Data Engineering

Software Architecture in Data-Centric Systems: A Practical Guide to Building Reliable, Scalable Data Platforms

March 03, 2026 at 02:08 PM | Est. read time: 12 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Modern software is increasingly data-centric-meaning the data is not just an output of the system, but the system’s primary product. Whether the goal is analytics, AI/ML, personalization, fraud detection, or […]

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Streaming vs Batch Processing: Choosing the Right Data Architecture for Your Business

February 23, 2026 at 02:10 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Modern data systems are expected to do two things at once: deliver insights quickly and do it reliably at scale. That’s where the “streaming vs batch” decision becomes one of

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Is the Data Lakehouse Just Hype-or a Natural Evolution of Modern Analytics?

February 23, 2026 at 02:21 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. For years, data teams have been stuck choosing between two imperfect worlds: data warehouses (fast and reliable, but rigid and costly at scale) and data lakes (flexible and affordable, but

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Trino: Federated Queries Across Multiple Data Sources (Without Moving Your Data)

February 20, 2026 at 02:41 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Modern analytics stacks rarely live in one place. A single dashboard might need customer attributes from a data warehouse, clickstream events from a data lake, and subscription data from an

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Apache Flink and Amazon Kinesis: Streaming at Scale (Without Losing Sleep)

February 20, 2026 at 03:21 PM | Est. read time: 9 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Modern products live and die by what they know right now: fraud signals, IoT telemetry, clickstream behavior, logistics updates, pricing changes, and application health metrics. Batch pipelines can’t keep up

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DataHub and OpenLineage: A Modern Blueprint for Data Governance and End-to-End Lineage

February 19, 2026 at 02:13 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Modern data stacks move fast: new pipelines ship weekly, dashboards multiply, and “who changed what?” becomes a daily question. The problem isn’t that organizations lack data-it’s that they lack trust

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Terraform for Data Platforms: Infrastructure as Code That Scales (and Stays Sane)

February 18, 2026 at 01:33 PM | Est. read time: 11 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Modern data platforms move fast: new pipelines, new storage layers, new compute clusters, tighter security requirements, and constant cost pressure. If your infrastructure is still built by clicking around in

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Redis vs. TimescaleDB for Real‑Time Data: Performance, Architecture, and When to Use Each

February 16, 2026 at 03:27 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Real-time applications live and die by latency, throughput, and how quickly you can turn fast-moving events into decisions. Two popular technologies often considered for these systems are Redis (an in-memory

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PostgreSQL vs MongoDB vs DynamoDB: How to Choose the Right Database for Your App in 2026

February 13, 2026 at 03:52 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Choosing a database isn’t just a technical checkbox-it shapes your product’s performance, scalability, developer experience, and long-term costs. If you’re comparing PostgreSQL vs MongoDB vs DynamoDB, you’re likely building something

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