Why are DQ checks critical for every data pipeline, and what are some of the different types of DQ alerts you can set up to enhance the reliability of your pipeline?
Apache Spark is a fast, open-source cluster computing framework for big data, supporting ML, SQL, and streaming. It’s scalable, efficient, and widely used.
Apache Flink is a crucial component of Apache Paimon since it offers the real-time processing power that enhances Paimon's strong consistency and storage features.
Explore two essential IoT protocols: OPC-UA for secure and structured industrial device communication and MQTT, a lightweight, real-time protocol for telemetry.
Learn how to build fault-tolerant, reactive event-driven applications using Spring WebFlux, Apache Kafka, and Dead Letter Queue to handle data loss efficiently.
Dark data is the vast amounts of unstructured information collected by organizations that often go unused. It includes emails, customer interactions, sensor data, etc.
To make long-term trend analysis easier, we can leverage datelists, where we store each metric value corresponding to a date in an array in a sequential manner.
A Data-First IDP integrates governance, traceability, and quality into workflows, transforming how data is managed, enabling scalable, AI-ready ecosystems.
Learn to implement Slowly Changing Dimension Type 2 (SCD2) in a data warehouse for tracking historical data, ensuring data integrity, and enabling scalability.
This article introduces process mining, explaining its key elements and practical applications for discovering and analyzing workflows using event data.
This article examines how QML can harness the principles of quantum mechanics to achieve significant computational advantages over classical approaches.