Data Engineering

Data Engineering has become increasingly relevant in the highly-connected, AI driven world. Today, data architectures are as vast and varied as the use-cases they support. For most of us, data architecture simply meant running an RDBMS for all of our needs, from transactional read-write workloads to ad-hoc point and scan analytics loads. As data grew in numbers more use-cases for data-driven products such as fraud detection systems, recommender systems, personalization services came into existence.

Data engineers entered the field to solve our problems by introducing specialized data stores for example; search engines, graph engines, large scale data processing such as Spark, NoSQL, stream processing and the machinery to glue them together like ETL pipelines, Kafka, Sqoop, Flume.

Under Data Engineering


How to choose a NoSQL database looking at the technology capabilities and limitations

How to re-engineer your application with artificial intelligence

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