Dynatrace

OCTOBER 6, 2023

Grail: Enterprise-ready data lakehouse Grail, the Dynatrace causational data lakehouse, was explicitly designed for observability and security data, with artificial intelligence integrated into its foundation. Buckets are similar to folders, a physical storage location. There is a default bucket for each table.

Artificial Intelligence 266

Artificial Intelligence Analytics Metrics Storage 266

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. High-performance analytics—no indexing required.

Artificial Intelligence 272

Artificial Intelligence Analytics Storage Architecture 272

Dynatrace

OCTOBER 17, 2023

Data, AI, analytics, and automation are key enablers for efficient IT operations Data is the foundation for AI and IT automation. The data is stored with full context, which enables AI to deliver precise answers with speed and analytics to give rich insights with efficiency. 5) in the Gartner report.

Monitoring 231

Monitoring Artificial Intelligence Performance Analytics 231

Dynatrace

JUNE 27, 2023

Many AWS services and third party solutions use AWS S3 for log storage. We hear from our customers how important it is to have a centralized, quick, and powerful access point to analyze these logs; hence we’re making it easier to ingest AWS S3 logs and leverage Dynatrace Log Management and Analytics powered by Grail.

AWS 204

AWS Cloud Lambda Analytics 204

Dynatrace

OCTOBER 17, 2023

Dynatrace has developed the purpose-built data lakehouse, Grail , eliminating the need for separate management of indexes and storage. All data is readily accessible without storage tiers, such as costly solid-state drives (SSDs). No storage tiers, no archiving or retrieval from archives, and no indexing or reindexing.

Retail 242

Retail Storage Best Practices Architecture 242

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. Data observability is crucial to analytics and automation, as business decisions and actions depend on data quality.

DevOps 198

DevOps Analytics Airlines Metrics 198

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

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. It is critical for fast-paced product innovation at Netflix since CL provides foundational data for personalization, A/B experimentation, and performance analytics.

Data Engineering 173

Data Engineering Engineering Big Data Software Engineering 173

Scalegrid

JANUARY 12, 2024

Various forms can take shape when discussing workloads within the realm of cloud computing environments – examples include order management databases, collaboration tools, videoconferencing systems, virtual desktops, and disaster recovery mechanisms. Storage is a critical aspect to consider when working with cloud workloads.

Cloud 130

Cloud Virtualization Storage Efficiency 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. Synthetic Monitoring is used to simulate defined customer behavior, for example when opening a website and clicking a certain button. Digital Business Analytics.

Analytics 246

Analytics Infrastructure AWS Operating System 246

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. Take a look at how integrated data analytics can help client applications. The Challenges with Parallel Queries.

Analytics 52

Analytics Airlines Cache Scalability 52

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. Take a look at how integrated data analytics can help client applications. The Challenges with Parallel Queries.

Analytics 52

Analytics Airlines Cache Scalability 52

Dynatrace

DECEMBER 20, 2021

For example, the <body> element of your page exists on one branch of this tree structure, with any <img> assets branching off. Consider the example of “rage clicks,” which are rapid clicks (or taps) on the same spot when a feature is unresponsive. Improved analytic context. In others, patterns emerge.

Mobile 251

Mobile Website Analytics Cache 251

Dynatrace

SEPTEMBER 30, 2021

The list of AWS certifications below shows there are two main AWS certification types: Core and Specialty, six classified as Core AWS Certifications, and five designated as Specialty AWS Certifications. Data analytics. As of June 2021, Amazon currently offers 11 certifications. Core AWS certifications. Machine learning.

AWS 215

AWS Best Practices Analytics Cloud 215

Dynatrace

APRIL 26, 2022

STM generates traffic that replicates the typical path or behavior of a user on a network to measure performance for example, response times, availability, packet loss, latency, jitter, and other variables). Endpoint monitoring (EM). Endpoints can be physical (i.e., PC, smartphone, server) or virtual (virtual machines, cloud gateways).

Monitoring 209

Monitoring Social Media IoT Metrics 209

Dynatrace

NOVEMBER 22, 2021

EC2 is Amazon’s Infrastructure-as-a-service (IaaS) compute platform designed to handle any workload at scale. With EC2, Amazon manages the basic compute, storage, networking infrastructure and virtualization layer, and leaves the rest for you to manage: OS, middleware, runtime environment, data, and applications. Amazon EC2.

Best Practices 217

Best Practices AWS Monitoring Serverless 217

Dynatrace

SEPTEMBER 13, 2023

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. This is where unified observability and Dynatrace Automations can help by leveraging causal AI and analytics to drive intelligent automation across your multicloud ecosystem.

AWS 252

AWS Efficiency Azure Cloud 252

Scalegrid

JULY 17, 2020

If you store each of the keys as columns, it will result in frequent DML operations – this can be difficult when your data set is large - for example, event tracking, analytics, tags, etc. For example, Stripe transactions. JSONB storage results in a larger storage footprint. JSON preserves duplicate keys.

Storage 321

Storage Database Efficiency Processing 321

The Netflix TechBlog

OCTOBER 27, 2020

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. For example, in order to enhance our user experience, one online application fetches subscribers’ preferences data to recommend movies and TV shows.

Latency 243

Latency Storage Big Data Tuning 243

The Netflix TechBlog

MARCH 10, 2023

are stored in secure storage layers. Amsterdam is built on top of three storage layers. And finally, we have an Apache Iceberg layer which stores assets in a denormalized fashion to help answer heavy queries for analytics use cases. It is also responsible for asset discovery, validation, sharing, and for triggering workflows.

Strategy 258

Strategy Cache Analytics Storage 258

Dynatrace

AUGUST 27, 2020

AWS IoT Analytics. AWS Storage Gateway. We’ve selected two example services: Amazon Sagemaker and Amazon Neptune ,, for this purpose. Amazon Neptune is a fast, reliable, fully-managed graph database service designed for applications working with highly connected datasets. Amazon Quantum Ledger Database (QLDB).

AWS 272

AWS Cloud IoT Database 272

The Netflix TechBlog

OCTOBER 19, 2020

a Netflix member via Twitter This is an example of a question our on-call engineers need to answer to help resolve a member issue?—?which Now let’s look at how we designed the tracing infrastructure that powers Edgar. A second job taps the data feed from the first job, does tail sampling of data and writes traces to the storage system.

Infrastructure 233

Infrastructure Transportation Storage Open Source 233

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. Synthetic Monitoring is used to simulate defined customer behavior, for example when opening a website and clicking a certain button. Digital Business Analytics.

Analytics 135

Analytics Infrastructure AWS Operating System 135

Dynatrace

DECEMBER 22, 2019

AWS offers a broad set of global, cloud-based services including computing, storage, networking, Internet of Things (IoT), and many others. Amazon Kinesis Data Analytics. Amazon Simple Storage Service (S3). Metrics for each service instance are presented in detailed charts—see the example for ECS below. Amazon Redshift.

AWS 137

AWS Metrics IoT Storage 137

Dynatrace

DECEMBER 22, 2019

AWS offers a broad set of global, cloud-based services including computing, storage, networking, Internet of Things (IoT), and many others. Amazon Kinesis Data Analytics. Amazon Simple Storage Service (S3). Metrics for each service instance are presented in detailed charts—see the example for ECS below. Amazon Redshift.

AWS 133

AWS Metrics IoT Storage 133

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. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases.

Analytics 191

Analytics Traffic Big Data Efficiency 191

Speed Curve

FEBRUARY 25, 2024

In this post I'm going to walk through: How I help people identify the causes of poor INP times Examples of some of the most common issues Approaches I've used to help sites improve their INP Identifying Interactions The Chrome UX Report (CrUX) can provide a high-level view of INP. Here is the approach I use when debugging interactions.

Analytics 91

Analytics Processing Google Mobile 91

Dynatrace

SEPTEMBER 8, 2023

To achieve this, we’ll create a Grail bucket specifically designed to retain data for a duration of 10 years (3,657 days). In the above recording, we demonstrate an alert specifically designed to notify when there is a significant increase in pending transactions.

Database 274

Database Artificial Intelligence Metrics Monitoring 274

Dynatrace

JANUARY 23, 2024

For example, high-quality data and predictive AI enable causal AI to provide precise, continuous, and actionable insights in real time. Edge computing brings compute and data storage closer to where data is generated to help reduce costs, boost performance, and improve customer experience. Join us for Dynatrace Perform 2024.

Performance 191

Performance DevOps Innovation Artificial Intelligence 191

All Things Distributed

AUGUST 18, 2011

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. Hadoop is quickly becoming the preferred tool for this type of large scale data analytics.

Big Data 111

Big Data Analytics AWS Cloud 111

Percona

MARCH 30, 2023

Further, open source databases can be modified in infinite ways, enabling institutions to meet their specific needs for data storage, retrieval, and processing. Non-relational databases: Instead of tables, non-relational (NoSQL) databases use document-based data storage, column-oriented storage, and graph databases.

Open Source 73

Open Source Database Storage Scalability 73

Percona

MARCH 17, 2023

These updates are designed to keep databases running at peak performance and simplify database operations. PMM2 uses VictoriaMetrics (VM) as its metrics storage engine. 25 Bytes, or you should plan storage roughly between 500 GB – one TB for a default 30 day retention period.

Monitoring 96

Monitoring Scalability Cache Database 96

Dynatrace

APRIL 21, 2022

These limits are designed to prevent ActiveGate startup process slowdown and excessive node resource usage. Improved error handling for unexpected storage issues. (APM-360014). The dynakube.yaml file now comes with default ActiveGate resource limits: 500m CPU requests. 1000m CPU limits. 512Mi memory requests. APM-361736).

Azure 221

Azure Metrics AWS Monitoring 221

All Things Distributed

MAY 18, 2010

Expanding the Cloud - Amazon S3 Reduced Redundancy Storage. Today a new storage option for Amazon S3 has been launched: Amazon S3 Reduced Redundancy Storage (RRS). This new storage option enables customers to reduce their costs by storing non-critical, reproducible data at lower levels of redundancy. Comments ().

Storage 71

Storage Cloud AWS Scalability 71

All Things Distributed

JUNE 21, 2018

Further, with the growth and scale of Amazon.com, boundless horizontal scale needed to be a key design point--scaling up simply wasn't an option. Airbnb is a great example of a customer building high-performance and scalable applications with Amazon Aurora. Take Expedia, for example.

Database 167

Database AWS Games Latency 167

Smashing Magazine

SEPTEMBER 20, 2021

For example, someone might web scrape all the product pages of a competitor’s retail site to harvest information about products being offered and current pricing to try to gain a competitive edge. A better approach is to use the data you are already collecting with your web analytics or R eal U ser M easurement ( RUM ) services.

Website 89

Website Analytics Google Cache 89

HammerDB

JUNE 27, 2022

Databases are highly sophisticated software, and to design and run a fair benchmark workload is a complex undertaking. The TPC designed benchmarks for transaction processing (OLTP) and analytics (OLAP) and anyone can run these benchmarks, have them audited by the TPC and published on the official benchmark rankings.

Benchmarking 62

Benchmarking Open Source C++ Cache 62