DZone

FEBRUARY 1, 2023

Built on Azure Blob Storage, Azure Data Lake Storage Gen2 is a suite of features for big data analytics. Azure Data Lake Storage Gen1 and Azure Blob Storage's capabilities are combined in Data Lake Storage Gen2. For instance, Data Lake Storage Gen2 offers scale, file-level security, and file system semantics.

Azure 250

Azure Storage Big Data Analytics 250

The Netflix TechBlog

JANUARY 13, 2022

The data platform is built on top of several distributed systems, and due to the inherent nature of these systems, it is inevitable that these workloads run into failures periodically. We have been working on an auto-diagnosis and remediation system called Pensive in the data platform to address these concerns.

Big Data 242

Big Data Infrastructure Metrics Games 242

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

Dynatrace

JUNE 26, 2023

Software analytics offers the ability to gain and share insights from data emitted by software systems and related operational processes to develop higher-quality software faster while operating it efficiently and securely. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI.

Software 246

Software Software Analytics Big Data 246

The Netflix TechBlog

MAY 4, 2023

Behind the scenes, a myriad of systems and services are involved in orchestrating the product experience. These backend systems are consistently being evolved and optimized to meet and exceed customer and product expectations. This technique facilitates validation on multiple fronts.

Traffic 347

Traffic Latency Tuning Systems 347

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. Key challenges. Performance.

Big Data 63

Big Data Storage Benchmarking Hardware 63

DZone

JANUARY 31, 2023

Big data is at the center of all business decisions these days. It refers to large volumes of data generated through different sources, and this data then provides the foundation for business decisions. There are different ways through which we can process data. The size of data in batch processing is known.

Processing 241

Processing Big Data Systems 241

Dynatrace

JANUARY 16, 2023

As Kubernetes adoption increases and it continues to advance technologically, Kubernetes has emerged as the “operating system” of the cloud. Kubernetes is emerging as the “operating system” of the cloud. Kubernetes is emerging as the “operating system” of the cloud. Kubernetes moved to the cloud in 2022.

Open Source 358

Open Source Java Operating System Programming 358

Rigor

MAY 22, 2019

Big data is like the pollution of the information age. The Big Data Struggle and Performance Reporting. Alternatively, a number of organizations have created their own internal home-grown systems for managing and distilling web performance and monitoring data. Conclusion.

Big Data 76

Big Data Monitoring Performance Metrics 76

The Netflix TechBlog

MARCH 25, 2019

Finally, imagine yourself in the role of a data platform reliability engineer tasked with providing advanced lead time to data pipeline (ETL) owners by proactively identifying issues upstream to their ETL jobs. Let’s review a few of these principles: Ensure data integrity ?—?Accurately Enable seamless integration?—?

Infrastructure 225

Infrastructure Big Data Transportation Architecture 225

The Morning Paper

DECEMBER 13, 2020

An overview of end-to-end entity resolution for big data , Christophides et al., It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. Open source ER systems. ACM Computing Surveys, Dec. 2020, Article No. All of the discussed approaches require schemas.

Big Data 83

Big Data Open Source Processing Analytics 83

The Netflix TechBlog

OCTOBER 28, 2021

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. I developed many batch and real-time data pipelines using open source technologies for AOL Advertising and eBay. What is your favorite project?

Data Engineering 181

Data Engineering Engineering Big Data Software Engineering 181

DZone

JANUARY 20, 2023

Having a distributed and scalable graph database system is highly sought after in many enterprise scenarios. Do Not Be Misled Designing and implementing a scalable graph database system has never been a trivial task.

Scalability 210

Scalability Big Data Hardware Internet 210

Dynatrace

JULY 6, 2022

While automating IT practices can save administrators a lot of time, without AIOps, the system is only as intelligent as the humans who program it. This kind of automation can support key IT operations, such as infrastructure, digital processes, business processes, and big-data automation. Big data automation tools.

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

The Netflix TechBlog

APRIL 29, 2019

Various software systems are needed to design, build, and operate this CDN infrastructure, and a significant number of them are written in Python. The configuration of these devices is controlled by several other systems including source of truth, application of configurations to devices, and back up.

Open Source 280

Open Source Network Infrastructure Big Data 280

The Netflix TechBlog

JULY 21, 2022

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data 188

Big Data Cache Engineering Data Engineering 188

Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. Like the development and design phases, these applications generate massive data volumes that offer relevant and actionable insights.

Artificial Intelligence 325

Artificial Intelligence Cloud Innovation Strategy 325

Dynatrace

SEPTEMBER 9, 2019

I love data. I have spent virtually my entire career looking at data. Synthetic data, network data, system data, and the list goes on. As much as I love data, data is cold, it lacks emotion. I still love data, but I am starting to love emotion-filled data. Dynatrace news.

Big Data 210

Big Data Metrics Virtualization Network 210

DZone

JULY 22, 2022

Honestly, these two terms have recently been doing rounds in the big data world. Over the years, EDI has become a standard document exchange system, whereas API is on its way to becoming a popular alternative to EDI.

Big Data 188

Big Data Technology Technology Systems 188

Dynatrace

OCTOBER 25, 2022

Early implementations of NoOps were just ‘lift and shift’ efforts that replicated existing systems to the cloud. AIOps , a term coined by Gartner in 2016, combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination. Evolution of NoOps.

DevOps 246

DevOps Big Data Cloud Innovation 246

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. So, what is ITOps? Why is IT operations important?

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

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 252

Latency Storage Big Data Tuning 252

Dynatrace

MARCH 24, 2023

Containers enable developers to package microservices or applications with the libraries, configuration files, and dependencies needed to run on any infrastructure, regardless of the target system environment. This means organizations are increasingly using Kubernetes not just for running applications, but also as an operating system.

Infrastructure 246

Infrastructure Open Source Operating System Cloud 246

Dynatrace

SEPTEMBER 15, 2020

Supported technologies include cloud services, big data, databases, OS, containers, and application runtimes like the JVM. Akamas is comparing data across different experiments to identify the optimal configuration of your system. Automation is the key to optimizing our systems. via Cloud APIs or plain simple SSH).

Performance 260

Performance Java Metrics Cloud 260

The Morning Paper

SEPTEMBER 8, 2019

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., Microsoft’s big data clusters have 10s of thousands of machines, and are used by thousands of users to run some pretty complex queries. ICDE’16 (PowerDrill is a Google internal system). VLDB’19.

Big Data 57

Big Data Analytics Latency Azure 57

Dynatrace

AUGUST 10, 2021

She dispelled the myth that more big data equals better decisions, higher profits, or more customers. Investing in data is easy but using it is really hard”. The fact is, data on its own isn’t meaningful. Tricia quoted the statistic that companies typically use 3% of their data to inform decisions.

DevOps 246

DevOps Innovation Big Data Cloud 246

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 211

Java Scalability Traffic Architecture 211

Dynatrace

OCTOBER 4, 2022

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. Grail addresses today’s challenges of big data and cloud everywhere: Grail is highly scalable, cost-effective, and super-fast.

Analytics 264

Analytics Artificial Intelligence Storage Serverless 264

Dynatrace

MARCH 14, 2022

Over the past decade, the industry moved from paper-based to electronic health records (EHRs)—digitizing the backbone of patient data. As patient care continues to evolve, IT teams have accelerated this shift from legacy, on-premises systems to cloud technology to more build, test, and deploy software, and fuel healthcare innovation.

Healthcare 246

Healthcare Artificial Intelligence Innovation Strategy 246

The Netflix TechBlog

JULY 26, 2021

This happens at an unprecedented scale and introduces many interesting challenges; one of the challenges is how to provide visibility of Studio data across multiple phases and systems to facilitate operational excellence and empower decision making. The audits check for equality (i.e.

Big Data 257

Big Data Government Processing Analytics 257

Dynatrace

APRIL 19, 2023

.” Accessing business insights and data with precision and long-term context After working with Dynatrace, BCLC now has a twenty-four-seven data center team with an easy-to-share, intuitive datacenter hyper wall dashboard showing the overall health of the entire system — infrastructure, applications, networks, and user experience.

Entertainment 237

Entertainment Analytics Healthcare Games 237

The Netflix TechBlog

JUNE 20, 2024

However, as the system has increased in scale and complexity, Pensive has been facing challenges due to its limited support for operational automation, especially for handling memory configuration errors and unclassified errors. To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.”

Data Engineering 206

Data Engineering Engineering Entertainment Software Engineering 206

Dynatrace

OCTOBER 17, 2022

Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. The four stages of data processing. This process continues until the system identifies a root cause. Two types of root cause.

DevOps 246

DevOps Artificial Intelligence Healthcare Innovation 246

Dynatrace

APRIL 11, 2023

Log4Shell required many organizations to take devices and applications offline to prevent malicious attackers from gaining access to IT systems and sensitive data. As a result, organizations need to be vigilant in identifying and addressing vulnerabilities to protect their systems and data.

Cloud 246

Cloud DevOps Open Source Retail 246

Dynatrace

MARCH 11, 2021

I took a big-data-analysis approach, which started with another problem visualization. This is what I wanted to optimize and avoid and many traditional (or homegrown) systems aren’t doing this. This time defines when Dynatrace will invoke the integration to 3 rd party system for a problem after it has been detected.

Tuning 264

Tuning Architecture Monitoring Big Data 264

Dynatrace

OCTOBER 14, 2021

Gartner defines AIOps as the combination of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” This contrasts stochastic AIOps approaches that use probability models to infer the state of systems. What is AIOps?

Artificial Intelligence 246

Artificial Intelligence DevOps Innovation Metrics 246

Dynatrace

JUNE 1, 2020

The variables that can impact the performance of an application vary; from coding errors or ‘bugs’ in the software, database slowdowns, hosting and network performance, to operating system and device type support.

Artificial Intelligence 254

Artificial Intelligence Social Media Monitoring IoT 254