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. However, this cannot be done without efficient, scalable data analytics.

Big Data 111

Big Data Analytics AWS Cloud 111

The Netflix TechBlog

JUNE 1, 2021

For example?—?clinical clinical data was often small enough to fit into memory on an average computer and only in rare cases would its computation require any technical ingenuity or massive computing power. Furthermore, engineering velocity was often sacrificed owing to rigid processes. You’re in the right place!

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 130

Dynatrace

JULY 6, 2020

Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. Let’s look at the Azure DB for MariaDB overview as an example. See the health of your big data resources at a glance. Azure Virtual Network Gateways.

Azure 148

Azure Cloud Big Data Virtualization 148

ScaleOut Software

JULY 19, 2021

This architecture does not apply computing resources to track the myriad data sources sending telemetry and continuously look for issues and opportunities that need immediate responses.

IoT 78

IoT Analytics Big Data Architecture 78

Scalegrid

MAY 13, 2020

Greenplum Database is a massively parallel processing (MPP) SQL database that is built and based on PostgreSQL. It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data.

Big Data 321

Big Data Database Artificial Intelligence Open Source 321

The Netflix TechBlog

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

Processing 210

Processing Big Data Efficiency Engineering 210

Percona

JANUARY 19, 2023

For instance, in Percona Managed Services , we have many clients with TBs worth of data that are well performant. In this blog post, we will review key topics to consider for managing large datasets more efficiently in MySQL. InnoDB will sort the data in primary key order, and that will serve to reference actual data pages on disk.

Open Source 87

Open Source Storage Database Big Data 87

All Things Distributed

JUNE 26, 2016

Here are the benefits of a comprehensive platform, with customer examples: A connected platform to sense the business environment. Examples of continuous sensing are found in the managed cloud platform built by Rachio on AWS IoT to enable the secure interaction of its connected devices with cloud applications/other devices.

AWS 90

AWS Cloud Healthcare Blockchain 90

All Things Distributed

DECEMBER 20, 2017

In today's era of global digitalization there are many examples that show that IT does matter. In this way, designers are part of an ecosystem in which the functionalities of simulations, data and people come together, enabling them to develop better products faster. More than mere support.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

Testsigma

MAY 26, 2021

The test results are a huge set of data and they need to be matched against the expected results, which are again stored in files. . Let us see a few scenarios where data-driven testing is useful in providing a quality product. Scenario 1: Tabular data . Scenario 2: Data Arrays. Example: E-commerce applications.

Testing 70

Testing Healthcare Performance Testing Website 70

Testsigma

MAY 14, 2021

However, the primary goal of traditional testing and cloud-based testing remains the same i.e., to deliver high-quality and efficient software. It provides better and simple disaster recovery because the process is automated. What are the areas which will be affected and need proper planning and management during this process?

Cloud 67

Cloud Testing Testing Tools Internet 67

Scalegrid

FEBRUARY 8, 2024

These elements work together to spread data over several locations physically distributed, possibly extending across different data centers while optimizing available storage resources. This process effectively duplicates essential parts of information to safeguard against potential loss.

Storage 130

Storage Systems Big Data Azure 130

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 volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Dynatrace

MAY 1, 2023

IT operations analytics is the process of unifying, storing, and contextually analyzing operational data to understand the health of applications, infrastructure, and environments and streamline everyday operations. ITOA automates repetitive cloud operations tasks and streamlines the flow of analytics into decision-making processes.

Analytics 188

Analytics Artificial Intelligence Big Data Open Source 188

Testsigma

MAY 7, 2020

IBM Big Data and Analytics Hub website cited a case study, where a US insurance company was estimating 15% of their testing efforts to be just test data collection for the backend system and the frontend system. The test data management for the company had become a big problem and had to be solved.

Testing 60

Testing Storage Database Processing 60

The Netflix TechBlog

JULY 26, 2021

Netflix is known for its loosely coupled microservice architecture and with a global studio footprint, surfacing and connecting the data from microservices into a studio data catalog in real time has become more important than ever. With the latest Data Mesh Platform, data movement in Netflix Studio reaches a new stage.

Big Data 253

Big Data Government Analytics Processing 253

Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. To achieve these AIOps benefits, comprehensive AIOps tools incorporate four key stages of data processing: Collection. Aggregation.

Artificial Intelligence 285

Artificial Intelligence Cloud Innovation Strategy 285

Dynatrace

APRIL 20, 2023

With more automated approaches to log monitoring and log analysis, however, organizations can gain visibility into their applications and infrastructure efficiently and with greater precision—even as cloud environments grow. “The weakness of a data lake is they fail when you need to access them fast,” Pawlowski said.

Analytics 187

Analytics Infrastructure Storage Efficiency 187

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. CloudOps includes processes such as incident management and event management. The four stages of data processing. Analyze the data.

DevOps 196

DevOps Artificial Intelligence Healthcare Innovation 196

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. The processed data is typically stored as data warehouse tables in AWS S3. Moving data with Bulldozer at Netflix.

Latency 243

Latency Storage Big Data Tuning 243

Highly Scalable

JUNE 5, 2012

From a functional point of view, we needed mainly a standard boolean query processing, so it was possible to use Solr/Lucene as a platform. It is intuitively clear that performance of intersection may be improved by processing of multiple elements at once using SIMD instructions. Vectorized Intersection. in this article.

C++ 102

C++ Java Performance Testing Efficiency 102

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?

Artificial Intelligence 212

Artificial Intelligence DevOps Innovation Metrics 212

Dynatrace

JANUARY 11, 2023

As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. For example, uptime detection can identify database instability and help to improve mean time to restoration. What is cloud monitoring?

Cloud 223

Cloud Monitoring Best Practices Infrastructure 223

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 186

Analytics Innovation Metrics Database 186

The Netflix TechBlog

MARCH 25, 2019

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

The Netflix TechBlog

DECEMBER 21, 2020

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Then deep dive into the merging use case of AutoOptimize and share some results and benefits.

Storage 203

Storage Latency Efficiency Data Engineering 203

Dynatrace

OCTOBER 4, 2022

Several pain points have made it difficult for organizations to manage their data efficiently and create actual value. Limited data availability constrains value creation. Traditional solutions and approaches are inefficient given the number of manual tasks that are required for effective log data ingest.

Analytics 234

Analytics Artificial Intelligence Storage Serverless 234

KeyCDN

OCTOBER 2, 2018

That’s why we’ve compiled an exhaustive list of web development blogs and newsletters to make this process easier. It’s awesome for discovering how grid systems, CSS animation, Big Data, etc all play roles in real-world web design. Be sure to check it out if your dev process needs a creative kick in the pants.

Development 75

Development Website Design Code 75

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 202

Java Scalability Traffic Architecture 202

The Netflix TechBlog

FEBRUARY 16, 2021

For example, the mobile plan launch in India and Southeast Asia was a huge success. 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. three plans and one offer homogeneously applied to all regions.

Mobile 209

Mobile Engineering Infrastructure Scalability 209

The Morning Paper

OCTOBER 26, 2020

On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. PVLDB’20. Emphasis mine ).

Cloud 104

Cloud Big Data Latency Architecture 104

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. For example, invoking a webhook that creates a ticket in an ITSM system. But that didn’t work for me. Why am I explaining this?

Tuning 263

Tuning Architecture Monitoring Big Data 263

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

All Things Distributed

DECEMBER 13, 2016

Take Peterborough City Council as an example. The council has deployed IoT Weather Stations in Schools across the City and is using the sensor information collated in a Data Lake to gain insights on whether the weather or pollution plays a part in learning outcomes.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

O'Reilly

SEPTEMBER 19, 2023

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.

Hardware 99

Hardware Storage Big Data Blockchain 99

Highly Scalable

MAY 1, 2012

Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. what is the cardinality of the data set)?

Analytics 191

Analytics Traffic Big Data Efficiency 191

Highly Scalable

MARCH 1, 2012

One of the most significant shortcomings of the Key-Value model is a poor applicability to cases that require processing of key ranges. In this article I describe several well-known data structures that are not specific for NoSQL, but are very useful in practical NoSQL modeling. Processing complexity VS total data volume.

Database 279

Database Ecommerce Efficiency Engineering 279