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.

Cache 130

Cache Storage Scalability Architecture 130

The Netflix TechBlog

OCTOBER 27, 2020

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. 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.

Latency 243

Latency Storage Big Data Tuning 243

Dynatrace

MARCH 24, 2023

Container technology enables organizations to efficiently develop cloud-native applications or to modernize legacy applications to take advantage of cloud services. Docker Swarm First introduced in 2014 by Docker, Docker Swarm is an orchestration engine that popularized the use of containers with developers.

Infrastructure 191

Infrastructure Open Source Operating System Cloud 191

Scalegrid

MARCH 14, 2024

In practice, a hybrid cloud operates by melding resources and services from multiple computing environments, which necessitates effective coordination, orchestration, and integration to work efficiently. Tailoring resource allocation efficiently ensures faster application performance in alignment with organizational demands.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

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 111

Big Data Analytics AWS Cloud 111

Dynatrace

DECEMBER 15, 2022

Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure. Adding application security to development and operations workflows increases efficiency. The primary goal of ITOps is to provide a high-performing, consistent IT environment.

Artificial Intelligence 208

Artificial Intelligence DevOps Hardware Virtualization 208

Percona

JANUARY 19, 2023

For instance, in Percona Managed Services , we have many clients with TBs worth of data that are well performant. In this blog post, we will review key topics to consider for managing large datasets more efficiently in MySQL. InnoDB will sort the data in primary key order, and that will serve to reference actual data pages on disk.

Open Source 85

Open Source Storage Database Big Data 85

The Morning Paper

OCTOBER 26, 2020

On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. PVLDB’20.

Cloud 104

Cloud Big Data Latency Architecture 104

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 Analytics Big Data Architecture 78

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 97

Hardware Storage Big Data Blockchain 97

All Things Distributed

JUNE 26, 2016

AdiMap uses Amazon Kinesis to process real-time streaming online ad data and job feeds, and processes them for storage in petabyte-scale Amazon Redshift. Advanced problem solving that connects big data with machine learning. A workflow engine to drive business decisions. We want you to start using it today.

AWS 90

AWS Cloud Healthcare Blockchain 90

Abhishek Tiwari

DECEMBER 27, 2017

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. Delta implements the unified data management layer by extending the Amazon S3 object storage for ACID transactions and automatic data indexing.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

Highly Scalable

MARCH 1, 2012

The following figure depicts imaginary “evolution” of the major NoSQL system families, namely, Key-Value stores, BigTable-style databases, Document databases, Full Text Search Engines, and Graph databases: NoSQL Data Models. Full Text Search Engines: Apache Lucene, Apache Solr. Document Databases: MongoDB, CouchDB.

Database 279

Database Ecommerce Efficiency Engineering 279

The Morning Paper

SEPTEMBER 19, 2019

AliGraph covers Alibaba’s distributed graph engine supporting the development of new GNN applications. Their dataset has about 7B edges… Meanwhile, AnalyticDB is Alibaba’s real-time OLAP RDBMS handling 10PB of data (in excess of 100 trillion rows!). Autoscaling tiered cloud storage in Anna. Research papers. (In

Blockchain 46

Blockchain Hardware Google Analytics 46

Testsigma

FEBRUARY 12, 2021

The usage by advanced techniques such as RPA, Artificial Intelligence, machine learning and process mining is a hyper-automated application that improves employees and automates operations in a way which is considerably more efficient than conventional automation. Gartner’s 2020 projections first included the trend of hyperautomation.

Artificial Intelligence 92

Artificial Intelligence Software Software IoT 92

All Things Distributed

DECEMBER 12, 2018

Winning in this race requires that we become much more customer oriented, much more efficient in all of our operations, and at the same time shift our culture towards more lean and experimental. The first platform is a real time, big data platform being used for analyzing traffic usage patterns to identify congestion and connectivity issues.

AWS 124

AWS Cloud Games Serverless 124

Testsigma

JULY 14, 2020

The expected output is also entered in the test data sheet or file. Test data storage can be achieved by any of the below options-. Tools/ frameworks for data-driven automation testing-. Time-efficient. Excel files. CSV files. XML files. Database tables. Text files. Broader test-coverage.

Testing 63

Testing Artificial Intelligence DevOps Big Data 63

All Things Distributed

NOVEMBER 15, 2010

Now that our ability to generate higher and higher clock rates has stalled and CPU architectural improvements have shifted focus towards multiple cores, we see that it is becoming harder to efficiently use these computer systems. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway.

AWS 136

AWS Latency Programming Architecture 136

Percona

JANUARY 14, 2019

However, ClickHouse is super efficient for timeseries and provides “sharding” out of the box (scalability beyond one node). Although such databases can be very efficient with counts and averages, some queries will be slow or simply non existent. Inserts are efficient for bulk inserts only. Processed 8.19

Database 40

Database Analytics Blockchain Healthcare 40

ACM Sigarch

NOVEMBER 1, 2019

As is the case for many high-quality computer systems conferences, the papers presented here involve a significant amount of engineering and experimentation on real hardware to convincingly evaluate innovative concepts end-to-end in a realistic setting. ATC ’19 was refreshingly different. Heterogeneous ISA. Programmable I/O Devices.

Architecture 12

Architecture Hardware Cache Storage 12