Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. To achieve these AIOps benefits, comprehensive AIOps tools incorporate four key stages of data processing: Collection. Aggregation.

Artificial Intelligence 282

Artificial Intelligence Cloud Innovation Strategy 282

Dynatrace

JULY 6, 2022

AIOps brings an additional level of analysis to observability, as well as the ability to respond to events that warrant it. Ultimately, IT automation can deliver consistency, efficiency, and better business outcomes for modern enterprises. IT automation tools can achieve enterprise-wide efficiency. Big data automation tools.

Artificial Intelligence 185

Artificial Intelligence Tuning Strategy Big Data 185

Dynatrace

OCTOBER 25, 2022

Organizations adopt DevOps, where developers and operations work together in a continuous loop, so they can develop software and resolve issues efficiently before they affect users. He meant that more and more developers are now becoming responsible for operations, and operations are becoming ingrained in developers’ job descriptions.

DevOps 213

DevOps Big Data Cloud Innovation 213

The Morning Paper

SEPTEMBER 8, 2019

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. VLDB’19. Approximate query support. Implementation.

Big Data 57

Big Data Analytics Latency Azure 57

Dynatrace

DECEMBER 15, 2022

Adding application security to development and operations workflows increases efficiency. AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations. ITOps vs. AIOps. ” The post What is ITOps?

Artificial Intelligence 198

Artificial Intelligence DevOps Hardware Virtualization 198

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. This second solution picks up at data collection, aggregation, and analysis, preparing it for execution. Deterministic AI.

DevOps 192

DevOps Artificial Intelligence Healthcare Innovation 192

The Netflix TechBlog

JUNE 7, 2021

Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the cloud network infrastructure to address the identified problems. The data is also used by security and other partner teams for insight and incident analysis.

Network 325

Network Transportation AWS Cloud 325

ScaleOut Software

JULY 19, 2021

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78

IoT Analytics Big Data Architecture 78

The Netflix TechBlog

MARCH 2, 2021

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Analytics 207

Analytics C++ Innovation Engineering 207

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.” A comprehensive, modern approach to AIOps is a unified platform that encompasses observability, AI, and analytics.

Artificial Intelligence 208

Artificial Intelligence DevOps Innovation Metrics 208

Highly Scalable

MAY 1, 2012

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce.

Analytics 191

Analytics Traffic Big Data Efficiency 191

ScaleOut Software

NOVEMBER 24, 2020

Rapid advances in the telematics industry have dramatically boosted the efficiency of vehicle fleets and have found wide ranging applications from long haul transport to usage-based insurance. The results of batch analysis are typically produced after an hour’s delay or more.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

All Things Distributed

DECEMBER 20, 2017

The benefit for customers: Authorized users can view this data and therefore manage their inventories across different sites, making the maintenance processes much more efficient. By knowing this, Kärcher can generate new top-line revenue in the form of subscription models for its analysis portal. More than mere support.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

The Morning Paper

SEPTEMBER 19, 2019

Could it be Analyzing efficient stream processing on modern hardware ? Hyper Dimension Shuffle describes how Microsoft improved the cost of data shuffling, one of the most costly operations, in their petabyte-scale internal big data analytics platform, SCOPE. for machine generated emails sent to humans).

Blockchain 46

Blockchain Hardware Google Analytics 46

Percona

JANUARY 14, 2019

What if we use ClickHouse (which is a columnar analytical database) as our main datastore? Well, typically, an analytical database is not a replacement for a transactional or key/value datastore. However, ClickHouse is super efficient for timeseries and provides “sharding” out of the box (scalability beyond one node).

Database 40

Database Analytics Blockchain Healthcare 40

Highly Scalable

MARCH 10, 2015

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods. In that case, the equation (1.2)

Retail 152

Retail C++ Analytics Metrics 152

Testsigma

MAY 7, 2020

IBM Big Data and Analytics Hub website cited a case study, where a US insurance company was estimating 15% of their testing efforts to be just test data collection for the backend system and the frontend system. DBA needs to add all the negative and boundary value conditions as well in test data for testing.

Testing 60

Testing Storage Database Processing 60

The Netflix TechBlog

APRIL 29, 2019

Demand Engineering Demand Engineering is responsible for Regional Failovers , Traffic Distribution, Capacity Operations and Fleet Efficiency of the Netflix cloud. We are heavy users of Jupyter Notebooks and nteract to analyze operational data and prototype visualization tools that help us detect capacity regressions.

Open Source 279

Open Source Network Infrastructure Big Data 279

The Netflix TechBlog

JULY 26, 2021

Such a paradigm aspires to assist front-line operations personnel and stakeholders in “running the business”²; performing their tasks through means such as ad hoc analysis, decision-support, and tracking (of tasks, assets, schedules, etc). Data Mesh leverages Iceberg tables as data warehouse sinks for downstream analytics use cases.

Big Data 253

Big Data Government Analytics Processing 253

All Things Distributed

DECEMBER 13, 2009

These can be incidental tasks, such as the analysis of a particular dataset, or tasks where the amount of work to be done is almost never finished, such as media conversion from a Hollywoods studios movie vault, or web crawling for a search indexing company. Driving down the cost of Big-Data analytics.

Cloud 63

Cloud AWS Storage Innovation 63

All Things Distributed

DECEMBER 13, 2016

With the launch of the AWS Europe (London) Region, AWS can enable many more UK enterprise, public sector and startup customers to reduce IT costs, address data locality needs, and embark on rapid transformations in critical new areas, such as big data analysis and Internet of Things. Fraud.net is a good example of this.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

Smashing Magazine

JULY 29, 2019

It was at this point I accidentally clicked on an ‘Explanation’ URL in the devtools, which took me to the network analysis reference , costing me about 5 MB of my budget. The weightiest of these was 186 KB, which isn’t particularly big — there were just so many of them, and they all downloaded at once. Google Dev Docs.

Cache 96

Cache Google Mobile Network 96

All Things Distributed

NOVEMBER 15, 2010

Now that our ability to generate higher and higher clock rates has stalled and CPU architectural improvements have shifted focus towards multiple cores, we see that it is becoming harder to efficiently use these computer systems. Driving down the cost of Big-Data analytics. Cluster Computer, Cluster GPU and Amazon EMR.

AWS 136

AWS Latency Programming Architecture 136