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Strengthening Container Security: A Practical Guide to Docker Hardened Images

Docker containers have become the backbone of modern application deployment, but with widespread adoption comes increased security scrutiny. Organizations face mounting pressure to secure their software supply chain, especially when using open-source container images that may contain packages with known Common Vulnerabilities and Exposures (CVEs). In December 2025, Docker made a groundbreaking move by releasing over 1,000 hardened container images completely free under the Ap

Jan 83 min read

Generating Synthetic Data Beyond Tabular Data Generation

Why This Pipeline Needed to Exist Most teams now hit a common wall: they need production‑like data, but real tables are locked behind privacy rules, legal reviews, or pure operational friction. Synthetic data promises a way out—but only if it behaves like the real thing, not just “passes the schema.” The project goal was clear and unforgiving: build a synthetic data pipeline that can plug into any PostgreSQL database with zero code changes, and still maintain close to 90% fid

Dec 24, 20255 min read

Challenges in Relational Multi-Table Synthetic Data Generation

1. Introduction Synthetic data generation is increasingly important when working with sensitive or regulated datasets. While generating synthetic data for single tables is straightforward using GANs or statistical models, generating relational multi-table synthetic data is significantly more complex. Relational databases do not exist in isolation. They contain relationships that define how information flows across the system: Foreign keys (parent → child) Many-to-one (St

Nov 19, 20255 min read

Semantic Data Matching for Large Datasets: A Scalable Pipeline

In the realm of data management, integrating information from diverse sources poses significant challenges due to variations in terminology, structure, and content. Traditional matching methods, which depend on exact or approximate string comparisons, often fail to capture underlying meanings, leading to incomplete or inaccurate alignments. To overcome this, fuzzy logic and phonetic matching became prominent approaches. Fuzzy matching uses algorithms like Levenshtein distanc

Oct 22, 20258 min read