Apache Kafka, Spark, Flink, Airflow, Iceberg, ClickHouse, ETL/ELT, data lakes, data warehouses, streaming pipelines
35 posts
Modern data stacks have evolved significantly, moving away from complex ETL pipelines toward simpler ELT (Extract, Load, Transform) architectures. In this paradigm, raw data is loaded into a data warehouse as quickly as possible, and the heavy lifting of transformation happens inside the database...
In the early days of big data, the centralized data warehouse was the gold standard. It worked beautifully for small-to-medium scale analytics. However, as organizations grew, these centralized architectures began to crumble under the weight of scalability issues, slow time-to-insight, and lack o...
For over a decade, Apache Airflow has been the undisputed king of data orchestration. However, as data stacks evolve from simple batch processing to real-time streams, feature stores, and lakehouse architectures, the traditional "task-based" DAG model often feels clunky. Many engineering teams ar...
In the realm of high-throughput data engineering, ensuring data integrity is paramount. When dealing with distributed event streams, the concepts of "at-least-once" and "exactly-once" delivery are not just theoretical—they are critical for building reliable pipelines. However, achieving true exac...
In the rapidly evolving landscape of machine learning deployment, the gap between batch-oriented data engineering and low-latency model inference has become a critical bottleneck. Traditionally, data engineers managed feature pipelines for offline training, while ML engineers struggled to replica...
In the realm of modern data engineering, speed is not just a luxury; it is a requirement. As organizations accumulate petabytes of data across disparate silos—S3, HDFS, Cassandra, Elasticsearch—the need for a unified, high-performance query layer becomes critical. Enter Presto (now split into Pre...
In the rapidly evolving landscape of data engineering, the distinction between batch and stream processing is becoming increasingly blurred. Organizations today demand insights not just from historical data, but from live events as they happen. This is where Apache Flink steps in as a titan of th...