Rigor

MAY 22, 2019

Big data is like the pollution of the information age. The Big Data Struggle and Performance Reporting. Alternatively, a number of organizations have created their own internal home-grown systems for managing and distilling web performance and monitoring data. No fuss, no muss.

Big Data 76

Big Data Monitoring Performance Metrics 76

DZone

AUGUST 4, 2022

Spark-Radiant is Apache Spark Performance and Cost Optimizer. Spark-Radiant will help optimize performance and cost considering catalyst optimizer rules, enhance auto-scaling in Spark, collect important metrics related to a Spark job, Bloom filter index in Spark, etc. Spark-Radiant is now available and ready to use.

Performance 246

Performance Metrics Availability Big Data 246

Dynatrace

MAY 1, 2023

This operational data could be gathered from live running infrastructures using software agents, hypervisors, or network logs, for example. ITOA collects operational data to identify patterns and anomalies for faster incident management and near-real-time insights. Choose a repository to collect data and define where to store data.

Analytics 246

Analytics Artificial Intelligence Big Data Open Source 246

All Things Distributed

AUGUST 18, 2011

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. Driving down the cost of Big-Data analytics. Comments ().

Big Data 94

Big Data Analytics AWS Cloud 94

DZone

JULY 29, 2019

In today's world, data is generated in high volumes and to make something out of it, extracted data is needed to be transformed, stored, maintained, governed and analyzed. These processes are only possible with a distributed architecture and parallel processing mechanisms that Big Data tools are based on.

Big Data 162

Big Data Government Open Source Storage 162

High Scalability

SEPTEMBER 14, 2020

This is a guest post by Limor Maayan-Wainstein , a senior technical writer with 10 years of experience writing about cybersecurity, big data, cloud computing, web development, and more. High performance computing (HPC) enables you to solve complex problems which cannot be solved by regular computing.

Cloud 261

Cloud Big Data Virtualization Efficiency 261

Dynatrace

JUNE 1, 2020

Application Performance Monitoring (APM) in its simplest terms is what practitioners use to ensure consistent availability, performance, and response times to applications. APM can also be referred to as: Application performance management. Performance monitoring. Dynatrace news. Application monitoring.

Monitoring 173

Monitoring Performance Social Media Artificial Intelligence 173

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Performance.

Big Data 63

Big Data Storage Benchmarking Hardware 63

DZone

JUNE 13, 2023

In short, it is the ability to handle more data, more users, and more demand without sacrificing performance, reliability, or security. The reason is straightforward, today, applications generate enormous amounts of data. It is not uncommon to question why scalability has grabbed the attention of the masses these days.

Scalability 253

Scalability IoT Big Data Internet 253

Dynatrace

JUNE 26, 2023

Software analytics offers the ability to gain and share insights from data emitted by software systems and related operational processes to develop higher-quality software faster while operating it efficiently and securely. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI.

Software 246

Software Software Analytics Big Data 246

DZone

JULY 17, 2023

Apache Spark is a leading platform in the field of big data processing, known for its speed, versatility, and ease of use. This article delves into various techniques that can be employed to optimize your Apache Spark jobs for maximum performance.

Big Data 130

Big Data Performance Open Source Tuning 130

The Netflix TechBlog

JANUARY 13, 2022

This blog will explore these two systems and how they perform auto-diagnosis and remediation across our Big Data Platform and Real-time infrastructure. The streaming platform recently added Data Mesh , and we need to expand Streaming Pensive to cover that.

Big Data 241

Big Data Infrastructure Metrics Games 241

Dynatrace

JANUARY 11, 2023

With more organizations taking the multicloud plunge, monitoring cloud infrastructure is critical to ensure all components of the cloud computing stack are available, high-performing, and secure. These next-generation cloud monitoring tools present reports — including metrics, performance, and incident detection — visually via dashboards.

Cloud 295

Cloud Monitoring Best Practices Infrastructure 295

Dynatrace

JULY 6, 2020

Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. Effortlessly optimize Azure database performance. Database-service views provide all the metrics you need to set up high-performance database services. Azure Front Door.

Azure 227

Azure Cloud Big Data Virtualization 227

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. ACM Computing Surveys, Dec. 2020, Article No.

Big Data 83

Big Data Open Source Processing Analytics 83

The Netflix TechBlog

OCTOBER 28, 2021

Netflix’s unique work culture and petabyte-scale data problems are what drew me to Netflix. During earlier years of my career, I primarily worked as a backend software engineer, designing and building the backend systems that enable big data analytics. Moreover, its petabyte scale also brings unique engineering challenges.

Data Engineering 177

Data Engineering Engineering Big Data Software Engineering 177

DZone

JANUARY 20, 2023

It has been a norm to perceive that distributed databases use the method of adding cheap PC(s) to achieve scalability (storage and computing) and attempt to store data once and for all on demand. However, doing the same cannot achieve equivalent scalability without massively sacrificing query performance on graph systems.

Scalability 210

Scalability Big Data Hardware Internet 210

Dynatrace

JUNE 1, 2020

Application Performance Monitoring (APM) in its simplest terms is what practitioners use to ensure consistent availability, performance, and response times to applications. APM can be referred to as: Application performance monitoring. Application performance management. Performance monitoring. Dynatrace news.

Artificial Intelligence 254

Artificial Intelligence Social Media Monitoring IoT 254

Dynatrace

OCTOBER 4, 2022

Data lakehouses take advantage of low-cost object stores like AWS S3 or Microsoft Azure Blob Storage to store and manage data cost-effectively. Data lakehouses offer a way to interrogate the data and send processing instructions in the form of queries. Data lakehouses deliver the query response with minimal latency.

Artificial Intelligence 246

Artificial Intelligence Storage Analytics Government 246

DZone

JULY 22, 2022

Honestly, these two terms have recently been doing rounds in the big data world. These technologies specialize in transmitting large amounts of data across different trading partners and companies. These technologies specialize in transmitting large amounts of data across different trading partners and companies.

Big Data 188

Big Data Technology Technology Systems 188

DZone

MAY 21, 2019

The demand for more IT resource-intensive applications has significantly increased today, whether it is to process quicker transactions, gain real-time insight, crunch big data sets, or to meet customer expectations. That’s because NVMe provides 6x higher bandwidth and IOPS advantage compared to SAS/SATA SSD.

Performance 100

Performance Big Data Storage Processing 100

DZone

DECEMBER 28, 2020

Nowadays, Big Data tests mainly include data testing, paving the way for the Internet of Things to become the center point. Factors such as reliability and quality are being given extra attention that results in the decrease of software app errors, enhancing the security and the app performance.

Software 193

Software Software Testing Big Data 193

Dynatrace

OCTOBER 25, 2022

This meant there were still operations, only they were performed by someone else! AIOps , a term coined by Gartner in 2016, combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.

DevOps 246

DevOps Big Data Cloud Innovation 246

Dynatrace

JULY 6, 2022

This kind of automation can support key IT operations, such as infrastructure, digital processes, business processes, and big-data automation. Big data automation tools. These tools provide the means to collect, transfer, and process large volumes of data that are increasingly common in analytics applications.

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

Dynatrace

APRIL 21, 2021

In February 2021, Dynatrace announced full support for Google’s Core Web Vitals metrics , which will help site owners as they start optimizing Core Web Vitals performance for SEO. Not everyone has expertise in performance optimization and how it can impact SEO. To do this effectively, you need a big data processing approach.

Traffic 264

Traffic Mobile Metrics Analytics 264

The Netflix TechBlog

JULY 21, 2022

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data 184

Big Data Cache Engineering Data Engineering 184

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 325

Artificial Intelligence Cloud Innovation Strategy 325

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. VLDB’19. in the paper). The accuracy was considered adequate by the developer.

Big Data 57

Big Data Analytics Latency Azure 57

The Netflix TechBlog

MAY 4, 2023

The first phase involves validating functional correctness, scalability, and performance concerns and ensuring the new systems’ resilience before the migration. These include Quality-of-Experience(QoE) measurements at the customer device level, Service-Level-Agreements (SLAs), and business-level Key-Performance-Indicators(KPIs).

Traffic 343

Traffic Latency Tuning Systems 343

DZone

MAY 30, 2019

With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous. Financial analysis with real-time analytics is used for predicting investments and drives the FinTech industry's needs for high-performance computing.

Artificial Intelligence 100

Artificial Intelligence Social Media FinTech Storage 100

Dynatrace

OCTOBER 4, 2022

Additionally, Grail delivers unrivaled performance without losing the precision of unsampled, gapless data. Grail addresses today’s challenges of big data and cloud everywhere: Grail is highly scalable, cost-effective, and super-fast. Turn log data into value and activate Grail. Use conditional statements.

Analytics 264

Analytics Artificial Intelligence Storage Serverless 264

Dynatrace

DECEMBER 15, 2022

The primary goal of ITOps is to provide a high-performing, consistent IT environment. Organizations measure these factors in general terms by assessing the usability, functionality, reliability, and performance of products and services. Performance. What does IT operations do? ITOps vs. AIOps.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

Dynatrace

JUNE 29, 2022

A hybrid cloud, however, combines public infrastructure and services with on-premises resources or a private data center to create a flexible, interconnected IT environment. Hybrid environments provide more options for storing and analyzing ever-growing volumes of big data and for deploying digital services.

Infrastructure 246

Infrastructure Cloud Azure AWS 246

DZone

AUGUST 23, 2023

In the era of big data, efficient data management and query performance are critical for organizations that want to get the best operational performance from their data investments.

Big Data 130

Big Data Analytics Innovation Efficiency 130

Dynatrace

FEBRUARY 16, 2023

As observability and security data converge in modern multicloud environments, there’s more data than ever to orchestrate and analyze. The goal is to turn more data into insights so the whole organization can make data-driven decisions and automate processes.

Analytics 246

Analytics Innovation Metrics Database 246

The Netflix TechBlog

APRIL 29, 2019

The service that orchestrates failover uses numpy and scipy to perform numerical analysis, boto3 to make changes to our AWS infrastructure, rq to run asynchronous workloads and we wrap it all up in a thin layer of Flask APIs. These libraries are the primary way users interface programmatically with work in the Big Data platform.

Open Source 279

Open Source Network Infrastructure Big Data 279

The Netflix TechBlog

OCTOBER 27, 2020

Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto , to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 248

Latency Storage Big Data Tuning 248