Apache Flink Interview Questions and Answers

access_time 2022-08-24T05:43:10.663Z face Bigdata Engineer
Apache Flink Interview Questions and Answers Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. 1...

Apache Edgent Interview Questions and Answers

access_time 2022-08-24T05:39:31.704Z face Bigdata Engineer
Apache Edgent Interview Questions and Answers Apache Edgent is a programming model and micro-kernel style runtime that can be embedded in gateways and small footprint edge devices enabling local, real-time, analytics on the continuous streams of data coming from equipment, vehicles, systems, applian...

Apache Drill Interview Questions and Answers

access_time 2022-08-24T05:33:12.035Z face Bigdata Engineer
Apache Drill Interview Questions and Answers Drill is an Apache open-source SQL query engine for Big Data exploration. Drill is designed from the ground up to support high-performance analysis on the semi-structured and rapidly evolving data coming from modern Big Data applications, while still prov...

Apache Daffodil Interview Questions and Answers

access_time 2022-08-24T05:29:24.67Z face Bigdata Engineer
Apache Daffodil Interview Questions and Answers Apache Daffodil is a library, requiring Java 8, used to convert between fixed format data and XML/JSON based on a DFDL schema. Some examples show the result of Daffodil parsing various inputs into XML. 1) When should I use an XSD facet like maxLength, ...

Apache CarbonData Interview Questions and Answers

access_time 2022-08-23T10:00:52.969Z face Bigdata Engineer
Apache CarbonData Interview Questions and Answers Apache CarbonData is a new big data file format for faster interactive query using advanced columnar storage, index, compression and encoding techniques to improve computing efficiency, which helps in speeding up queries by an order of magnitude fast...
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