Dynatrace

OCTOBER 4, 2022

Data warehouses offer a single storage repository for structured data and provide a source of truth for organizations. However, organizations must structure and store data inputs in a specific format to enable extract, transform, and load processes, and efficiently query this data. Massively parallel processing.

Artificial Intelligence 246

Artificial Intelligence Storage Analytics Government 246

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. Key challenges.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Dynatrace

DECEMBER 15, 2022

In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025. This transition to public, private, and hybrid cloud is driving organizations to automate and virtualize IT operations to lower costs and optimize cloud processes and systems. So, what is ITOps? What is ITOps? Performance.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

The Netflix TechBlog

DECEMBER 21, 2020

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Some of the optimizations are prerequisites for a high-performance data warehouse.

Storage 212

Storage Latency Efficiency Data Engineering 212

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 188

Big Data Cache Engineering Data Engineering 188

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. A sizable fraction of the jobs are much larger.

Big Data 57

Big Data Analytics Latency Azure 57

The Morning Paper

OCTOBER 26, 2020

Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. What follows is a discussion of where big data systems might be heading, heavily inspired by the remarks in this paper, but with several of my own thoughts mixed in.

Cloud 104

Cloud Big Data Latency Architecture 104

The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. In this way, no human intervention is required in the remediation process. Multi-objective optimizations. user name).

Tuning 217

Tuning Efficiency Big Data Engineering 217

All Things Distributed

NOVEMBER 15, 2010

From financial processing and traditional oil & gas exploration HPC applications to integrating complex 3D graphics into online and mobile applications, the applications of GPU processing appear to be limitless. For example, the most fundamental abstraction trade-off has always been latency versus throughput.

AWS 125

AWS Programming Latency Architecture 125

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

Scalegrid

FEBRUARY 8, 2024

These elements work together to spread data over several locations physically distributed, possibly extending across different data centers while optimizing available storage resources. This process effectively duplicates essential parts of information to safeguard against potential loss.

Storage 130

Storage Systems Big Data Azure 130

ScaleOut Software

JULY 19, 2021

And it can maintain contextual information about every data source (like the medical history of a device wearer or the maintenance history of a refrigeration system) and keep it immediately at hand to enhance the analysis. Conventional streaming analytics architectures have not kept up with the growing demands of IoT.

IoT 78

IoT Big Data Analytics Architecture 78

All Things Distributed

AUGUST 16, 2011

There are different considerations when deciding where to allocate resources with latency and cost being the two obvious ones, but compliance sometimes plays an important role as well. Government and Big Data. One particular early use case for AWS GovCloud (US) will be massive data processing and analytics.

AWS 88

AWS Government Big Data Cloud 88

Scalegrid

MARCH 28, 2024

Redis can be configured to optimally utilize both RDB and AOF persistence methods optimally, achieving a balance between speed and data safety while minimizing the impact on response times due to its child process handling for disk writes. Data transfer technology. Cube or box Block chain of abstract financial data.

Cache 130

Cache Storage Architecture Scalability 130

All Things Distributed

JULY 13, 2010

Customers with complex computational workloads such as tightly coupled, parallel processes, or with applications that are very sensitive to network performance, can now achieve the same high compute and networking performance provided by custom-built infrastructure while benefiting from the elasticity, flexibility and cost advantages of Amazon EC2.

Cloud 96

Cloud AWS Automotive Latency 96

The Morning Paper

JUNE 23, 2019

Factor VI in the 12-factor app manifesto , “Execute the app as one or more stateless processes,” to be dropped and replaced with “Execute the app as one or more stateful processes.” session state that you want to survive an application process crash), and to keep the application server/services layer stateless.

Cache 79

Cache Latency Google Network 79

Scalegrid

MARCH 14, 2024

Defining Hybrid Cloud Strategy The decision-making process about where to situate data and applications is vital to any hybrid cloud solution. Ensuring Security and Compliance Securing a hybrid cloud necessitates defending infrastructure, applications, and data that span both on-premises and cloud services.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Abhishek Tiwari

DECEMBER 27, 2017

In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture. A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

All Things Distributed

JULY 11, 2011

Spot Instances are ideal for use cases like web and data crawling, financial analysis, grid computing, media transcoding, scientific research, and batch processing. Driving down the cost of Big-Data analytics. However, customers with these use cases need a way to more easily and reliably target Availability Zones.

AWS 69

AWS Storage Cloud Big Data 69

VoltDB

MARCH 13, 2024

In the age of big-data-turned-massive-data, maintaining high availability , aka ultra-reliability, aka ‘uptime’, has become “paramount”, to use a ChatGPT word. Even if it’s only 10 seconds, that’s a 10-second backlog that will have to be processed when ‘normal service has resumed’. What you own, you control.

Availability 52

Availability Latency DevOps Systems 52

The Netflix TechBlog

NOVEMBER 3, 2023

Democratizing Stream Processing @ Netflix By Guil Pires , Mark Cho , Mingliang Liu , Sujay Jain Data powers much of what we do at Netflix. On the Data Platform team, we build the infrastructure used across the company to process data at scale.

Processing 271

Processing Engineering Infrastructure Latency 271

Dynatrace

JUNE 26, 2024

While measuring app response time under different circumstances provides a latency value, for example, it doesn’t tell you why the app is slow, fast, or somewhere in between. Democratizing data consumption Democratizing data consumption means making data available and accessible. Put simply, context is king.

Analytics 246

Analytics Infrastructure Metrics Efficiency 246

The Netflix TechBlog

OCTOBER 14, 2024

Workbench is a remote development workspace based on Titus that allows data practitioners to work with big data and machine learning use cases at scale. This document details the intriguing process of debugging this issue, all the way from the UI down to the Linux kernel. The input to stdin is sent to the backend (i.e.,

Latency 214

Latency Virtualization Traffic Processing 214

Testsigma

FEBRUARY 12, 2021

The implementation of emerging technologies has helped improve the process of software development, testing, design and deployment. With all of these processes in place, cost optimization is also a high concern for organizations worldwide. Dominance of Robotic Process Automation. Hyperautomation. The most recent 2021 trend.

Artificial Intelligence 92

Artificial Intelligence Software Software IoT 92

VoltDB

OCTOBER 17, 2024

What Makes the Automotive Industry Ripe for Real-Time Data Decisioning? The automotive industry is characterized by complex supply chains, intricate production processes, and stringent quality requirements. Production Optimization Optimizing production processes is essential for improving efficiency and reducing costs.

Automotive 52

Automotive IoT Energy Artificial Intelligence 52

Adrian Cockcroft

NOVEMBER 18, 2024

Damian Wylie, Head of Product, Wherobots SUS201 | Data-driven sustainability with AWS Many AWS customers are working through core sustainability challenges such as reducing emissions, optimizing supply chains, and reducing waste.

AWS 98

AWS Energy Lambda Government 98