Dynatrace

JULY 29, 2022

What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.

Analytics 178

Analytics Storage Retail Monitoring 178

Dynatrace

FEBRUARY 15, 2023

A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. But making decisions about which data to store in easily accessible hot storage upfront requires IT pros to know which questions they want to ask ahead of time and ensure that data is indexed.

Analytics 130

Analytics Storage Cloud DevOps 130

Dynatrace

JANUARY 12, 2022

Messaging systems can significantly improve the reliability, performance, and scalability of the communication processes between applications and services. In traditional or hybrid IT environments, messaging systems are used to decouple heavyweight processing, buffer work, or smooth over spiky workloads. Dynatrace news.

Analytics 183

Analytics Systems DevOps Healthcare 183

Dynatrace

NOVEMBER 15, 2021

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Logs can include data about user inputs, system processes, and hardware states. What is log analytics? Log monitoring vs log analytics.

Analytics 205

Analytics Monitoring Artificial Intelligence DevOps 205

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 182

Analytics Innovation Metrics Database 182

Scalegrid

MAY 13, 2020

Greenplum Database is a massively parallel processing (MPP) SQL database that is built and based on PostgreSQL. Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. What Exactly is Greenplum? At a glance – TLDR.

Big Data 321

Big Data Database Artificial Intelligence Open Source 321

Dynatrace

OCTOBER 4, 2022

Grail needs to support security data as well as business analytics data and use cases. With that in mind, Grail needs to achieve three main goals with minimal impact to cost: Cope with and manage an enormous amount of data —both on ingest and analytics. Ingest and process with Grail. Thus, it can scale massively. Retain data.

Artificial Intelligence 264

Artificial Intelligence Analytics Storage Architecture 264

Dynatrace

OCTOBER 4, 2022

These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues. Each process could generate multiple log entries, adding up to terabytes of data every day. A modern approach to log analytics stores data without indexing.

Analytics 217

Analytics Storage Infrastructure Metrics 217

Dynatrace

JANUARY 4, 2024

Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. Improving data quality is a strategic process that involves all organizational members who create and use data. Fragmented and siloed data storage can create inconsistencies and redundancies. That’s where causal AI can help.

Government 196

Government Analytics Benchmarking Storage 196

Dynatrace

SEPTEMBER 5, 2023

They handle complex infrastructure, maintain service availability, and respond swiftly to incidents. By analyzing patterns and trends, predictive analytics helps identify potential issues or opportunities, enabling proactive actions to prevent problems or capitalize on advantageous situations. Proactive resource allocation.

Artificial Intelligence 260

Artificial Intelligence DevOps Analytics Engineering 260

DZone

JUNE 4, 2019

NVMe Storage Use Cases. NVMe storage's strong performance, combined with the capacity and data availability benefits of shared NVMe storage over local SSD, makes it a strong solution for AI/ML infrastructures of any size. There are several AI/ML focused use cases to highlight.

Storage 100

Storage FinTech Artificial Intelligence Performance 100

Dynatrace

MAY 23, 2024

With the AI-powered Dynatrace platform now generally available on Azure, Azure Native Dynatrace Service customers can now leverage the full AI power of the Dynatrace platform directly from Azure. Also, see how to automate this process with bicep and how to automate with Azure CLI.

Azure 187

Azure Cloud Metrics Innovation 187

Dynatrace

OCTOBER 6, 2023

Buckets are similar to folders, a physical storage location. Debug-level logs, which also generate high volumes and have a shorter lifespan or value period than other logs, could similarly benefit from dedicated storage. Suppose a single Grail environment is central storage for pre-production and production systems.

Artificial Intelligence 258

Artificial Intelligence Analytics Metrics Storage 258

Dynatrace

OCTOBER 23, 2023

Secondly, determining the correct allocation of resources (CPU, memory, storage) to each virtual machine to ensure optimal performance without over-provisioning can be difficult. Therefore, we have redesigned this extension from scratch, replacing the previously available WMI-based extension.

Efficiency 210

Efficiency Virtualization Hardware Performance 210

Dynatrace

MARCH 27, 2024

Customers can then ingest logs from AWS into the Dynatrace® platform, for in-depth log management and security analytics powered by Grail TM. AWS AppFabric ingests and normalizes audit logs from SaaS applications and delivers them to an Amazon Simple storage service (Amazon S3) bucket in an organization’s AWS account.

AWS 242

AWS Analytics Government Monitoring 242

Dynatrace

SEPTEMBER 8, 2022

Log management is an organization’s rules and policies for managing and enabling the creation, transmission, analysis, storage, and other tasks related to IT systems’ and applications’ log data. Comparing log monitoring, log analytics, and log management. These two processes feed into one another. Dynatrace news.

Systems 182

Systems Cloud Analytics DevOps 182

Dynatrace

JANUARY 18, 2023

Business events are a special class of events, new to Business Analytics; together with Grail, our data lakehouse, they provide the precision and advanced analytics capabilities required by your most important business use cases. Analytics without boundaries. Example business events from anywhere.

Analytics 224

Analytics Retail Storage Monitoring 224

Scalegrid

JANUARY 12, 2024

All rely heavily on utilizing allocated portions from existing pools made available through specific providers as part of their service offerings. In the realm of cloud-based business operations, there is an increasing dependence on complex information processing patterns. Ultimately improving efficiency while minimizing errors.

Cloud 130

Cloud Virtualization Storage Efficiency 130

Dynatrace

JUNE 27, 2023

Logs complement out-of-the-box metrics and enable automated actions for responding to availability, security, and other service events. Many AWS services and third party solutions use AWS S3 for log storage. Dynatrace Log Management and Analytics powered by Grail enables you to get answers from logs with any query at any time.

AWS 194

AWS Cloud Lambda Analytics 194

Dynatrace

NOVEMBER 20, 2023

Effectively automating IT processes is key to addressing the challenges of  complex cloud environments. Relying on manual processes results in outages, increased costs, and frustrated customers. These three types of AI used together enable more effective IT automation than a single form of AI on its own. What is AIOps?

AWS 205

AWS Artificial Intelligence Analytics Cloud 205

Scalegrid

MARCH 28, 2024

This article will explore how they handle data storage and scalability, perform in different scenarios, and, most importantly, how these factors influence your choice. It uses a hash table to manage these pairs, divided into fixed-size buckets with linked lists for key-value storage.

Cache 130

Cache Storage Scalability Architecture 130

Scalegrid

JANUARY 8, 2024

Key Takeaways AI integration in cloud computing increases operational efficiency by automating processes, optimizing resource allocation, and improving scalability, leading to cost savings and allowing IT teams to concentrate on strategic initiatives.

Artificial Intelligence 147

Artificial Intelligence Cloud Scalability Analytics 147

Dynatrace

JANUARY 31, 2024

million” – Gartner Data observability is a practice that helps organizations understand the full lifecycle of data, from ingestion to storage and usage, to ensure data health and reliability. At its core, data observability is about ensuring the availability, reliability, and quality of data.

DevOps 191

DevOps Analytics Airlines Metrics 191

Dynatrace

JUNE 6, 2023

Container orchestration platforms automate daily operations for processes such as the re-creation of failed containers and rolling deployments. This key feature helps in maintaining availability and reduces the need for manual intervention. AI-powered analytics. This helps to avoid downtime for end users. Automated scaling.

Open Source 188

Open Source Storage Analytics Innovation 188

ScaleOut Software

JULY 14, 2021

Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.

IoT 52

IoT Azure Analytics Serverless 52

Scalegrid

DECEMBER 21, 2023

To achieve optimal tracking results it is important to choose wisely among available tools like Prometheus or Grafana, which offer deeper insights into understanding your Redis® instances for better performance optimization. Setting Up RedisInsight Getting RedisInsight up and running is a simple process.

Strategy 130

Strategy Monitoring Latency DevOps 130

Dynatrace

APRIL 19, 2023

Enterprise data stores grow with the promise of analytics and the use of data to enable behavioral security solutions, cognitive analytics, and monitoring and supervision. ” This data is excluded from storage, but teams can still gain value from data enrichment beforehand. Dynatrace protects data above industry standards.

Design 188

Design Storage Programming Analytics 188

Dynatrace

DECEMBER 5, 2023

Streamline privacy requirements with flexible retention periods Data retention is a critical aspect of data handling, and it’s not just about privacy compliance—it’s about having the flexibility to optimize data storage times in Grail for your Dynatrace use cases. Other data types will be available soon). What’s next?

Storage 163

Storage Healthcare Best Practices Speed 163

Dynatrace

FEBRUARY 15, 2023

Further, developers can build more agility into their processes with an intuitive, low-code approach to create custom, compliant, and intelligent data-driven apps and automation. Additionally, its modern architecture delivers cost-effective storage and compute. Site Reliability Guardian.

Analytics 130

Analytics Storage Cloud Code 130

The Netflix TechBlog

OCTOBER 28, 2021

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. CL provides an end-to-end solution for logging, processing, and analyzing user interactions on Netflix apps from all devices. What is your favorite project?

Data Engineering 173

Data Engineering Engineering Big Data Software Engineering 173

Dynatrace

NOVEMBER 4, 2022

On average, IT, development, and security teams in the sector rely on nine different monitoring tools to maintain visibility into performance and availability. This process combines AI with security and operations. Distributed architectures create another challenge for governments and public-sector organizations.

Government 147

Government Analytics Cloud Storage 147

Scalegrid

JANUARY 20, 2020

Note: ScaleGrid implements follower clusters using storage snapshots. It is not available for our in-memory database offerings like hosting for Redis™*. And since the entire import is performed using storage snapshots, rather than a logical dump, the process is nearly instantaneous. Data Analytics. The best part?

Analytics 185

Analytics Storage Database Systems 185

Dynatrace

MARCH 24, 2023

Container orchestration is a process that automates the deployment and management of containerized applications and services at scale. This orchestration includes provisioning, scheduling, networking, ensuring availability, and monitoring container lifecycles. How does container orchestration work?

Infrastructure 182

Infrastructure Open Source Operating System Cloud 182

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. We will examine each of these elements in more detail. A pivotal element of any hybrid cloud strategy is adhering to regulatory requirements and compliance standards specific to an industry.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Dynatrace

AUGUST 24, 2020

But there are other related components and processes (for example, cloud provider infrastructure) that can cause problems in applications running on Kubernetes. And because Dynatrace can consume CloudWatch metrics, almost all your AWS usage information is available to you within Dynatrace. Digital Business Analytics. Conclusion.

Analytics 237

Analytics Infrastructure AWS Operating System 237

The Morning Paper

SEPTEMBER 10, 2019

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Anchored in the primary use case of supporting Google’s YouTube business, what we’re looking at here could well be the future of data processing at Google. Because they had too many data processing systems! ;). VLDB’19. are divided.

Analytics 75

Analytics Latency Cache Google 75

All Things Distributed

MARCH 11, 2015

Many of these innovations will have a significant analytics component or may even be completely driven by it. For example many of the Internet of Things innovations that we have seen come to life in the past years on AWS all have a significant analytics components to it. Cloud analytics are everywhere.

Analytics 135

Analytics Cloud Healthcare Retail 135