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. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.
Dynatrace
JULY 6, 2022
And what are the best strategies to reduce manual labor so your team can focus on more mission-critical issues? 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. Big data automation tools.
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Dynatrace
DECEMBER 15, 2022
To ensure resilience, ITOps teams simulate disasters and implement strategies to mitigate downtime and reduce financial loss. If malware, data corruption, or another security breach occurs, ITOps teams work with security teams to identify, isolate, and remediate affected systems to minimize damage and data loss.
Dynatrace
MARCH 14, 2022
AIOps (or “AI for IT operations”) uses artificial intelligence so that big data can help IT teams work faster and more effectively. There are two main approaches to AIOps: Traditional AIOps: Machine learning models identify correlations between IT events. Gartner introduced the concept of AIOps in 2016.
The Netflix TechBlog
MAY 4, 2023
This blog series will examine the tools, techniques, and strategies we have utilized to achieve this goal. In this testing strategy, we execute a copy (replay) of production traffic against a system’s existing and new versions to perform relevant validations. This approach has a handful of benefits.
Dynatrace
APRIL 11, 2023
The focus on bringing various organizational teams together—such as development, business, and security teams — makes sense as observability data, security data, and business event data coalesce in these cloud-native environments.
Abhishek Tiwari
DECEMBER 22, 2017
Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. This can be time-consuming and bit challenging if you are dealing with hundreds of data sources and thousands of event types.
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