Tag: Data Engineering
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Expert: event-driven orchestration with EventBridge and Step Functions
In complex distributed architectures, orchestrating event-driven workflows reliably is a core challenge. This article explores how AWS EventBridge and Step Functions combine to deliver powerful, maintainable, and scalable event-driven orchestration.
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Empirical: Parquet vs ORC compression benchmarks
Parquet and ORC are the heavyweights of columnar storage in modern data engineering, each designed for high-performance analytics on massive datasets. In this post, we empirically benchmark both formats under post-2024 workloads, comparing compression ratios, read/write throughput, CPU utilization, and query latency across common engines like Spark, Trino, and DuckDB. The results shed light on…
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Expert: advanced lineage propagation across systems
Modern data systems demand end-to-end lineage propagation that spans clouds, tools, and architectures. This article explores advanced lineage propagation techniques, open standards, and real-world implementations powering enterprise-scale data ecosystems in 2025.
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Introduction to stream processing concepts
Stream processing is at the core of real-time analytics and event-driven architectures. This article introduces stream processing concepts, explains key differences from batch processing, and highlights tools like Apache Kafka, Flink, and Materialize that enable continuous computation on live data streams.
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Empirical: OLTP vs OLAP query performance comparison
An empirical 2025 analysis comparing OLTP and OLAP systems across latency, throughput, and scalability metrics. The post benchmarks PostgreSQL, ClickHouse, and Snowflake, examining architectural trade-offs and real-world engineering implications.
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Tools: Prometheus, Grafana, Airflow sensors
Prometheus, Grafana, and Airflow sensors form the core of modern observability and orchestration in data engineering. This post explores how these tools interact, with practical examples, integration strategies, and best practices for building reliable, metrics-driven data pipelines.
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Best practices: domain ownership and federated governance
Discover how domain ownership and federated governance enable organizations to scale autonomy without losing control. This best-practice guide explores principles, architecture, and tooling strategies for implementing distributed accountability while maintaining global consistency.
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Introduction to streaming data architecture
Streaming data architecture is the backbone of modern real-time systems, powering everything from recommendation engines to IoT telemetry and financial analytics. This post introduces the core concepts, patterns, and tools behind streaming architectures, with practical insights on how to design scalable, fault-tolerant pipelines for real-world applications.
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Tools: Great Expectations, Soda Core, Deequ
Data quality validation is no longer an afterthought but a core component of modern data pipelines. This article explores three leading open-source frameworks — Great Expectations, Soda Core, and Deequ — that automate data validation, profiling, and continuous monitoring. We compare their architecture, integration capabilities, and practical strengths through empirical examples and real-world use cases…
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Expert: real-time feature stores and ML stream inference
Real-time feature stores are redefining machine learning architectures by enabling continuous and consistent feature computation for streaming inference. This post dives deep into how these systems operate, their architecture, key tools, and emerging trends in operational ML engineering.