Big Data Efficiency Processing Systems 
Highly Scalable
AUGUST 20, 2013
The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. Fault-tolerance.
Dynatrace
JUNE 26, 2023
This, in turn, accelerates the need for businesses to implement the practice of software automation to improve and streamline processes. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. Automate DevSecOps processes at scale. Application security.
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The Morning Paper
DECEMBER 13, 2020
An overview of end-to-end entity resolution for big data , Christophides et al., It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. The processing mode – traditional batch (with or without budget constraints), or incremental. Block processing.
Dynatrace
JULY 6, 2022
At its most basic, automating IT processes works by executing scripts or procedures either on a schedule or in response to particular events, such as checking a file into a code repository. Adding AIOps to automation processes makes the volume of data that applications and multicloud environments generate much less overwhelming.
The Netflix TechBlog
MARCH 4, 2024
Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. In this way, no human intervention is required in the remediation process. Multi-objective optimizations. user name).
VoltDB
OCTOBER 17, 2024
What Makes the Automotive Industry Ripe for Real-Time Data Decisioning? The automotive industry is characterized by complex supply chains, intricate production processes, and stringent quality requirements. Efficient supply chain management is crucial for minimizing production costs and meeting delivery schedules.
The Netflix TechBlog
JUNE 20, 2024
To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.” However, as the system has increased in scale and complexity, Pensive has been facing challenges due to its limited support for operational automation, especially for handling memory configuration errors and unclassified errors.
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