Dynatrace

JULY 6, 2022

AIOps brings an additional level of analysis to observability, as well as the ability to respond to events that warrant it. Ultimately, IT automation can deliver consistency, efficiency, and better business outcomes for modern enterprises. IT automation tools can achieve enterprise-wide efficiency. Big data automation tools.

Artificial Intelligence 198

Artificial Intelligence Tuning Strategy Big Data 198

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 218

Artificial Intelligence Storage Analytics Government 218

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. Individual samplers need to be built to be high throughput and memory efficient.

Big Data 57

Big Data Analytics Latency Azure 57

Dynatrace

OCTOBER 25, 2022

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. He meant that more and more developers are now becoming responsible for operations, and operations are becoming ingrained in developers’ job descriptions.

DevOps 225

DevOps Big Data Cloud Innovation 225

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. This second solution picks up at data collection, aggregation, and analysis, preparing it for execution. Deterministic AI.

DevOps 205

DevOps Artificial Intelligence Healthcare Innovation 205

Dynatrace

DECEMBER 15, 2022

Adding application security to development and operations workflows increases efficiency. AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations. ITOps vs. AIOps. ” The post What is ITOps?

Artificial Intelligence 211

Artificial Intelligence DevOps Hardware Virtualization 211

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 264

Tuning Architecture Monitoring Big Data 264

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. What is cloud monitoring? predict and prevent security breaches and outages. ” The post What is cloud monitoring?

Cloud 230

Cloud Monitoring Best Practices Infrastructure 230

Dynatrace

FEBRUARY 16, 2023

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

Analytics 195

Analytics Innovation Metrics Database 195

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 second solution picks up at data collection, aggregation and analysis, and prepares it for execution (grey arc).

Artificial Intelligence 221

Artificial Intelligence DevOps Innovation Metrics 221

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

APRIL 29, 2019

Demand Engineering Demand Engineering is responsible for Regional Failovers , Traffic Distribution, Capacity Operations and Fleet Efficiency of the Netflix cloud. Orchestration The Big Data Orchestration team is responsible for providing all of the services and tooling to schedule and execute ETL and Adhoc pipelines.

Open Source 279

Open Source Network Infrastructure Big Data 279

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., ASPLOS’19. When Seer does need to turn on its lower level instrumentation to pinpoint the likely cause of a predicted QoS violation, it has two different modes for doing this.

Big Data 45

Big Data Cloud Performance Hardware 45

The Netflix TechBlog

JUNE 7, 2021

The attributed flow data drives various use cases within Netflix like network monitoring and network usage forecasting available via Lumen dashboards and machine learning based network segmentation. The data is also used by security and other partner teams for insight and incident analysis.

Network 325

Network Transportation AWS Cloud 325

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?

IoT 78

IoT Analytics Big Data Architecture 78

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

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. One of the most common analyses that I do is a look-back analysis on the explore-data.

Analytics 207

Analytics C++ Innovation Engineering 207

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. The framework carries out a differential analysis against the preceding version of the snapshot to quickly identify unintended bugs in rule changes.

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 97

Hardware Storage Big Data Blockchain 97

ACM Sigarch

MAY 31, 2023

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. Jason Lowe-Power (UC Davis) discussed smart memory management and the need for an efficient interface for it.

Latency 52

Latency Hardware Cache Architecture 52

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. Fraud.net is a good example of this.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

All Things Distributed

JUNE 26, 2016

Advanced problem solving that connects big data with machine learning. At-scale computing and visual analysis. deploys its customers’ genomic pipelines on Amazon EC2 for highly complex and sensitive DNA research activities. For more details, see the case studies at All AWS Customer Stories.

AWS 90

AWS Cloud Healthcare Blockchain 90

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

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 results of batch analysis are typically produced after an hour’s delay or more.

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

Could it be Analyzing efficient stream processing on modern hardware ? Hyper Dimension Shuffle describes how Microsoft improved the cost of data shuffling, one of the most costly operations, in their petabyte-scale internal big data analytics platform, SCOPE. for machine generated emails sent to humans).

Blockchain 46

Blockchain Hardware Google Analytics 46

Highly Scalable

JANUARY 31, 2012

Applications: Log Analysis, Data Querying. Applications: Log Analysis, Data Querying, ETL, Data Validation. Each Mapper runs simulation for specified amount of data which is 1/Nth of the required sampling and emit error rate. Applications: ETL, Data Analysis. Distributed Task Execution.

C++ 144

C++ Network Ecommerce Processing 144

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

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. DBA needs to add all the negative and boundary value conditions as well in test data for testing.

Testing 60

Testing Storage Database Processing 60

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. Text analysis.

Database 40

Database Analytics Blockchain Healthcare 40

All Things Distributed

NOVEMBER 15, 2010

Now that our ability to generate higher and higher clock rates has stalled and CPU architectural improvements have shifted focus towards multiple cores, we see that it is becoming harder to efficiently use these computer systems. Driving down the cost of Big-Data analytics. Cluster Computer, Cluster GPU and Amazon EMR.

AWS 136

AWS Latency Programming Architecture 136

All Things Distributed

DECEMBER 13, 2009

These can be incidental tasks, such as the analysis of a particular dataset, or tasks where the amount of work to be done is almost never finished, such as media conversion from a Hollywoods studios movie vault, or web crawling for a search indexing company. Driving down the cost of Big-Data analytics.

Cloud 63

Cloud AWS Storage Innovation 63

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

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

Cache 96

Cache Google Mobile Network 96