Dynatrace

MAY 7, 2024

Fluent Bit is a telemetry agent designed to receive data (logs, traces, and metrics), process or modify it, and export it to a destination. Fluent Bit was designed to help you adjust your data and add the proper context, which can be helpful in the observability backend. What’s the difference between Fluent Bit and Fluentd?

Best Practices 255

Best Practices IoT Metrics Storage 255

Uber Engineering

AUGUST 14, 2019

Maintaining Uber’s large-scale data warehouse comes with an operational cost in terms of ETL functions and storage. In our experience, optimizing for operational efficiency requires answering one key question: for which tables does the maintenance cost supersede utility?

Efficiency 96

Efficiency Engineering Design Storage 96

The Netflix TechBlog

OCTOBER 8, 2024

Building on these foundational abstractions, we developed the TimeSeries Abstraction  — a versatile and scalable solution designed to efficiently store and query large volumes of temporal event data with low millisecond latencies, all in a cost-effective manner across various use cases.

Latency 242

Latency Storage Traffic Tuning 242

The Netflix TechBlog

SEPTEMBER 24, 2021

Figure 1: A Simplified Video Processing Pipeline With this architecture, chunk encoding is very efficient and processed in distributed cloud computing instances. From chunk encoding to assembly and packaging, the result of each previous processing step must be uploaded to cloud storage and then downloaded by the next processing step.

Cloud 242

Cloud Media Storage Cache 242

DZone

JANUARY 23, 2022

Hardware - servers/storage hardware/software faults such as disk failure, disk full, other hardware failures, servers running out of allocated resources, server software behaving abnormally, intra DC network connectivity issues, etc. . – this is addressed through monitoring and redundancy. Redundancy by building additional data centers.

Hardware 243

Hardware DevOps Network Storage 243

Dynatrace

SEPTEMBER 5, 2024

These developments open up new use cases, allowing Dynatrace customers to harness even more data for comprehensive AI-driven insights, faster troubleshooting, and improved operational efficiency. Customers have had a positive response to our native syslog implementation, noting its easy setup and efficiency.

Innovation 273

Innovation AWS Analytics Storage 273

Dynatrace

OCTOBER 4, 2022

A data lakehouse addresses these limitations and introduces an entirely new architectural design. This architecture offers rich data management and analytics features (taken from the data warehouse model) on top of low-cost cloud storage systems (which are used by data lakes). Grail is built for such analytics, not storage.

Artificial Intelligence 278

Artificial Intelligence Analytics Storage Architecture 278

Scalegrid

JULY 17, 2020

JSONB supports indexing the JSON data, and is very efficient at parsing and querying the JSON data. JSONB storage has some drawbacks vs. traditional columns: PostreSQL does not store column statistics for JSONB columns. JSONB storage results in a larger storage footprint. It is a decomposed binary format to store JSON.

Storage 321

Storage Database Efficiency Processing 321

The Morning Paper

MARCH 8, 2020

Building an elastic query engine on disaggregated storage , Vuppalapati, NSDI’20. This paper describes the design decisions behind the Snowflake cloud-based data warehouse. have altered the many assumptions that guided the design and optimization of the Snowflake system. From shared-nothing to disaggregation.

Storage 112

Storage Engineering Cache Serverless 112

Dynatrace

JANUARY 17, 2024

As organizations turn to artificial intelligence for operational efficiency and product innovation in multicloud environments, they have to balance the benefits with skyrocketing costs associated with AI. The good news is AI-augmented applications can make organizations massively more productive and efficient.

Strategy 234

Strategy Artificial Intelligence Storage Cloud 234

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. Unlike data warehouses, however, data is not transformed before landing in storage.

Artificial Intelligence 221

Artificial Intelligence Storage Analytics Government 221

The Netflix TechBlog

NOVEMBER 12, 2024

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 253

Latency Cache Infrastructure Strategy 253

High Scalability

SEPTEMBER 8, 2018

Anna is not only incredibly fast, it’s incredibly efficient and elastic too: an autoscaling, multi-tier, selectively-replicating cloud service. The issue is that Anna is now orders of magnitude more efficient than competing systems, in addition to being orders of magnitude faster. What's changed ?

Storage 199

Storage Performance AWS Efficiency 199

The Netflix TechBlog

FEBRUARY 13, 2023

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. Media Feature Storage: Amber Storage Media feature computation tends to be expensive and time-consuming. We accomplish this by paving the path to: Accessing and processing media data (e.g.

Media 299

Media Storage Infrastructure Systems 299

Dynatrace

AUGUST 1, 2022

Kubernetes was initially designed with a strong focus on stateless workloads, meaning these workloads do not need to store any persistent data. You quickly realize that it will take ages to fill up the overprovisioned database storage. Two days later, your database runs out of storage in the middle of the night. Dynatrace news.

Storage 200

Storage Database Network Metrics 200

Dynatrace

JUNE 10, 2024

As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Security analytics solutions are designed to handle modern applications that rely on dynamic code and microservices. Dehydrated data has been compressed or otherwise altered for storage in a data warehouse.

Analytics 243

Analytics Network Open Source Hardware 243

The Netflix TechBlog

OCTOBER 27, 2020

The data warehouse is not designed to serve point requests from microservices with low latency. Therefore, we must efficiently move data from the data warehouse to a global, low-latency and highly-reliable key-value store. It leverages Netflix Scheduler for scheduling the Bulldozer Jobs. Moving data with Bulldozer at Netflix.

Latency 254

Latency Storage Big Data Tuning 254

Dynatrace

AUGUST 8, 2019

One of our design principles is to make sure that Dynatrace is as easy to set up and use as possible. Smaller network and Dynatrace cluster storage footprint. The post Rebuilt OneAgent installer for Windows provides more efficient installation appeared first on Dynatrace blog. Dynatrace news. msi-based deployment.

Efficiency 119

Efficiency Network Storage Architecture 119

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 248

Retail Storage Best Practices Architecture 248

The Netflix TechBlog

OCTOBER 19, 2020

Now let’s look at how we designed the tracing infrastructure that powers Edgar. Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage. A second job taps the data feed from the first job, does tail sampling of data and writes traces to the storage system.

Infrastructure 239

Infrastructure Transportation Storage Open Source 239

The Netflix TechBlog

APRIL 26, 2022

We will share how its design has evolved over the years and the lessons learned while building it. To understand Axion’s design, we need to know the various components that interact with it. The motivation has not changed since then; the design has. Design evolution Axion fact store has four components?—?fact

Storage 197

Storage Design Scalability Latency 197

Dynatrace

OCTOBER 17, 2023

AI can help automate tasks, improve efficiency, and identify potential problems before they occur. Data, AI, analytics, and automation are key enablers for efficient IT operations Data is the foundation for AI and IT automation. IT automation also helps improve operational efficiency by automating repetitive tasks.

Monitoring 239

Monitoring Artificial Intelligence Performance Analytics 239

Dynatrace

MAY 26, 2021

Trace-based sampling can help you save storage costs. This can help you save money in storage costs in the long run. After all, it’s key to sample and store traces efficiently while not missing out on important events. OTel is designed to ensure flexibility and stability. So, the design of OTel allows for flexibility.

AWS 224

AWS Strategy Storage Metrics 224

The Morning Paper

JANUARY 28, 2020

Narrowing the gap between serverless and its state with storage functions , Zhang et al., Shredder is " a low-latency multi-tenant cloud store that allows small units of computation to be performed directly within storage nodes. " In from of them is a networking layer, and the in-memory storage layer holds the actual data.

Serverless 99

Serverless Storage Latency Cloud 99

Dynatrace

OCTOBER 4, 2022

As a result, organizations need software to work perfectly to create customer experiences, deliver innovation, and generate operational efficiency. Traditionally, though, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs.

Analytics 204

Analytics Software Software Storage 204

Dynatrace

NOVEMBER 11, 2020

Dynatrace OneAgent deployment and life-cycle management are already widely considered to be industry benchmarks for reliability and efficiency. Note that most of the changes we’ve introduced so far and those that are detailed below are all designed to be invisible to you, taking place entirely automatically in the background.

Best Practices 210

Best Practices Storage Java Benchmarking 210

Highly Scalable

MARCH 1, 2012

First, we should note that SQL and relational model in general were designed long time ago to interact with the end user. And this was where a new evolution of data models began: Key-Value storage is a very simplistic, but very powerful model. The main design theme is “ What answers do I have?” ” .

Database 279

Database Ecommerce Efficiency Engineering 279

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. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

AWS 259

AWS Efficiency Azure Cloud 259

Dynatrace

JUNE 6, 2023

Kubernetes enables efficient resource utilization by easily scaling applications and services based on demand. With the ability to monitor resource utilization metrics such as CPU and memory in real time, teams can optimize their operations, resulting in reduced cost and greater overall efficiency. Automated scaling. Self-healing.

Open Source 205

Open Source Storage Analytics Innovation 205

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 270

Artificial Intelligence Metrics Analytics Storage 270

DZone

JUNE 24, 2024

Objectives Modern AI innovations require proper infrastructure, especially concerning data throughput and storage capabilities. While GPUs drive faster results, legacy storage solutions often lag behind, causing inefficient resource utilization and extended times in completing the project.

Systems 130

Systems Software Software Storage 130

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 279

Database Artificial Intelligence Metrics Monitoring 279

The Netflix TechBlog

DECEMBER 17, 2024

How can we design systems that recognize these nuances and empower every title to shine and bring joy to ourmembers? This led to a suite of fragmented scripts, runbooks, and ad hoc solutions scattered across teamsan approach that was neither sustainable nor efficient. Yet, these pages couldnt be more different.

Traffic 174

Traffic Scalability Strategy Monitoring 174

All Things Distributed

MAY 19, 2017

In many, high-throughput, OLTP style applications the database plays a crucial role to achieve scale, reliability, high-performance and cost efficiency. In a paper published this week at SIGMOD'17 , the Amazon Aurora team presents the design considerations for the new database engine and how they addressed them.

Database 91

Database Design Cloud Storage 91

Dynatrace

JANUARY 31, 2024

Designed to reach beyond petabyte (PB) scale, OpenPipeline will—at its launch— quintuple the ingest throughput from 100 TB a day per tenant to 500 TB a day per tenant. OpenPipeline high-performance filtering and preprocessing provides full ingest and storage control for the Dynatrace platform. Commitment to privacy.

Analytics 203

Analytics Processing Transportation Storage 203

The Netflix TechBlog

MARCH 10, 2020

by Damir Svrtan and Sergii Makagon As the production of Netflix Originals grows each year, so does our need to build apps that enable efficiency throughout the entire creative process. The idea of Hexagonal Architecture is to put inputs and outputs at the edges of our design.

Architecture 346

Architecture Transportation Java Strategy 346

The Netflix TechBlog

JUNE 20, 2024

To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.” SQLMesh, an open-source project born from our collective experience at companies like Airbnb, Apple, Google, and Netflix, is designed to handle the complexities of evolving data pipelines at an internet scale.

Data Engineering 209

Data Engineering Engineering Entertainment Software Engineering 209