DZone

FEBRUARY 23, 2023

Introduction With big data streaming platform and event ingestion service Azure Event Hubs , millions of events can be received and processed in a single second. Any real-time analytics provider or batching/storage adaptor can transform and store data supplied to an event hub.

Azure 306

Azure Big Data Storage Analytics 306

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

Uber Engineering

OCTOBER 17, 2018

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

Big Data 109

Big Data Latency Transportation Traffic 109

DZone

JUNE 3, 2024

This is especially the case when it comes to taking advantage of vast amounts of data stored in cloud platforms like Amazon S3 - Simple Storage Service, which has become a central repository of data types ranging from the content of web applications to big data analytics.

Big Data 278

Big Data AWS Storage Analytics 278

Uber Engineering

AUGUST 3, 2018

From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data.

Big Data 110

Big Data Transportation Engineering Storage 110

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

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. Driving Storage Costs Down for AWS Customers. Comments (). At werner.ly

Big Data 91

Big Data Analytics AWS Cloud 91

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. Storage provisioning.

Big Data 63

Big Data Storage Benchmarking Hardware 63

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. You can learn more about it from my talk at the Flink forward conference.

Data Engineering 194

Data Engineering Engineering Big Data Software Engineering 194

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. Cloud storage monitoring. What is cloud monitoring? Virtual machine (VM) monitoring.

Cloud 295

Cloud Monitoring Best Practices Infrastructure 295

All Things Distributed

AUGUST 20, 2012

Managing Cold Storage with Amazon Glacier. With the introduction of Amazon Glacier , IT organizations now have a solution that removes the headaches of digital archiving and provides extremely low cost storage. With Amazon Glacier any organization now has access to the same data archiving capabilities as the worldâ??s

Storage 113

Storage Cloud AWS Media 113

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 262

Latency Storage Big Data Tuning 262

DZone

JULY 29, 2019

These processes are only possible with a distributed architecture and parallel processing mechanisms that Big Data tools are based on. One of the top trending open-source data storage that responds to most of the use cases is Elasticsearch.

Big Data 162

Big Data Government Open Source Storage 162

DZone

JANUARY 20, 2023

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. Having a distributed and scalable graph database system is highly sought after in many enterprise scenarios.

Scalability 210

Scalability Big Data Hardware Internet 210

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

DZone

JUNE 3, 2019

Normally, GPU nodes don't have much room for SSDs, which limits the opportunity to train very deep neural networks that need more data. For example, one well-respected vendor's standard solution is limited to 7.5TB of internal storage, and it can only scale to 30TB.

Storage 100

Storage Performance Network Scalability 100

Dynatrace

APRIL 20, 2023

“Logs magnify these issues by far due to their volatile structure, the massive storage needed to process them, and due to potential gold hidden in their content,” Pawlowski said, highlighting the importance of log analysis. “The weakness of a data lake is they fail when you need to access them fast,” Pawlowski said.

Analytics 246

Analytics Infrastructure Storage Retail 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. Note: The survey excluded all commercial observability offerings, including Dynatrace.

Open Source 358

Open Source Java Programming Operating System 358

Dynatrace

MARCH 24, 2023

Problems include provisioning and deployment; load balancing; securing interactions between containers; configuration and allocation of resources such as networking and storage; and deprovisioning containers that are no longer needed. How does container orchestration work?

Infrastructure 246

Infrastructure Open Source Operating System Cloud 246

DZone

MAY 30, 2019

With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous. This has given rise to a completely new set of computing workloads for Machine Learning which drives Artificial Intelligence applications.

Artificial Intelligence 100

Artificial Intelligence Social Media FinTech Storage 100

The Netflix TechBlog

JUNE 20, 2024

In this talk, Jessica Larson shares her takeaways from building a new data platform post-GDPR. To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.”

Data Engineering 219

Data Engineering Engineering Entertainment Software Engineering 219

Dynatrace

DECEMBER 15, 2022

Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure. AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

Uber Engineering

APRIL 5, 2018

Three years ago, Uber Engineering adopted Hadoop as the storage ( HDFS ) and compute ( YARN ) infrastructure for our organization’s big data analysis.

Systems 109

Systems Big Data Storage Infrastructure 109

DZone

MAY 25, 2024

As NetEase expands its business horizons, the logs and time series data it receives explode, and problems like surging storage costs and declining stability come. As NetEase's pick among all big data components for platform upgrades, Apache Doris fits into both scenarios and brings much faster query performance.

Best Practices 130

Best Practices Big Data Games Storage 130

All Things Distributed

MAY 18, 2010

Expanding the Cloud - Amazon S3 Reduced Redundancy Storage. Today a new storage option for Amazon S3 has been launched: Amazon S3 Reduced Redundancy Storage (RRS). This new storage option enables customers to reduce their costs by storing non-critical, reproducible data at lower levels of redundancy. Comments ().

Storage 64

Storage Cloud AWS Scalability 64

The Netflix TechBlog

MAY 4, 2023

Given the scale of the data being generated using replay traffic, we record the responses from the two sides to a cost-effective cold storage facility using technology like Apache Iceberg. This summary provides an excellent high-level view of the analysis and the overall match rate across the production and replay paths.

Traffic 356

Traffic Latency Tuning Systems 356

Dynatrace

FEBRUARY 16, 2023

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

Analytics 246

Analytics Innovation Metrics Database 246

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. Fraud.net is a good example of this.

AWS 153

AWS Cloud Artificial Intelligence IoT 153

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

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

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 Storage Database IoT 147

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 108

Servers Social Media AWS Storage 108

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. Perhaps the greatest benefit of an unordered Key-Value data model is that entries can be partitioned across multiple servers by just hashing the key.

Database 279

Database Ecommerce Efficiency Engineering 279

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

All Things Distributed

OCTOBER 7, 2015

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift. Big data challenges.

Cloud 111

Cloud Big Data AWS Analytics 111

All Things Distributed

MARCH 28, 2011

The scalability, reliability and durability requirements for Cloud Drive are very high which is why they decided to make use of the Amazon Simple Storage Service (S3) as the core component of their service. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway. At werner.ly Syndication.

AWS 120

AWS Cloud Storage Internet 120