What is database denormalization? Advantages, disadvantages and examples
Learn about database denormalization, its advantages, disadvantages, and examples. Discover how it optimizes query speed and simplifies database architecture.
Maciej Hampel
Learn about database denormalization, its advantages, disadvantages, and examples. Discover how it optimizes query speed and simplifies database architecture.
Maciej Hampel

IoT security for business: common vulnerabilities in connected workplace devices and six ways to protect your systems, from updates to monitoring.

Data reliability is one of the core data quality metrics. What it means, how to assess whether your data is reliable, and examples from business.
Laura Kszczanowicz

Visual search lets users find products with an image instead of words. Learn how it works, the technology behind it, and why retailers are adopting it.

BigQuery is replacing Hadoop for enterprise analytics. Why companies move large data workloads to Google Cloud and what changes after the migration.

Ten data engineering best practices for your team, from keeping the tech stack current and automating processes to data quality checks and documentation.
Pawel Jedrzejewicz

The 7 Vs of Big Data explained: volume, velocity, variety, and beyond. See what each one means and why they matter for your data strategy.

Core data engineering techniques that turn raw data into analytics-ready assets, with notes on advanced analytics integration and real-world case studies.
DS Stream
How Kubernetes automates deployment, scaling and management of containerized applications for FMCG companies, plus its architecture and best practices.
Kamil Leduchowski

Airflow Celery Executor explained: how it distributes tasks across workers, when to use it, and how to configure it for scalable data pipelines.
Tomasz Stachera
How GenAI agents automate programming work, from code generation to workflow automation, and where they help engineering teams reduce development costs.


MLOps in FMCG supply chains: automating the deployment, monitoring and management of ML models, with the key tools, best practices and results to expect.
