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. ITOA automates repetitive cloud operations tasks and streamlines the flow of analytics into decision-making processes.

Analytics 199

Analytics Artificial Intelligence Big Data Open Source 199

Dynatrace

JUNE 26, 2023

This, in turn, accelerates the need for businesses to implement the practice of software automation to improve and streamline processes. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. Automate DevSecOps processes at scale.

Software 198

Software Software Analytics Big Data 198

DZone

DECEMBER 27, 2023

Welcome to the first post in our exciting series on mastering offline data pipeline's best practices, focusing on the potent combination of Apache Airflow and data processing engines like Hive and Spark. Working together, they form the backbone of many modern data engineering solutions.

Best Practices 140

Best Practices Data Engineering Big Data Games 140

Scalegrid

JUNE 28, 2024

It is widely utilized across various industries, such as finance, telecommunications, and e-commerce, for managing activities, including transaction processing, data streaming, and instantaneous messaging. Key Takeaways RabbitMQ is an open-source message broker facilitating seamless data exchange across diverse systems.

IoT 130

IoT Healthcare Programming Open Source 130

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 214

Tuning Efficiency Big Data Engineering 214

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

The Morning Paper

SEPTEMBER 8, 2019

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. VLDB’19. A sizable fraction of the jobs are much larger.

Big Data 57

Big Data Latency Analytics Azure 57

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 200

Artificial Intelligence Tuning Strategy Big Data 200

Dynatrace

OCTOBER 4, 2022

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. What is a data lakehouse? Data warehouses.

Artificial Intelligence 219

Artificial Intelligence Storage Analytics Government 219

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. What is your favorite project?

Data Engineering 178

Data Engineering Engineering Big Data Software Engineering 178

The Netflix TechBlog

JUNE 20, 2024

To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.” To address this, we propose developing an intelligent agent that can automatically discover, map, and query all data within an enterprise.

Data Engineering 204

Data Engineering Engineering Entertainment Software Engineering 204

Dynatrace

APRIL 20, 2023

With more automated approaches to log monitoring and log analysis, however, organizations can gain visibility into their applications and infrastructure efficiently and with greater precision—even as cloud environments grow. “The weakness of a data lake is they fail when you need to access them fast,” Pawlowski said.

Analytics 198

Analytics Infrastructure Storage Architecture 198

VoltDB

OCTOBER 17, 2024

What Makes the Automotive Industry Ripe for Real-Time Data Decisioning? The automotive industry is characterized by complex supply chains, intricate production processes, and stringent quality requirements. Efficient supply chain management is crucial for minimizing production costs and meeting delivery schedules.

Automotive 52

Automotive IoT Energy Artificial Intelligence 52

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. To achieve these AIOps benefits, comprehensive AIOps tools incorporate four key stages of data processing: Collection. Aggregation.

Artificial Intelligence 296

Artificial Intelligence Cloud Innovation Strategy 296

Dynatrace

OCTOBER 25, 2022

NoOps is a concept in software development that seeks to automate processes and eliminate the need for an extensive IT operations team. Organizations adopt DevOps, where developers and operations work together in a continuous loop, so they can develop software and resolve issues efficiently before they affect users. What is NoOps?

DevOps 227

DevOps Big Data Cloud Innovation 227

Dynatrace

OCTOBER 4, 2022

Several pain points have made it difficult for organizations to manage their data efficiently and create actual value. Limited data availability constrains value creation. Traditional solutions and approaches are inefficient given the number of manual tasks that are required for effective log data ingest.

Analytics 244

Analytics Artificial Intelligence Storage Serverless 244

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. Moving data with Bulldozer at Netflix.

Latency 249

Latency Storage Big Data Tuning 249

The Netflix TechBlog

OCTOBER 14, 2024

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. This document details the intriguing process of debugging this issue, all the way from the UI down to the Linux kernel. Specifically, pystan uses asyncio.

Latency 211

Latency Virtualization Traffic Processing 211

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 97

Big Data Analytics AWS Cloud 97

The Netflix TechBlog

JULY 26, 2021

Netflix is known for its loosely coupled microservice architecture and with a global studio footprint, surfacing and connecting the data from microservices into a studio data catalog in real time has become more important than ever. With the latest Data Mesh Platform, data movement in Netflix Studio reaches a new stage.

Big Data 256

Big Data Government Processing Analytics 256

The Netflix TechBlog

MARCH 25, 2019

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

Infrastructure 223

Infrastructure Big Data Transportation Architecture 223

Scalegrid

MARCH 14, 2024

Defining Hybrid Cloud Strategy The decision-making process about where to situate data and applications is vital to any hybrid cloud solution. The public cloud’s ability to scale efficiently enables ‘cloudbursting’ when demand spikes without requiring businesses to overprovision their own infrastructures.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Scalegrid

MARCH 28, 2024

Snapshots provide point-in-time captures of the dataset, which are efficient for recovery on startup. 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. Data transfer technology.

Cache 130

Cache Storage Architecture Scalability 130

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. Website monitoring examines a cloud-hosted website’s processes, traffic, availability, and resource use. What is cloud monitoring?

Cloud 231

Cloud Monitoring Best Practices Infrastructure 231

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. CloudOps includes processes such as incident management and event management. The four stages of data processing. Analyze the data.

DevOps 207

DevOps Artificial Intelligence Healthcare Innovation 207

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? What is ITOps? ITOps vs. AIOps.

Artificial Intelligence 213

Artificial Intelligence DevOps Hardware Virtualization 213

Adrian Cockcroft

NOVEMBER 18, 2024

Paul Reed, Clean Energy & Sustainability, AWS Solutions, Amazon Web Services SUS101 | Advancing sustainable AWS infrastructure to power AI solutions In this session, learn how AWS is committed to innovating with data center efficiency and lowering its carbon footprint to build a more sustainable business.

AWS 98

AWS Energy Lambda Government 98

Tech News Gather

NOVEMBER 16, 2024

Hoverwatch offers a simple and efficient way to monitor devices without requiring root access or jailbreak. Most similar tools charge upfront fees before granting access to tracking features, but Hoverwatch offers a straightforward setup process without unnecessary complications.

Mobile 52

Mobile Social Media Big Data Monitoring 52

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. Emphasis mine ).

Cloud 104

Cloud Big Data Latency Architecture 104

Dynatrace

FEBRUARY 16, 2023

The goal is to turn more data into insights so the whole organization can make data-driven decisions and automate processes. Grail data lakehouse delivers massively parallel processing for answers at scale Modern cloud-native computing is constantly upping the ante on data volume, variety, and velocity.

Analytics 196

Analytics Innovation Metrics Database 196

The Netflix TechBlog

DECEMBER 21, 2020

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Then deep dive into the merging use case of AutoOptimize and share some results and benefits.

Storage 209

Storage Latency Efficiency Data Engineering 209

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. See the health of your big data resources at a glance. Azure HDInsight supports a broad range of use cases including data warehousing, machine learning, and IoT analytics.

Azure 152

Azure Cloud Big Data Virtualization 152

The Netflix TechBlog

JUNE 1, 2021

clinical data was often small enough to fit into memory on an average computer and only in rare cases would its computation require any technical ingenuity or massive computing power. There was not enough scope to explore the distributed and large-scale computing challenges that usually come with big data processing.

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 130

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. Container orchestration is a process that automates the deployment and management of containerized applications and services at scale.

Infrastructure 196

Infrastructure Open Source Operating System Cloud 196

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

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.” The second challenge with traditional AIOps centers around the data processing cycle. But what is AIOps, exactly?

Artificial Intelligence 223

Artificial Intelligence DevOps Innovation Metrics 223

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 208

Java Scalability Traffic Architecture 208