Data Engineering Hardware Microservices 
O'Reilly Media - Ideas
JULY 15, 2020
Microservices seem to be everywhere. Scratch that: talk about microservices seems to be everywhere. So we wanted to determine to what extent, and how, O’Reilly subscribers are empirically using microservices. Here’s a summary of our key findings: Most adopters are successful with microservices. And that’s the problem.
CIO
DECEMBER 22, 2022
DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?
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O'Reilly Media - Ideas
FEBRUARY 11, 2020
Key survey results: The C-suite is engaged with data quality. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Data quality might get worse before it gets better. An additional 7% are data engineers.
O'Reilly Media - Ideas
MARCH 1, 2022
And Holochain is a decentralized framework for building peer-to-peer microservices–no cloud provider needed. ApacheHop is a metadata-driven data orchestration for building dataflows and data pipelines. It integrates with Spark and other data engines, and is programmed using a visual drag-and-drop interface, so it’s low code.
Abhishek Tiwari
DECEMBER 27, 2017
Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Native frameworks.
O'Reilly Media - Ideas
MARCH 18, 2020
The sample is far from tech-laden, however: the only other explicit technology category—“Computers, Electronics, & Hardware”—accounts for less than 7% of the sample. Data scientists dominate, but executives are amply represented. One-sixth of respondents identify as data scientists, but executives—i.e.,
Abhishek Tiwari
JULY 12, 2018
In this post, we will discuss why you should avoid building data pipelines in first place. Depending on the use cases, it is quite possible that you can achieve similar outcomes by using techniques such as data virtualisation or simply building microservices. A data pipeline is a software which runs on hardware.
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