High Scalability

JANUARY 11, 2022

There are two major processes which gets executed when a user posts a photo on Instagram. Firstly, the synchronous process which is responsible for uploading image content on file storage, persisting the media metadata in graph data-storage, returning the confirmation message to the user and triggering the process to update the user activity.

Design 334

Design Media Storage Logistics 334

ScaleOut Software

JULY 14, 2021

Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.

IoT 52

IoT Azure Analytics Serverless 52

Scalegrid

MARCH 28, 2024

This article will explore how they handle data storage and scalability, perform in different scenarios, and, most importantly, how these factors influence your choice. It uses a hash table to manage these pairs, divided into fixed-size buckets with linked lists for key-value storage.

Cache 130

Cache Storage Scalability Architecture 130

The Morning Paper

SEPTEMBER 10, 2019

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Anchored in the primary use case of supporting Google’s YouTube business, what we’re looking at here could well be the future of data processing at Google. Because they had too many data processing systems! ;). VLDB’19. are divided.

Analytics 75

Analytics Latency Cache Google 75

Dynatrace

JANUARY 31, 2024

million” – Gartner Data observability is a practice that helps organizations understand the full lifecycle of data, from ingestion to storage and usage, to ensure data health and reliability. Data observability is crucial to analytics and automation, as business decisions and actions depend on data quality.

DevOps 198

DevOps Analytics Airlines Metrics 198

Scalegrid

JANUARY 8, 2024

Key Takeaways AI integration in cloud computing increases operational efficiency by automating processes, optimizing resource allocation, and improving scalability, leading to cost savings and allowing IT teams to concentrate on strategic initiatives. Key among these trends is the emphasis on security and intelligent analytics.

Artificial Intelligence 147

Artificial Intelligence Cloud Scalability Analytics 147

Scalegrid

JANUARY 12, 2024

In the realm of cloud-based business operations, there is an increasing dependence on complex information processing patterns. Storage is a critical aspect to consider when working with cloud workloads. There are numerous choices available for deploying these workloads on various cloud provider platforms that offer unique capabilities.

Cloud 130

Cloud Virtualization Storage Efficiency 130

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. How Bulldozer leverages Spark, Protobuf and KV DAL for moving the data.

Latency 243

Latency Storage Big Data Tuning 243

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. We will examine each of these elements in more detail. A pivotal element of any hybrid cloud strategy is adhering to regulatory requirements and compliance standards specific to an industry.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

The Netflix TechBlog

OCTOBER 19, 2020

Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. The process started with manual pull of member account information that was part of the session.

Infrastructure 233

Infrastructure Transportation Storage Open Source 233

Dynatrace

SEPTEMBER 13, 2023

This is where unified observability and Dynatrace Automations can help by leveraging causal AI and analytics to drive intelligent automation across your multicloud ecosystem. This process enables you to continuously evaluate software against predefined quality criteria and service level objectives (SLOs) in pre-production environments.

AWS 252

AWS Efficiency Azure Cloud 252

Dynatrace

DECEMBER 15, 2022

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. Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure.

Artificial Intelligence 208

Artificial Intelligence DevOps Hardware Virtualization 208

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

Dynatrace

APRIL 26, 2022

STM generates traffic that replicates the typical path or behavior of a user on a network to measure performance for example, response times, availability, packet loss, latency, jitter, and other variables). One use case for STM is to model the behavior of a customer in the form of a flow of transactions along the buyer’s journey.

Monitoring 209

Monitoring Social Media IoT Metrics 209

The Netflix TechBlog

SEPTEMBER 2, 2020

When a problem occurs, we put on our detective hats and start our mystery-solving process by gathering evidence. This difference has substantial technological implications, from the classification of what’s interesting to transport to cost-effective storage (keep an eye out for later Netflix Tech Blog posts addressing these topics).

Latency 296

Latency Transportation Engineering Traffic 296

Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. Fault-tolerance. Interoperability with Hadoop.

Big Data 154

Big Data Processing Lambda Database 154

All Things Distributed

OCTOBER 16, 2019

Because these IoT devices are powered by microprocessors or microcontrollers that have limited processing power and memory, they often rely heavily on AWS and the cloud for processing, analytics, storage, and machine learning. In other words, process the data closer to where it's created.

IoT 126

IoT Healthcare Internet Internet 126

Percona

JUNE 22, 2023

Number of slow queries recorded Select types, sorts, locks, and total questions against a database Command counters and handlers used by queries give an overall traffic summary Along with this, PMM also comes with Query Analytics giving much detailed information about queries getting executed.

Performance 102

Performance Monitoring Traffic Database 102

All Things Distributed

JUNE 7, 2017

This is because data gets more valuable when it can be processed together with other data. At the same time, it can be valuable to process some data right at the source where it is generated. Some applications may also rely on timely decisions: when maneuvering heavy machinery, an absolute minimum of latency is critical.

AWS 118

AWS IoT Lambda Storage 118

All Things Distributed

JUNE 21, 2018

Use cases such as gaming, ad tech, and IoT lend themselves particularly well to the key-value data model where the access patterns require low-latency Gets/Puts for known key values. The purpose of DynamoDB is to provide consistent single-digit millisecond latency for any scale of workloads.

Database 167

Database AWS Games Latency 167

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

OCTOBER 12, 2016

Three years ago, as part of our AWS Fast Data journey we introduced Amazon ElastiCache for Redis , a fully managed in-memory data store that operates at sub-millisecond latency. This allows for faster failover times while minimizing latency. Building upon Redis. Redis and Fast Data.

Scalability 130

Scalability Analytics Cache AWS 130

Dotcom-Montior

MAY 12, 2020

Websites are now more than just the storage and retrieval of information to present content to users. A request will be sent from the client-side and an HTTP check waits on the server port to get the message, process it, and then send back the response. Network latency. Processing and generating the response. Wi-Fi usage.

Monitoring 63

Monitoring Entertainment Hardware Latency 63

ACM Sigarch

DECEMBER 6, 2018

smart cameras & analytics) to interactive/immersive environments and autonomous driving (e.g. Orchestrate the processing flow across an end-to-end infrastructure. Each of these categories opens up challenging problems in AI/visual algorithms, high-density computing, bandwidth/latency, distributed systems.

Wireless 54

Wireless IoT Analytics Architecture 54

Abhishek Tiwari

JULY 12, 2018

It can be used to power new analytics, insight, and product features. These data pipelines can process data at petabytes scale and to some extent, their success can be attributed to an army of engineers devoted to build and maintain internal data pipelines. It can be used to power new analytics, insight, and product features.

Latency 63

Latency Analytics Scalability Engineering 63

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. It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

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. billion by 2025, up 32.6

Artificial Intelligence 92

Artificial Intelligence Software Software IoT 92

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 136

AWS Latency Programming Architecture 136

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. One particular early use case for AWS GovCloud (US) will be massive data processing and analytics. Driving Storage Costs Down for AWS Customers.

AWS 111

AWS Government Big Data Cloud 111

Alex Russell

MARCH 6, 2021

A then-representative $200USD device had 4-8 slow (in-order, low-cache) cores, ~2GiB of RAM, and relatively slow MLC NAND flash storage. Advances in browser content processing. Instead, experience teaches that it's the process of taking care to attend to those least-well-off that changes outcomes. The Moto G4 , for example.

Performance 124

Performance Network Cache Metrics 124

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 118

Cloud AWS Automotive Latency 118

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 Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway. Driving down the cost of Big-Data analytics. At werner.ly

AWS 85

AWS Storage Cloud Big Data 85

Abhishek Tiwari

NOVEMBER 3, 2018

Most of the CMS vendors dodge questions of evolution by talking about incremental innovation primarily focused on customer experience (CX) such as analytics and personalisation. Alternatively, you can upload output directory to cloud object/blob storage such as Amazon S3 or Azure Blob Storage and serve your site from there.

Systems 63

Systems Cache Website Network 63

HammerDB

DECEMBER 5, 2020

For TPC-C this meant enough available spindles to reduce I/O latency and for TPC-H enough bandwidth for data throughput. This is not a new concept, as given in the description for the earlier TPC-B workload “ a transaction monitor can multiplex transaction streams to match the processing profile of the database subsystem.

C++ 40

C++ Benchmarking Virtualization Open Source 40

ScaleOut Software

MAY 4, 2014

A recent blog post highlighted a Microsoft technical report which asserts that most Hadoop workloads are 100 GB or smaller, and for almost all workloads except the very largest “a single ‘scale-up’ server can process each of these jobs and do as well or better than a cluster in terms of performance, cost, power, and server density.”

Network 40

Network Servers Analytics Storage 40

HammerDB

OCTOBER 12, 2018

As is also the case this limitation is at the database level (especially the storage engine) rather than the hardware level. InnoDB is the storage engine that will deliver the best OLTP throughput and should be chosen for this test. . maximum transition latency: Cannot determine or is not supported. .

Best Practices 40

Best Practices Scalability Performance C++ 40

Smashing Magazine

JANUARY 11, 2021

This guide has been kindly supported by our friends at LogRocket , a service that combines frontend performance monitoring , session replay, and product analytics to help you build better customer experiences. Study common complaints coming into customer service and sales team, study analytics for high bounce rates and conversion drops.

Performance 145

Performance Cache Media Metrics 145