The Morning Paper

SEPTEMBER 8, 2019

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., Microsoft’s big data clusters have 10s of thousands of machines, and are used by thousands of users to run some pretty complex queries. A small example might help bring this to life. VLDB’19. Universe(0.5,

Big Data 57

Big Data Analytics Latency Azure 57

The Netflix TechBlog

NOVEMBER 20, 2023

We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. IPS provides the incremental processing support with data accuracy, data freshness, and backfill for users and addresses many of the challenges in workflows. past 3 hours or 10 days).

Processing 210

Processing Big Data Efficiency Engineering 210

Dynatrace

JULY 6, 2022

Vulnerability management is one example of a DevSecOps workflow that teams should automate to ensure vulnerability scans run regularly. Ultimately, IT automation can deliver consistency, efficiency, and better business outcomes for modern enterprises. IT automation tools can achieve enterprise-wide efficiency. Read eBook now!

Artificial Intelligence 198

Artificial Intelligence Tuning Strategy Big Data 198

The Netflix TechBlog

OCTOBER 28, 2021

Netflix’s unique work culture and petabyte-scale data problems are what drew me to Netflix. 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. You can learn more about it from my talk at the Flink forward conference.

Data Engineering 173

Data Engineering Engineering Big Data Software Engineering 173

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

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. For example, consider the adoption of a multicloud framework that enables companies to use best-fit clouds for important operational tasks.

Artificial Intelligence 294

Artificial Intelligence Cloud Innovation Strategy 294

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

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 196

Analytics Infrastructure Storage Efficiency 196

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 243

Analytics Artificial Intelligence Storage Serverless 243

The Netflix TechBlog

JULY 26, 2021

At Netflix Studio, teams build various views of business data to provide visibility for day-to-day decision making. With dependable near real-time data, Studio teams are able to track and react better to the ever-changing pace of productions and improve efficiency of global business operations using the most up-to-date information.

Big Data 253

Big Data Government Analytics Processing 253

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

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 230

Cloud Monitoring Best Practices Infrastructure 230

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 Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., ASPLOS’19. It then provides the cluster manager with recommendations on how to avoid the performance degradation altogether.

Big Data 45

Big Data Cloud Performance Hardware 45

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. Let’s say, for example, an application is experiencing a slowdown in receiving its search requests. Deterministic AI.

DevOps 205

DevOps Artificial Intelligence Healthcare Innovation 205

Dynatrace

FEBRUARY 16, 2023

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022. Logs on Grail Log data is foundational for any IT analytics.

Analytics 195

Analytics Innovation Metrics Database 195

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. Learning more Interested in learning more about data roles at Netflix? 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 151

Azure Cloud Big Data Virtualization 151

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.

Cloud 104

Cloud Big Data Latency Architecture 104

The Netflix TechBlog

NOVEMBER 3, 2023

An example of a Data Mesh pipeline which moves and transforms data using Union, GraphQL Enrichment, and Column Rename Processor before writing to an Iceberg table. The existing Data Mesh Processors have a lot of overlap with SQL.

Processing 265

Processing Engineering Infrastructure Latency 265

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 88

Open Source Storage Database Big Data 88

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

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 264

Tuning Architecture Monitoring Big Data 264

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.” Let’s say, for example, an application is experiencing a slowdown in receiving its search requests. What is AIOps?

Artificial Intelligence 221

Artificial Intelligence DevOps Innovation Metrics 221

The Netflix TechBlog

APRIL 29, 2019

Demand Engineering Demand Engineering is responsible for Regional Failovers , Traffic Distribution, Capacity Operations and Fleet Efficiency of the Netflix cloud. One example is the Spectator Python client library, a library for instrumenting code to record dimensional time series metrics.

Open Source 279

Open Source Network Infrastructure Big Data 279

The Netflix TechBlog

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

Storage 203

Storage Latency Efficiency Data Engineering 203

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

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. Fraud.net is a good example of this.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

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

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

SEPTEMBER 5, 2013

Over the past few years, two important trends that have been disrupting the database industry are mobile applications and big data. The explosive growth in mobile devices and mobile apps is generating a huge amount of data, which has fueled the demand for big data services and for high scale databases.

Big Data 98

Big Data Mobile Latency Database 98

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

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 97

Hardware Storage Big Data Blockchain 97

Abhishek Tiwari

DECEMBER 27, 2017

It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake. Databricks Delta is a perfect example of this class. Based on data access pattern i.e. hot, warm and cold Alluxio makes.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

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

Highly Scalable

MARCH 1, 2012

For example, a user account can be modeled as a set of entries with composite keys like UserID_name, UserID_email, UserID_messages and so on. For example, Jeans attributes are not consistent across brands and specific for each manufacturer. Messages can be grouped into buckets, for example, daily buckets.

Database 279

Database Ecommerce Efficiency Engineering 279

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. Examples are Agile testing, TDD, automation testing, regression testing, etc. Examples are DevOps, AWS, Big Data, Testing as Service, testing environments.

Cloud 67

Cloud Testing Testing Tools Internet 67