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 157

Big Data Government Open Source Storage 157

The Netflix TechBlog

JANUARY 13, 2022

This blog will explore these two systems and how they perform auto-diagnosis and remediation across our Big Data Platform and Real-time infrastructure. In the future, we are looking to automate this process. The streaming platform recently added Data Mesh , and we need to expand Streaming Pensive to cover that.

Big Data 238

Big Data Infrastructure Metrics Hardware 238

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. Moreover, its petabyte scale also brings unique engineering challenges.

Data Engineering 173

Data Engineering Engineering Big Data Software Engineering 173

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

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 201

Scalability Big Data Hardware Internet 201

The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. In this way, no human intervention is required in the remediation process. Multi-objective optimizations. user name).

Tuning 210

Tuning Efficiency Big Data Engineering 210

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

JULY 6, 2022

At its most basic, automating IT processes works by executing scripts or procedures either on a schedule or in response to particular events, such as checking a file into a code repository. Adding AIOps to automation processes makes the volume of data that applications and multicloud environments generate much less overwhelming.

Artificial Intelligence 189

Artificial Intelligence Tuning Strategy Big Data 189

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. Performance. So, what is ITOps? What is ITOps?

Artificial Intelligence 202

Artificial Intelligence DevOps Hardware Virtualization 202

DZone

DECEMBER 21, 2020

Setting up a data warehouse is the first step towards fully utilizing big data analysis. Still, it is one of many that need to be taken before you can generate value from the data you gather. An important step in that chain of the process is data modeling and transformation.

Big Data 189

Big Data Google Scalability Processing 189

The Netflix TechBlog

MAY 4, 2023

The first phase involves validating functional correctness, scalability, and performance concerns and ensuring the new systems’ resilience before the migration. These include Quality-of-Experience(QoE) measurements at the customer device level, Service-Level-Agreements (SLAs), and business-level Key-Performance-Indicators(KPIs).

Traffic 339

Traffic Latency Tuning Systems 339

Scalegrid

MAY 13, 2020

Greenplum Database is a massively parallel processing (MPP) SQL database that is built and based on PostgreSQL. It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data.

Big Data 321

Big Data Database Artificial Intelligence Open Source 321

Dynatrace

JULY 6, 2020

Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. Effortlessly optimize Azure database performance. Database-service views provide all the metrics you need to set up high-performance database services. Azure Front Door.

Azure 148

Azure Cloud Big Data Virtualization 148

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. These limitations create an opportunity for real-time device tracking to fill the gap.

IoT 78

IoT Analytics Big Data Architecture 78

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.

Infrastructure 186

Infrastructure Open Source Operating System Cloud 186

DZone

JUNE 4, 2019

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. NVMe Storage Use Cases. There are several AI/ML focused use cases to highlight.

Storage 100

Storage FinTech Artificial Intelligence Performance 100

Dynatrace

APRIL 11, 2023

Today’s organizations face increasing pressure to keep their cloud-based applications performing and secure. As data from different corners of the enterprise proliferates, teams need a better way to bring data together to identify performance and security issues, minimize security risk, and drive greater business value.

Cloud 187

Cloud DevOps Open Source Retail 187

Dynatrace

JANUARY 16, 2023

The study analyzes factual Kubernetes production data from thousands of organizations worldwide that are using the Dynatrace Software Intelligence Platform to keep their Kubernetes clusters secure, healthy, and high performing. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.

Open Source 343

Open Source Java Operating System Programming 343

Scalegrid

NOVEMBER 25, 2019

ScyllaDB offers significantly lower latency which allows you to process a high volume of data with minimal delay. In fact, according to ScyllaDB’s performance benchmark report, their 99.9 So this type of performance has to come at a cost, right? percentile latency is up to 11X better than Cassandra on AWS EC2 bare metal.

Big Data 187

Big Data Database Open Source Azure 187

The Netflix TechBlog

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

Processing 210

Processing Big Data Efficiency Engineering 210

Rigor

SEPTEMBER 11, 2019

Reading time 16 min Whether you’re a web performance expert, an evangelist for the culture of performance, a web engineer incorporating performance into your process, or someone new to the web performance entirely, you probably identify as curious, excited about new ideas, and always learning. Rick Byers.

Performance 89

Performance Education Google Website 89

The Netflix TechBlog

JUNE 7, 2021

At much less than 1% of CPU and memory on the instance, this highly performant sidecar provides flow data at scale for network insight. Berkeley Packet Filter (BPF) is an in-kernel execution engine that processes a virtual instruction set, and has been extended as eBPF for providing a safe way to extend kernel functionality.

Network 325

Network Transportation AWS Cloud 325

Percona

JANUARY 19, 2023

While the technologies have evolved and matured enough, there are still some people thinking that MySQL is only for small projects or that it can’t perform well with large tables. With disks being faster nowadays and CPU and memory resources being cheaper, we could easily say MySQL can handle TBs of data with good performance.

Open Source 87

Open Source Storage Database Big Data 87

The Netflix TechBlog

OCTOBER 15, 2019

Delta is an eventual consistent, event driven, data synchronization and enrichment platform. Existing Solutions Dual Writes In order to keep two datastores in sync, one could perform a dual write, which is executing a write to one datastore following a second write to the other.

Transportation 192

Transportation Architecture Processing Storage 192

High Scalability

APRIL 14, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc.

Education 105

Education Software Engineering Scalability Engineering 105

High Scalability

MAY 12, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc.

Education 102

Education Software Engineering Scalability Engineering 102

High Scalability

MARCH 24, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc.

Education 105

Education Software Engineering Engineering Big Data 105

High Scalability

APRIL 28, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc.

Education 102

Education Software Engineering Scalability Engineering 102

High Scalability

MARCH 30, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc.

Education 105

Education Software Engineering Engineering Big Data 105

High Scalability

MAY 26, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). You will be designing and implementing distributed systems : large-scale web crawling platform, integrating Deep Learning based web data extraction components, working on queue algorithms, large datasets, creating a development platform for other company departments, etc.

Education 100

Education AWS Software Engineering Scalability 100

Testsigma

MAY 14, 2021

It provides better and simple disaster recovery because the process is automated. What are the tools that will be required to perform cloud testing? When we decide to start cloud-based testing in a project then we need to decide how we are going to manage the whole process of cloud-based testing. What risks should be considered?

Cloud 67

Cloud Testing Testing Tools Internet 67

Testsigma

MAY 26, 2021

It is a classic scenario where we require data-driven testing(DDT) to perform thorough testing on the input data. DDT needs to be performed for negative and positive test cases as depicted in the table below: Username value Password value Valid Valid Valid Invalid Invalid Valid Invalid Invalid Valid NULL NULL Valid NULL NULL.

Testing 70

Testing Healthcare Performance Testing Website 70

ScaleOut Software

NOVEMBER 24, 2020

The volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data. At the same time, telemetry snapshots are stored in a data lake, such as HDFS , for offline batch analysis and visualization using big data tools like Spark.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Scalegrid

FEBRUARY 8, 2024

These elements work together to spread data over several locations physically distributed, possibly extending across different data centers while optimizing available storage resources. This process effectively duplicates essential parts of information to safeguard against potential loss.

Storage 130

Storage Systems Big Data Azure 130

ScaleOut Software

JUNE 15, 2020

Instead, most applications just sift through the telemetry for patterns that might indicate exceptional conditions and forward the bulk of incoming messages to a data lake for offline scrubbing with a big data tool such as Spark. Maintain State Information for Each Data Source. The list goes on.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

JUNE 15, 2020

Instead, most applications just sift through the telemetry for patterns that might indicate exceptional conditions and forward the bulk of incoming messages to a data lake for offline scrubbing with a big data tool such as Spark. Maintain State Information for Each Data Source. The list goes on.

Analytics 52

Analytics IoT Lambda Big Data 52

Dynatrace

MAY 1, 2023

IT operations analytics is the process of unifying, storing, and contextually analyzing operational data to understand the health of applications, infrastructure, and environments and streamline everyday operations. This enables AIOps teams to better predict performance and security issues and improve overall IT operations.

Analytics 188

Analytics Artificial Intelligence Big Data Open Source 188