product. It could be a report, a dataset, or a dashboard: anything that delivers valuable information to its users. In a Data Mesh setup, different business teams (like finance, sales, or marketing) are responsible for creating and maintaining their own data products.

Data Products should be:

• Well-documented (so users understand what it is and how to use it)

• Reliable (it delivers correct and up-to-date data)

• Accessible (others can easily find and use it)

This approach helps make data more usable across the organization and encourages teams to take ownership of the data they know best.

Data Products must have a Data Owner assigned, to help establish clarity and trust. Without ownership, data products can become messy, outdated, or nobody understands how to use them.

→ Data Governance
→ Data Culture
→ Data Product

Data Mesh is a way of thinking about how to manage data in large companies. Instead of having one central team control everything, Data Mesh gives different departments (like marketing, sales, or finance) the responsibility to manage and share their own data, almost like each team runs a small data business. The goal is to make data more available, trusted, and useful across the whole company, without overloading or putting too much dependency on a single team.

→ Data Warehouse
→ Unstructured Data
→ Business Intelligence

Structured data is information that is organized in a clear way, usually in tables with rows and columns. Typical formats are like in a spreadsheet or a database. Each piece of data fits neatly into a specific category (like name, date, or amount), which makes it easy to search, sort, and analyze. Because it follows a consistent format, structured data is ideal for reports, dashboards, and automated systems.

→ Data Lakehouse
→ Structured Data
→ Data Mesh

A Data Warehouse (DWH) is a well-organized storage system that collects data from different parts of a business: sales, finance, or operations. It stores it in a structured way, ready for reporting and analysis. A well structured DWH is like a neat library where every piece of data is carefully labeled and stored in the right place.

Unlike a Data Lake, which stores raw and messy data, a Data Warehouse stores clean, processed, and organized data that’s easy to understand and use for business decisions.

→ Data Warehouse
→ Data Lakehouse
→ Data Mesh

A Data Lake is a large digital storage space where you can keep all kinds of data: structured (like spreadsheets), semi-structured (like JSON files), and unstructured (like images or documents, PDFs). Unlike traditional databases that need everything organized first, a data lake lets you store information as it is, and organize or analyze it later when needed. It’s great for flexibility and working with very large volumes of data.

Data Lakes typically offer cheaper storage compared to Data Warehouses.

→ Data Warehouse
→ Data Lake
→ Data Mesh

A Data Lakehouse combines the best parts of two systems: a data lake and a data warehouse. A data lake stores huge amounts of raw data (like a messy digital storage room), while a data warehouse organizes data for easy reporting (like a clean, structured library). A data lakehouse does both: it stores lots of data like a lake, but also keeps it organized and ready to use like a warehouse. This makes it easier and faster to turn data into insights – all in one place.