Scalegrid

FEBRUARY 8, 2024

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

OCTOBER 4, 2022

A data lakehouse features the flexibility and cost-efficiency of a data lake with the contextual and high-speed querying capabilities of a data warehouse. Data warehouses offer a single storage repository for structured data and provide a source of truth for organizations. How does a data lakehouse work?

Artificial Intelligence 246

Artificial Intelligence Storage Analytics Government 246

Dynatrace

OCTOBER 4, 2022

Teams have introduced workarounds to reduce storage costs. Additionally, efforts such as lowered data retention times, two-tiered storage systems, shaky index management, sampled data, and data pipelines reduce the overall amount of stored data. Dynatrace discovers logs automatically at scale.

Analytics 264

Analytics Artificial Intelligence Storage Serverless 264

Dynatrace

JANUARY 11, 2023

As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. Website monitoring examines a cloud-hosted website’s processes, traffic, availability, and resource use. Cloud storage monitoring.

Cloud 295

Cloud Monitoring Best Practices Infrastructure 295

The Netflix TechBlog

OCTOBER 27, 2020

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 252

Latency Storage Big Data Tuning 252

DZone

JANUARY 20, 2023

There is a countless number of enterprises, particularly Internet giants, that have explored ways to make graph data processing scalable. 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.

Scalability 210

Scalability Big Data Hardware Internet 210

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

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. However, this cannot be done without efficient, scalable data analytics.

Big Data 92

Big Data Analytics AWS Cloud 92

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. What is your favorite project?

Data Engineering 181

Data Engineering Engineering Big Data Software Engineering 181

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

Dynatrace

APRIL 20, 2023

-based financial services group, discussed how the bank uses log monitoring on the Dynatrace platform with an emphasis on observability and security data. To grasp the challenges of multifeatured, cross-team cooperation dealing with observability data, consider the content of the logs generated. Dissolving data silos.

Analytics 246

Analytics Infrastructure Storage Architecture 246

DZone

JUNE 4, 2019

NVMe Storage Use Cases. NVMe storage's strong performance, combined with the capacity and data availability benefits of shared NVMe storage over local SSD, makes it a strong solution for AI/ML infrastructures of any size. There are several AI/ML focused use cases to highlight.

Storage 100

Storage Artificial Intelligence FinTech Performance 100

Dynatrace

MARCH 24, 2023

Container orchestration is a process that automates the deployment and management of containerized applications and services at scale. Container orchestration enables organizations to manage and automate the many processes and services that comprise workflows. How does container orchestration work?

Infrastructure 246

Infrastructure Open Source Operating System Cloud 246

Dynatrace

JANUARY 16, 2023

Redis is an in-memory key-value store and cache that simplifies processing, storage, and interaction with data in Kubernetes environments. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch. Databases : Among databases, Redis is the most used at 60%.

Open Source 358

Open Source Java Operating System Programming 358

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? ITOps vs. AIOps.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

Dynatrace

FEBRUARY 16, 2023

The goal is to turn more data into insights so the whole organization can make data-driven decisions and automate processes. 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.

Analytics 246

Analytics Innovation Metrics Database 246

The Netflix TechBlog

MAY 4, 2023

Traffic Duplication and Correlation: The initial step requires the implementation of a mechanism to clone and fork production traffic to the newly established pathway, along with a process to record and correlate responses from the original and alternative routes.

Traffic 347

Traffic Latency Tuning Systems 347

The Netflix TechBlog

JUNE 20, 2024

In this talk, Jessica Larson shares her takeaways from building a new data platform post-GDPR. Data Productivity at Scale Recording Speaker : Iaroslav Zeigerman (Co-Founder and Chief Architect at Tobiko Data) Summary : The development and evolution of data pipelines are hindered by outdated tooling compared to software development.

Data Engineering 206

Data Engineering Engineering Entertainment Software Engineering 206

Highly Scalable

MARCH 1, 2012

And this was where a new evolution of data models began: Key-Value storage is a very simplistic, but very powerful model. One of the most significant shortcomings of the Key-Value model is a poor applicability to cases that require processing of key ranges. Data duplication and denormalization are first-class citizens.

Database 279

Database Ecommerce Efficiency Engineering 279

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.

AWS 154

AWS Cloud Artificial Intelligence IoT 154

Scalegrid

FEBRUARY 11, 2025

This article will help you understand the core differences in data structure, scalability, and use cases. Whether you need a relational database for complex transactions or a NoSQL database for flexible data storage, weve got you covered. This allows for precise data manipulation and retrieval.

Scalability 147

Scalability Database Storage IoT 147

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

All Things Distributed

FEBRUARY 17, 2011

Since a few days ago this weblog serves 100% of its content directly out of the Amazon Simple Storage Service (S3) without the need for a web server to be involved. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway. Driving down the cost of Big-Data analytics. Comments ().

AWS 120

AWS Website Storage Servers 120

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

All Things Distributed

OCTOBER 7, 2015

In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,

Cloud 112

Cloud Big Data AWS Analytics 112

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

All Things Distributed

APRIL 27, 2011

The storage systems weve pioneered demonstrate extreme scalability while maintaining tight control over performance, availability, and cost. For example, our Simple Storage Service, Elastic Block Store, and SimpleDB all derive their basic architecture from unique Amazon technologies. Driving Storage Costs Down for AWS Customers.

Technology 127

Technology Technology AWS Storage 127

Scalegrid

JUNE 28, 2024

It is widely utilized across various industries, such as finance, telecommunications, and e-commerce, for managing activities, including transaction processing, data streaming, and instantaneous messaging. Key Takeaways RabbitMQ is an open-source message broker facilitating seamless data exchange across diverse systems.

IoT 130

IoT Healthcare Programming Open Source 130

O'Reilly

SEPTEMBER 19, 2023

It progressed from “raw compute and storage” to “reimplementing key services in push-button fashion” to “becoming the backbone of AI work”—all under the umbrella of “renting time and storage on someone else’s computers.” ” (It will be easier to fit in the overhead storage.)

Hardware 134

Hardware Storage Big Data Blockchain 134

All Things Distributed

AUGUST 17, 2011

As some of you may remember I was pretty excited when Amazon Simple Storage Service (S3) released its website feature such that I could serve this weblog completely from S3. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway. Driving down the cost of Big-Data analytics.

Servers 109

Servers Social Media AWS Website 109

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. The best they can usually do in real-time using general purpose tools is to filter and look for patterns of interest.

IoT 78

IoT Big Data Analytics Architecture 78

Scalegrid

MARCH 28, 2024

With these goals in mind, two in-memory data stores, Redis and Memcached, have emerged as the top contenders. This article will explore how they handle data storage and scalability, perform in different scenarios, and, most importantly, how these factors influence your choice. Data transfer technology. 3d render.

Cache 130

Cache Storage Architecture Scalability 130

All Things Distributed

MARCH 28, 2011

The methods for accessing these objects is also rapidly changing; where in the past you needed a PC or a Laptop to access these objects, now many of our electronic devices have become capable of processing them. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway. At werner.ly Syndication.

AWS 121

AWS Cloud Storage Internet 121

All Things Distributed

DECEMBER 8, 2016

It adopted Amazon Redshift, Amazon EMR and AWS Lambda to power its data warehouse, big data, and data science applications, supporting the development of product features at a fraction of the cost of competing solutions. Kik Interactive is a Canadian chat platform with hundreds of millions of users around the globe.

AWS 128

AWS Cloud Lambda Innovation 128

All Things Distributed

AUGUST 16, 2011

ITAR is the International Traffic in Arms Regulatory framework which stipulates for example that data must be stored in an environment where physical and logical access is restricted to US Persons. 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 14, 2024

Defining Hybrid Cloud Strategy The decision-making process about where to situate data and applications is vital to any hybrid cloud solution. This consistency aids not only in application deployment but also simplifies scaling processes. We will examine each of these elements in more detail.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

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. Because of its focus on latency, the generic CPU yielded rather inefficient system for graphics processing.

AWS 125

AWS Programming Latency Architecture 125