Dynatrace

JUNE 26, 2023

Software automation is the practice of creating software applications to reduce or eliminate human intervention in repetitive, time-consuming IT tasks and cloud operations. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. Applications and microservices monitoring.

Software 193

Software Software Analytics Big Data 193

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 291

Artificial Intelligence Cloud Innovation Strategy 291

Dynatrace

DECEMBER 15, 2022

As more organizations adopt cloud-native technologies, traditional approaches to IT operations have been evolving. Complex cloud computing environments are increasingly replacing traditional data centers. In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025.

Artificial Intelligence 208

Artificial Intelligence DevOps Hardware Virtualization 208

Dynatrace

JULY 6, 2022

Scripts and procedures usually focus on a particular task, such as deploying a new microservice to a Kubernetes cluster, implementing data retention policies on archived files in the cloud, or running a vulnerability scanner over code before it’s deployed. Monitoring and logging are fundamental building blocks of observability.

Artificial Intelligence 194

Artificial Intelligence Tuning Strategy Big Data 194

Scalegrid

FEBRUARY 8, 2024

Real-World Use Cases of Distributed Storage Distributed storage systems are the backbone of massively scalable storage services, designed to serve both cloud-based and on-premises environments. These systems enable vast amounts of data to be spread over multiple nodes, allowing for simultaneous access and boosting processing efficiency.

Storage 130

Storage Systems Big Data Azure 130

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. AIOps use cases.

DevOps 202

DevOps Artificial Intelligence Healthcare Innovation 202

Dynatrace

FEBRUARY 16, 2023

Grail data lakehouse delivers massively parallel processing for answers at scale Modern cloud-native computing is constantly upping the ante on data volume, variety, and velocity. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

Analytics 191

Analytics Innovation Metrics Database 191

Dynatrace

OCTOBER 4, 2022

While data lakehouses combine the flexibility and cost-efficiency of data lakes with the querying capabilities of data warehouses, it’s important to understand how these storage environments differ. Data warehouses. Data warehouses were the original big data storage option.

Artificial Intelligence 214

Artificial Intelligence Storage Analytics Government 214

The Netflix TechBlog

JUNE 7, 2021

At much less than 1% of CPU and memory on the instance, this highly performant sidecar provides flow data at scale for network insight. Challenges The cloud network infrastructure that Netflix utilizes today consists of AWS services such as VPC, DirectConnect, VPC Peering, Transit Gateways, NAT Gateways, etc and Netflix owned devices.

Network 325

Network Transportation AWS Cloud 325

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.” Only deterministic AIOps technology enables fully automated cloud operations across the entire enterprise development lifecycle.

Artificial Intelligence 218

Artificial Intelligence DevOps Innovation Metrics 218

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., Seer is an online system that observes the behaviour of cloud applications (using the DeathStarBench microservices for the evaluation) and predicts when QoS violations may be about to occur. ASPLOS’19.

Big Data 45

Big Data Cloud Performance Hardware 45

All Things Distributed

DECEMBER 13, 2016

The British Government is also helping to drive innovation and has embraced a cloud-first policy for technology adoption. The council has deployed IoT Weather Stations in Schools across the City and is using the sensor information collated in a Data Lake to gain insights on whether the weather or pollution plays a part in learning outcomes.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

Dynatrace

SEPTEMBER 15, 2020

Stefano started his presentation by showing how much cost and performance optimization is possible when knowing how to properly configure your application runtimes, databases, or cloud environments: Correct configuration of JVM parameters can save up to 75% resource utilization while delivering same or better performance!

Performance 189

Performance Java Metrics Cloud 189

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. Python is also a tool we typically use for automation tasks, data exploration and cleaning, and as a convenient source for visualization work.

Open Source 279

Open Source Network Infrastructure Big Data 279

All Things Distributed

JANUARY 6, 2016

We believe that with the launch of the Seoul Region, AWS will enable many more enterprise customers in Korea to reduce the cost of their IT operations and innovate faster in critical new areas such as big data analysis, Internet of Things, and more.

AWS 110

AWS Cloud Games Latency 110

ScaleOut Software

JULY 19, 2021

Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention. Let’s take a closer look at today’s conventional streaming analytics architectures, which can be hosted in the cloud or on-premises.

IoT 78

IoT Analytics Big Data Architecture 78

ACM Sigarch

MAY 31, 2023

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. In the first phase, CXL memory should be transparent to applications without requiring changes, especially in public cloud environments.

Latency 52

Latency Hardware Cache Architecture 52

ScaleOut Software

JUNE 15, 2020

Instead, most applications just sift through the telemetry for patterns that might indicate exceptional conditions and forward the bulk of incoming messages to a data lake for offline scrubbing with a big data tool such as Spark. Maintain State Information for Each Data Source.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

JUNE 15, 2020

Instead, most applications just sift through the telemetry for patterns that might indicate exceptional conditions and forward the bulk of incoming messages to a data lake for offline scrubbing with a big data tool such as Spark. Maintain State Information for Each Data Source.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

NOVEMBER 24, 2020

At the same time, telemetry snapshots are stored in a data lake, such as HDFS , for offline batch analysis and visualization using big data tools like Spark. The results of batch analysis are typically produced after an hour’s delay or more. A second key limitation is the lack of real-time aggregate analysis.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Brendan Gregg

APRIL 30, 2018

In this year's CFP we’re looking for topics covering the latest trends and best practices in cloud computing, containerization, machine learning, big data, infrastructure, scalability, DevOps, IT management, automation, reliability, monitoring, performance tuning, security, databases, programming, datacenters, and more.

DevOps 43

DevOps Network Best Practices Programming 43

Brendan Gregg

APRIL 29, 2018

In this year's CFP we’re looking for topics covering the latest trends and best practices in cloud computing, containerization, machine learning, big data, infrastructure, scalability, DevOps, IT management, automation, reliability, monitoring, performance tuning, security, databases, programming, datacenters, and more.

DevOps 40

DevOps Network Best Practices Programming 40