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 208

Artificial Intelligence Analytics Storage Government 208

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 217

DevOps Big Data Cloud Innovation 217

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 196

DevOps Artificial Intelligence Healthcare Innovation 196

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 Analytics Latency Azure 57

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 202

Artificial Intelligence DevOps Hardware Virtualization 202

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 186

Analytics Innovation Metrics Database 186

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 212

Artificial Intelligence DevOps Innovation Metrics 212

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 223

Cloud Monitoring Best Practices Infrastructure 223

Dynatrace

MARCH 11, 2021

I took a big-data-analysis approach, which started with another problem visualization. This is required for understanding how I intend to improve the efficiency of (manual) alert ticket handling. For this I didn’t want to use simple visualization, I wanted to analyze the problem data itself. Problem type analysis.

Tuning 263

Tuning Architecture Monitoring Big Data 263

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. What’s missing in this picture? Conventional streaming analytics architectures have not kept up with the growing demands of IoT.

IoT 78

IoT Analytics Big Data Architecture 78

The Netflix TechBlog

JUNE 7, 2021

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. The data is also used by security and other partner teams for insight and incident analysis. What is BPF?

Network 325

Network Transportation AWS Cloud 325

The Netflix TechBlog

MARCH 2, 2021

I started working at a local payment processing company after graduation, where I built survival models to calculate lifetime value and experimented with them on our brand new big data stack. I was doing data science without realizing it. Each company has their own spin on data scientist responsibilities.

Analytics 207

Analytics C++ Innovation Engineering 207

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.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

All Things Distributed

JUNE 26, 2016

previously known as Emdeon) uses Amazon SNS to handle millions of confidential client transactions daily to process claims and pharmacy requests serving over 340K physicians and 60K pharmacies in full compliance with healthcare industry regulations. . Seamless ingestion of large volumes of sensed data.

AWS 90

AWS Cloud Healthcare Blockchain 90

ScaleOut Software

NOVEMBER 24, 2020

Rapid advances in the telematics industry have dramatically boosted the efficiency of vehicle fleets and have found wide ranging applications from long haul transport to usage-based insurance. The volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Highly Scalable

MAY 1, 2012

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce.

Analytics 191

Analytics Traffic Big Data Efficiency 191

All Things Distributed

DECEMBER 20, 2017

The benefit for customers: Authorized users can view this data and therefore manage their inventories across different sites, making the maintenance processes much more efficient. By knowing this, Kärcher can generate new top-line revenue in the form of subscription models for its analysis portal. More than mere support.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

The Morning Paper

SEPTEMBER 19, 2019

It handles an order of magnitude more throughput than a prototype built on a stream processing engine. Could it be Analyzing efficient stream processing on modern hardware ? .” Crusher is a Google system for automatically discovering email templates (e.g. for machine generated emails sent to humans). What’s their secret???

Blockchain 46

Blockchain Hardware Google Analytics 46

Testsigma

MAY 7, 2020

IBM Big Data and Analytics Hub website cited a case study, where a US insurance company was estimating 15% of their testing efforts to be just test data collection for the backend system and the frontend system. The test data management for the company had become a big problem and had to be solved.

Testing 60

Testing Storage Database Processing 60

Highly Scalable

JANUARY 31, 2012

Applications: Log Analysis, Data Querying. It is required to save all items that have the same value of function into one file or perform some other computation that requires all such items to be processed as a group. Reducer obtains all items grouped by function value and process or save them. Distributed Task Execution.

C++ 144

C++ Network Ecommerce Processing 144

The Netflix TechBlog

JULY 26, 2021

Operational Reporting is a reporting paradigm specialized in covering high-resolution, low-latency data sets, serving detailed day-to-day activities¹ and processes of a business domain. At Netflix Studio, teams build various views of business data to provide visibility for day-to-day decision making. tactical) in nature.

Big Data 253

Big Data Government Analytics Processing 253

Highly Scalable

MARCH 10, 2015

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods. In that case, the equation (1.2)

Retail 152

Retail C++ Analytics Metrics 152

All Things Distributed

NOVEMBER 15, 2010

From financial processing and traditional oil & gas exploration HPC applications to integrating complex 3D graphics into online and mobile applications, the applications of GPU processing appear to be limitless. Because of its focus on latency, the generic CPU yielded rather inefficient system for graphics processing.

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 4.15

Database 40

Database Analytics Blockchain Healthcare 40

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 219

Infrastructure Big Data Transportation Architecture 219

The Netflix TechBlog

FEBRUARY 16, 2021

Operational Efficiency: The majority of the changes require metadata configuration files and library code changes, usually taking days of testing and service release to adopt the updates. Self Service Management UI: A straightforward visualization tool for rules management and are in the process of supporting direct rules editing.

Mobile 209

Mobile Engineering Infrastructure Scalability 209

O'Reilly

SEPTEMBER 19, 2023

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.

Hardware 99

Hardware Storage Big Data Blockchain 99

ACM Sigarch

NOVEMBER 1, 2019

Alongside more traditional sessions such as Real-World Deployed Systems and Big Data Programming Frameworks, there were many papers focusing on emerging hardware architectures, including embedded multi-accelerator SoCs, in-network and in-storage computing, FPGAs, GPUs, and low-power devices. ATC ’19 was refreshingly different.

Architecture 12

Architecture Hardware Cache Storage 12

Abhishek Tiwari

JULY 7, 2023

In the era of big data and complex data processing, data pipelines have emerged as a popular solution for managing and manipulating data. They provide a systematic approach to extract, transform, and load (ETL) data from various sources, enabling organizations to derive valuable insights.

Logistics 52

Logistics Transportation Scalability Data Engineering 52

Smashing Magazine

JULY 29, 2019

It was at this point I accidentally clicked on an ‘Explanation’ URL in the devtools, which took me to the network analysis reference , costing me about 5 MB of my budget. The weightiest of these was 186 KB, which isn’t particularly big — there were just so many of them, and they all downloaded at once. . <script MB of data.

Cache 96

Cache Google Mobile Network 96

The Netflix TechBlog

SEPTEMBER 10, 2019

After recreating the dataset, you can plot the raw numbers and perform custom analyses to understand the distribution of the data across test cells. With our new platform for experimentation analysis, it’s easy for scientists to perfectly recreate analyses on their laptops in a notebook. This is an ongoing journey.

Metrics 215

Metrics Architecture Infrastructure Innovation 215