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. For example, observability analytics can provide data on current infrastructure usage and any bottlenecks or performance problems. Predictive analysis.

Analytics 246

Analytics Infrastructure Metrics Efficiency 246

All Things Distributed

SEPTEMBER 29, 2016

Based in the Paris area, the region will provide even lower latency and will allow users who want to store their content in datacenters in France to easily do so. Today, I am very excited to announce our plans to open a new AWS Region in France! The new region in France will be ready for customers to use in 2017.

AWS 158

AWS IoT Internet Internet 158

The Morning Paper

OCTOBER 26, 2020

As a production system within Microsoft capturing around a quadrillion events and indexing 16 trillion search keys per day it would be interesting in its own right, but there’s a lot more to it than that. It makes heavy use of comparatively expensive data-center computing facilities. Emphasis mine ). Emphasis mine ).

Cloud 104

Cloud Big Data Latency Architecture 104

The Netflix TechBlog

NOVEMBER 20, 2023

Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. Backfill: Backfilling datasets is a common operation in big data processing. append, overwrite, etc.).

Processing 219

Processing Big Data Efficiency Engineering 219

All Things Distributed

JUNE 20, 2017

This enables customers to serve content to their end users with low latency, giving them the best application experience. In 2008, AWS opened a point of presence (PoP) in Hong Kong to enable customers to serve content to their end users with low latency. Since then, AWS has added two more PoPs in Hong Kong, the latest in 2016.

AWS 126

AWS Logistics Cloud Social Media 126

Highly Scalable

MAY 1, 2012

Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. what is the cardinality of the data set)?

Analytics 191

Analytics Traffic Big Data Efficiency 191

The Netflix TechBlog

NOVEMBER 3, 2023

Additionally, instead of implementing business logic by composing multiple individual Processors together, users could express their logic in a single SQL query, avoiding the additional resource and latency overhead that came from multiple Flink jobs and Kafka topics.

Processing 271

Processing Engineering Infrastructure Latency 271

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., on end-to-end latency) and less than 0.15% on throughput. This tracing system is similar to Dapper and Zipkin and records per-microservice latencies and number of outstanding requests. ASPLOS’19.

Big Data 45

Big Data Cloud Performance Hardware 45

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

Scalegrid

MARCH 28, 2024

On the other hand, an append-only file ensures data safety by recording every write operation that modifies the dataset, allowing for complete data reconstruction in the event of a restart. Advanced Redis Features Showdown Big data center concept, cloud database, server power station of the future.

Cache 130

Cache Storage Scalability Architecture 130

The Morning Paper

JUNE 23, 2019

A high CPU cost due to marshalling data to/from the RInK store formats to the application data format. In ProtoCache (a component of a widely used Google application), 27% of its latency when using a traditional S+RInK design came from marshalling/un-marshalling. Fetching too much data in a single query (i.e.,

Cache 79

Cache Latency Google Network 79

Abhishek Tiwari

DECEMBER 27, 2017

A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. 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

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. Maintain control This may sound a bit crazy, but if you’re going to own the latency/availability SLA, then you need to ‘own’ as much of the call path as possible.

Availability 52

Availability Latency DevOps Systems 52

Scalegrid

NOVEMBER 25, 2019

ScyllaDB offers significantly lower latency which allows you to process a high volume of data with minimal delay. percentile latency is up to 11X better than Cassandra on AWS EC2 bare metal. So what are some of the reasons why users would pick ScyllaDB vs. Cassandra? So this type of performance has to come at a cost, right?

Big Data 187

Big Data Database Open Source Azure 187

The Netflix TechBlog

OCTOBER 14, 2024

Overview At Netflix, the Analytics and Developer Experience organization, part of the Data Platform, offers a product called Workbench. 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. We then exported the .har

Latency 214

Latency Virtualization Traffic Processing 214

VoltDB

OCTOBER 17, 2024

Respond to disruptions: Supply chain disruptions, such as natural disasters or geopolitical events, can have a significant impact on production. Artificial Intelligence (AI) and Machine Learning (ML) AI and ML algorithms analyze real-time data to identify patterns, predict outcomes, and recommend actions.

Automotive 52

Automotive IoT Energy Artificial Intelligence 52

Adrian Cockcroft

NOVEMBER 18, 2024

Discover how their solution saves customers hours of manual effort by automating the analysis of tens of thousands of documents to better manage investor events, report internally to executive teams, and find new investors to target. After re:Invent, I will update this post with the videos from the event, as I did last year.

AWS 98

AWS Energy Lambda Government 98