Availability Big Data Efficiency Training 
Scalegrid
MAY 13, 2020
When handling large amounts of complex data, or big data, chances are that your main machine might start getting crushed by all of the data it has to process in order to produce your analytics results. Greenplum features a cost-based query optimizer for large-scale, big data workloads. Query Optimization.
Dynatrace
JUNE 26, 2024
With an all-source data approach, organizations can move beyond everyday IT fire drills to examine key performance indicators (KPIs) and service-level agreements (SLAs) to ensure they’re being met. And they can create relevant queries based on available data to answer questions and make business decisions.
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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. Dynamic approaches schedule block processing on the fly to maximise efficiency. ACM Computing Surveys, Dec.
The Morning Paper
SEPTEMBER 8, 2019
Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., Microsoft’s big data clusters have 10s of thousands of machines, and are used by thousands of users to run some pretty complex queries. Creating training datasets for machine learning ! VLDB’19. Implementation.
Dynatrace
OCTOBER 17, 2022
Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. It works without having to identify training data, then training and honing. A huge advantage of this approach is speed.
The Morning Paper
MAY 14, 2019
Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., A DNN model is trained to recognise patterns in space and time that lead to QoS violations. When available, it can use hardware level performance counters. ASPLOS’19.
Dynatrace
OCTOBER 14, 2021
Gartner defines AIOps as the combination of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” They require extensive training, and real-user must spend valuable time filtering any false positives. What is AIOps?
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