2015 Big Data Data Engineering Microservices 
d2iq
FEBRUARY 19, 2021
2015): Hidden Technical Debt in Machine Learning Systems. Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. The data engineer’s main focus is on ETL: extracting, transforming, and loading data.
Altexsoft
FEBRUARY 10, 2021
Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.
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Existek
JANUARY 11, 2022
Along with meeting customer needs for computing and storage, they continued extending services by presenting products dealing with analytics, Big Data, and IoT. The next big step in advancing Azure was introducing the container strategy, as containers and microservices took the industry to a new level.
Kentik
NOVEMBER 16, 2015
KDE handles over 10B flow records/day with a microservice architecture that's optimized using metrics. Here at Kentik, our Kentik Detect service is powered by a multi-tenant big data datastore called Kentik Data Engine. So it was critical to instrument every component leading to, around, and within our data engine.
Altexsoft
DECEMBER 14, 2022
delivering microservice-based and cloud-native applications; standardized continuous integration and delivery ( CI/CD ) processes for applications; isolation of multiple parallel applications on a host system; faster application development; software migration; and. Common Docker use cases. Typical areas of application of Docker are.
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