Scalegrid

FEBRUARY 8, 2024

It utilizes methodologies like DStore, which takes advantage of underused hard drive space by using it for storing vast amounts of collected datasets while enabling efficient recovery processes. These systems enable vast amounts of data to be spread over multiple nodes, allowing for simultaneous access and boosting processing efficiency.

Storage 130

Storage Systems Big Data Azure 130

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 214

Artificial Intelligence Storage Analytics Government 214

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 202

DevOps Artificial Intelligence Healthcare Innovation 202

Dynatrace

DECEMBER 15, 2022

The roles and responsibilities of ITOps team members include the following: A system administrator configures servers, installs applications, monitors the health of the system, and fixes and upgrades hardware. Adding application security to development and operations workflows increases efficiency. Functionality. ITOps vs. AIOps.

Artificial Intelligence 208

Artificial Intelligence DevOps Hardware Virtualization 208

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. This is required for understanding how I intend to improve the efficiency of (manual) alert ticket handling.

Tuning 264

Tuning Architecture Monitoring Big Data 264

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. But logs are just one pillar of the observability triumvirate.

Analytics 191

Analytics Innovation Metrics Database 191

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? What is AIOps?

Artificial Intelligence 218

Artificial Intelligence DevOps Innovation Metrics 218

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

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. Python is also a tool we typically use for automation tasks, data exploration and cleaning, and as a convenient source for visualization work.

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., When a QoS violation is predicted to occur and a culprit microservice located, Seer uses a lower level tracing infrastructure with hardware monitoring primitives to identify the reason behind the QoS violation.

Big Data 45

Big Data Cloud Performance Hardware 45

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

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

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

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

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

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

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