DZone
DECEMBER 27, 2023
Welcome to the first post in our exciting series on mastering offline data pipeline's best practices, focusing on the potent combination of Apache Airflow and data processing engines like Hive and Spark. Working together, they form the backbone of many modern data engineering solutions.
Dynatrace
DECEMBER 15, 2022
As workloads shift to public, private, and hybrid cloud environments, CloudOps teams help IT and DevOps manage increasing complexity by defining and managing best practices for cloud-based operations. ITOps vs. AIOps.
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The Netflix TechBlog
JUNE 1, 2021
clinical data was often small enough to fit into memory on an average computer and only in rare cases would its computation require any technical ingenuity or massive computing power. There was not enough scope to explore the distributed and large-scale computing challenges that usually come with big data processing.
DZone
JANUARY 19, 2023
Because with the advent of cloud providers, we are less worried about managing data centers. This leads to an increase in the size of data as well. Big data is generated and transported using various mediums in single requests. Everything is available within seconds on-demand.
Rigor
JANUARY 13, 2020
Take, for example, The Web Almanac , the golden collection of Big Data combined with the collective intelligence from most of the authors listed below, brilliantly spearheaded by Google’s @rick_viscomi. Data-driven strategies and battle tested techniques will make you a master of mobile-and improve your non-mobile design, too!
Dynatrace
JANUARY 11, 2023
As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. Best practices to consider. What is cloud monitoring? Define goals with a clear path to success.
O'Reilly
JANUARY 13, 2020
Change is inevitable, and as programming languages continue to lean in to optimization for new trends in the cloud, microservices, big data, and machine learning, each language and its ecosystem will continue to adapt in its own unique way. ” What lies ahead?
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