Dynatrace

OCTOBER 23, 2023

This leads to a more efficient and streamlined experience for users. Secondly, determining the correct allocation of resources (CPU, memory, storage) to each virtual machine to ensure optimal performance without over-provisioning can be difficult. Challenges with running Hyper-V Working with Hyper-V can come with several challenges.

Efficiency 227

Efficiency Virtualization Hardware Performance 227

DZone

JANUARY 29, 2020

To resolve the problem it was suggested to find more suitable data storage. There are several limitations to store and fetch such data (all restrictions could be found in official documentation ). For some internal reasons well known Amazon S3 bucket was chosen for this purpose. The choice affected the project's unit test base.

Storage 124

Storage Efficiency Testing Database 124

Scalegrid

JANUARY 5, 2024

Enhanced data security, better data integrity, and efficient access to information. Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity.

Efficiency 147

Efficiency Storage Database Scalability 147

Scalegrid

FEBRUARY 8, 2024

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130

Storage Systems Big Data Azure 130

DZone

AUGUST 6, 2021

On a small scale, this isn’t problematic but when dealing with large-scale deployments, organizations end up using lots of computing, storage, and human resources just to manage their indexes. As the database grows in size, so does the index management cost.

Storage 189

Storage Database Systems Efficiency 189

Scalegrid

MAY 13, 2020

High performance, query optimization, open source and polymorphic data storage are the major Greenplum advantages. Greenplum’s high performance eliminates the challenge most RDBMS have scaling to petabtye levels of data, as they are able to scale linearly to efficiently process data. Polymorphic Data Storage. Major Use Cases.

Big Data 321

Big Data Database Artificial Intelligence Open Source 321

The Netflix TechBlog

SEPTEMBER 18, 2024

Our goal was to build a versatile and efficient data storage solution that could handle a wide variety of use cases, ranging from the simplest hashmaps to more complex data structures, all while ensuring high availability, tunable consistency, and low latency. Developers just provide their data problem rather than a database solution!

Latency 254

Latency Storage Cache Efficiency 254

Scalegrid

MAY 11, 2024

MongoDB offers several storage engines that cater to various use cases. The default storage engine in earlier versions was MMAPv1, which utilized memory-mapped files and document-level locking. The newer, pluggable storage engine, WiredTiger, addresses this by using prefix compression, collection-level locking, and row-based storage.

Storage 130

Storage Engineering Cache Database 130

DZone

JUNE 28, 2024

At first, data tiering was a tactic used by storage systems to reduce data storage costs. This involved grouping data that was not accessed as often into more affordable, if less effective, storage array choices. Even though they are quite costly, SSDs and flash can be categorized as high-performance storage classes.

Azure 223

Azure Storage Performance Cloud 223

DZone

SEPTEMBER 14, 2023

In today's data-driven world, efficient data processing plays a pivotal role in the success of any project. In this article, we will delve into strategies to ensure that your data pipeline is resource-efficient, cost-effective, and time-efficient.

Big Data 279

Big Data Processing Games Open Source 279

DZone

OCTOBER 30, 2024

Log-Structured Merge Trees (LSM trees) are a powerful data structure widely used in modern databases to efficiently handle write-heavy workloads. They offer significant performance benefits through batching writes and optimizing reads with sorted data structures.

Strategy 244

Strategy Storage Efficiency Database 244

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. Let’s dive into the various aspects of this abstraction.

Latency 242

Latency Storage Traffic Tuning 242

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

DZone

OCTOBER 6, 2021

But along with advantages and uses, computer vision has its challenges in the department of modern applications, which deep neural networks can address quickly and efficiently. With the soaring demand for computing power and storage, it is challenging to deploy deep neural network applications. Network Compression.

Network 264

Network Storage Efficiency 264

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

MAY 7, 2024

which offers a range of updates: HTTP/2 support: Fluentbit now supports HTTP/2, enabling efficient data transmission with Gzip compression for OpenTelemetry data, enhancing pipeline performance. By default, you have a storage type memory, but you may exceed this buffer limit if you have a lot of data. What’s new in Fluent Bit 3.0

Best Practices 255

Best Practices IoT Metrics Storage 255

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

Dynatrace

OCTOBER 4, 2022

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). This decoupling ensures the openness of data and storage formats, while also preserving data in context. Grail is built for such analytics, not storage.

Artificial Intelligence 278

Artificial Intelligence Analytics Storage Architecture 278

The Morning Paper

MARCH 8, 2020

Building an elastic query engine on disaggregated storage , Vuppalapati, NSDI’20. Snowflake is a data warehouse designed to overcome these limitations, and the fundamental mechanism by which it achieves this is the decoupling (disaggregation) of compute and storage. joins) during query processing. Disaggregation (or not).

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

Dynatrace

OCTOBER 7, 2021

This means you no longer have to provision, scale, and maintain servers to run your applications, databases, and storage systems. Speed is next; serverless solutions are quick to spin up or down as needed, and there are no delays due to limited storage or resource access. AWS offers four serverless offerings for storage.

Serverless 293

Serverless AWS Lambda Storage 293

The Netflix TechBlog

MARCH 8, 2021

They could need a GPU when doing graphics-intensive work or extra large storage to handle file management. It also allows for logic statements to handle situations such as mount this storage in this environment only or only run this script if this file does not exist. Artists need many components to be customized.

Entertainment 307

Entertainment Storage Open Source Hardware 307

Dynatrace

OCTOBER 4, 2022

Several pain points have made it difficult for organizations to manage their data efficiently and create actual value. This approach is cumbersome and challenging to operate efficiently at scale. Teams have introduced workarounds to reduce storage costs. Limited data availability constrains value creation.

Analytics 246

Analytics Artificial Intelligence Storage Serverless 246

Dynatrace

OCTOBER 4, 2022

In most data storage models, indexing engines enable faster access to query logs. But indexing requires schema management and additional storage to be effective, which adds cost and overhead. This can vastly reduce an organization’s storage costs and improve data efficiency. Eliminates team silos.

Analytics 234

Analytics Storage Infrastructure Metrics 234

DZone

APRIL 11, 2023

Modern tech stacks such as Apache Spark, Azure Data Factory, Azure Databricks, and Azure Synapse Analytics offer powerful tools for building optimized data pipelines that can efficiently ingest and process data on the cloud. It provides built-in connectors for various data sources such as databases, file systems, cloud storage, and more.

Azure 246

Azure Analytics Storage Cloud 246

DZone

JULY 20, 2021

Taking a proactive and efficient approach to Kubernetes cluster monitoring can help engineering teams identify and predict many critical problems like CPU outage, memory outage, storage issues well in advance of these issues taking a toll on a business. Introduction.

Monitoring 246

Monitoring Storage Efficiency Engineering 246

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

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

DZone

JUNE 15, 2023

Moreover, the process of collecting these profiles introduces overhead during application runtime and necessitates the storage and visualization of significantly large datasets. However, it does not provide visibility into the operations taking place at the code level, such as method, socket, and thread states.

Latency 253

Latency Storage Strategy Metrics 253

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

JULY 29, 2022

With the right log management and observability platform, IT teams can efficiently identify the root cause of problems during these peak times and maintain three-nines of availability — or 99.98%. Cold storage and rehydration. Cold storage and rehydration. Inadequate context.

Analytics 226

Analytics Storage Retail Monitoring 226

The Netflix TechBlog

OCTOBER 27, 2020

Therefore, we must efficiently move data from the data warehouse to a global, low-latency and highly-reliable key-value store. What is Bulldozer Bulldozer is a self-serve data platform that moves data efficiently from data warehouse tables to key-value stores in batches. Figure 1 shows how we use Bulldozer to move data at Netflix.

Latency 254

Latency Storage Big Data Tuning 254

Dynatrace

SEPTEMBER 26, 2022

Progressive rollouts, rollbacks, storage orchestration, bin packing, self-healing, cost efficiency, and access to the Cloud Native Computing Foundation (CNCF) ecosystem carry heavy observability challenges. Unlike evictions from resource exhaustion on a node, this event resulted from ephemeral storage limits exceeded on the pod.

Storage 311

Storage Engineering Traffic Infrastructure 311

Dynatrace

AUGUST 1, 2022

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. Therefore, you don’t know your current growth rate and can’t estimate the required storage for keeping the database up and running for the next month.

Storage 200

Storage Database Network Metrics 200

Dynatrace

AUGUST 8, 2019

Smaller network and Dynatrace cluster storage footprint. The post Rebuilt OneAgent installer for Windows provides more efficient installation appeared first on Dynatrace blog. It now provides reliability and ease of deployment even in tricky situations like hardened Windows OS settings or in the presence of strict antivirus rules.

Efficiency 119

Efficiency Network Storage Architecture 119

DZone

AUGUST 16, 2023

Caching is the process of storing frequently accessed data or resources in a temporary storage location, such as memory or disk, to improve retrieval speed and reduce the need for repetitive processing. Bandwidth optimization: Caching reduces the amount of data transferred over the network, minimizing bandwidth usage and improving efficiency.

Cache 246

Cache Scalability Performance Latency 246