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?

Artificial Intelligence 197

Artificial Intelligence Storage Analytics Government 197

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. Defining Hybrid Cloud Strategy The decision-making process about where to situate data and applications is vital to any hybrid cloud solution.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

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. Further, business leaders must often determine whether the data is relevant for the business and if they can afford it.

Analytics 177

Analytics Infrastructure Storage Efficiency 177

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. Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes.

Analytics 223

Analytics Artificial Intelligence Storage Serverless 223

Dynatrace

JANUARY 11, 2023

With more organizations taking the multicloud plunge, monitoring cloud infrastructure is critical to ensure all components of the cloud computing stack are available, high-performing, and secure. These next-generation cloud monitoring tools present reports — including metrics, performance, and incident detection — visually via dashboards.

Cloud 214

Cloud Monitoring Best Practices Infrastructure 214

Dynatrace

JULY 6, 2022

As organizations continue to adopt multicloud strategies, the complexity of these environments grows, increasing the need to automate cloud engineering operations to ensure organizations can enforce their policies and architecture principles. IT automation tools can achieve enterprise-wide efficiency. Big data automation tools.

Artificial Intelligence 178

Artificial Intelligence Tuning Strategy Big Data 178

Dynatrace

FEBRUARY 16, 2023

As observability and security data converge in modern multicloud environments, there’s more data than ever to orchestrate and analyze. The goal is to turn more data into insights so the whole organization can make data-driven decisions and automate processes. Logs on Grail Log data is foundational for any IT analytics.

Analytics 176

Analytics Innovation Metrics Database 176

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., Finally, we show that Seer can identify application level design bugs, and provide insights on how to better architect microservices to achieve predictable performance. ASPLOS’19.

Big Data 45

Big Data Cloud Performance Hardware 45

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. Flow Exporter The Flow Exporter is a sidecar that uses eBPF tracepoints to capture TCP flows at near real time on instances that power the Netflix microservices architecture. What is BPF?

Network 325

Network Transportation AWS Cloud 325

Dynatrace

DECEMBER 15, 2022

The primary goal of ITOps is to provide a high-performing, consistent IT environment. Organizations measure these factors in general terms by assessing the usability, functionality, reliability, and performance of products and services. Performance. What does IT operations do? ITOps vs. AIOps.

Artificial Intelligence 191

Artificial Intelligence DevOps Hardware Virtualization 191

Dynatrace

MARCH 14, 2022

The healthcare industry is embracing cloud technology to improve the efficiency, quality, and security of patient care, and this year’s HIMSS Conference in Orlando, Fla., AIOps (or “AI for IT operations”) uses artificial intelligence so that big data can help IT teams work faster and more effectively.

Healthcare 218

Healthcare Artificial Intelligence Innovation Strategy 218

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 144

Azure Cloud Big Data Virtualization 144

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. Apache Mesos with the Marathon DC/OS is popular for large-scale production clusters running existing workloads on big data systems, such as Hadoop, Kafka, and Spark.

Infrastructure 176

Infrastructure Open Source Operating System Cloud 176

ScaleOut Software

JULY 19, 2021

Today’s streaming analytics architectures are not equipped to make sense of this rapidly changing information and react to it as it arrives. This data is also periodically uploaded to a data lake for offline batch analysis that calculates key statistics and looks for big trends that can help optimize operations.

IoT 78

IoT Analytics Big Data Architecture 78

ACM Sigarch

MAY 31, 2023

Introduction Memory systems are evolving into heterogeneous and composable architectures. Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications.

Latency 52

Latency Hardware Cache Architecture 52

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 visualization I used the same backend architecture as for the real-time visualization I presented previously.

Tuning 260

Tuning Architecture Monitoring Big Data 260

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. Analyze the data. Alert fatigue and chasing false positives are not only efficiency problems. Execute an action plan.

DevOps 185

DevOps Artificial Intelligence Healthcare Innovation 185

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 85

Open Source Storage Database Big Data 85

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.” But what is AIOps, exactly? And how can it support your organization? What is AIOps? Challenges of traditional AIOps. AIOps use cases.

Artificial Intelligence 201

Artificial Intelligence DevOps Innovation Metrics 201

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. However, telematics architectures face challenges in responding to telemetry in real time. Current Telematics Architecture.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

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

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

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

The Morning Paper

JUNE 23, 2019

I say go ahead and use local state as a performance boost, so long as you’re fine to have that state wiped out at any moment. We’ve seen similar high marshalling overheads in big data systems too.) Fetching too much data in a single query (i.e., getting the whole value when you supply the key). What is that something?

Cache 79

Cache Latency Google Lambda 79

ACM Sigarch

NOVEMBER 1, 2019

This blog post gives a glimpse of the computer systems research papers presented at the USENIX Annual Technical Conference (ATC) 2019, with an emphasis on systems that use new hardware architectures. As a consequence, the vast majority of the papers in the past has usually focused on conventional X86 or GPU-accelerated architectures.

Architecture 12

Architecture Hardware Cache Storage 12

All Things Distributed

DECEMBER 20, 2017

For example, Kärcher, the maker of cleaning technologies, manages its entire fleet through the cloud solution "Kärcher Fleet" This transmits data from the company's cleaning devices e.g. about the status of maintenance and loading, when the machines are used, and where the machines are located. This pattern should be broken.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

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. A daily process ranks the records by timestamp to generate a data frame of compacted records.

Big Data 253

Big Data Government Analytics Processing 253

The Netflix TechBlog

NOVEMBER 20, 2023

We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. IPS provides the incremental processing support with data accuracy, data freshness, and backfill for users and addresses many of the challenges in workflows. past 3 hours or 10 days).

Processing 210

Processing Big Data Efficiency Engineering 210

The Netflix TechBlog

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 203

Storage Latency Efficiency Data Engineering 203

The Netflix TechBlog

OCTOBER 27, 2020

Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto , to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 243

Latency Storage Big Data Tuning 243

Highly Scalable

MARCH 10, 2015

The goal is to find a set of the most promising candidates who should receive the resource in order to maximize the overall performance of the targeted group of customers. Moreover, gross margin is not the only performance metric that is important for retailers. The resource can be homogenous (i.e. Applications. Applications.

Retail 152

Retail C++ Analytics Metrics 152

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 202

Java Scalability Traffic Architecture 202

Abhishek Tiwari

DECEMBER 27, 2017

In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture. A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

All Things Distributed

NOVEMBER 15, 2010

This incredible power is available for anyone to use in the usual pay-as-you-go model, removing the investment barrier that has kept many organizations from adopting GPUs for their workloads even though they knew there would be significant performance benefit. From CPU to GPU.

AWS 136

AWS Latency Programming Architecture 136

The Netflix TechBlog

SEPTEMBER 10, 2019

You can look at ABlaze (our centralized A/B testing platform) and take a quick look at how it’s performing. Note that the new encodes perform well in the lower quantiles but worse in the higher ones You notice that the first new encode (Cell 2?—?Encode Simulated dataset that shows what the distribution of play delay may look like.

Metrics 215

Metrics Architecture Infrastructure Innovation 215