CIO

FEBRUARY 17, 2025

By evaluating metrics like lead time (time to start an action) and cycle time (time spent on productive work), utilities can identify repetitive tasks that can be automated. First, set clear objectives and success metrics. For utilities in particular, it helps teams identify high-impact opportunities.

Azure 117

Azure Metrics Systems Review Technical Review 117

Cloudera

DECEMBER 4, 2024

Ready Flows: Accelerate development with pre-built templates for common data integration and processing tasks, freeing up developers to focus on higher-value activities. empowers data engineers to build and deploy data pipelines faster, accelerating time-to-value for the business.

Metrics 93

Metrics Generative AI Open Source Data Engineering 93

Xebia

DECEMBER 23, 2024

It must be a joint effort involving everyone who uses the platform, from data engineers and scientists to analysts and business stakeholders. Platform Level: At this level, organizations should focus on understanding the total expenditure across their entire data platform.

Data 130

Data Storage Culture Resources 130

Xebia

DECEMBER 23, 2024

It must be a joint effort involving everyone who uses the platform, from data engineers and scientists to analysts and business stakeholders. Platform Level: At this level, organizations should focus on understanding the total expenditure across their entire data platform.

Data 130

Data Storage Culture Resources 130

TechCrunch

MAY 24, 2022

Preql founders Gabi Steele and Leah Weiss were data engineers in the early days at WeWork. They later opened their own consultancy to help customers build data stacks, and they saw a stubborn consistency in the types of information their clients needed. They don’t stop there though.

Data 202

Data Metrics Data Engineering Storage 202

TechCrunch

JUNE 2, 2021

The core idea behind Iterative is to provide data scientists and data engineers with a platform that closely resembles a modern GitOps-driven development stack. After spending time in academia, Iterative co-founder and CEO Dmitry Petrov joined Microsoft as a data scientist on the Bing team in 2013.

Artificial Inteligence 249

Artificial Inteligence Machine Learning Open Source Data Engineering 249

CIO

JUNE 7, 2022

A data scientist is a mix of a product analyst and a business analyst with a pinch of machine learning knowledge, says Mark Eltsefon, data scientist at TikTok. And in a mature ML environment, ML engineers also need to experiment with serving tools that can help find the best performing model in production with minimal trials, he says.

Artificial Inteligence 218

Artificial Inteligence Technical Review Fractional CTO Data Engineering 218

TechCrunch

AUGUST 4, 2022

Most BI tools are thin applications with no data engine of their own, and only as fast as the database they sit atop. Rill, on the other hand, is a thick application that comes with its own embedded in-memory OLAP engine ( DuckDB in Rill Developer, and Apache Druid in Rill Cloud). Rill has an extremely opinionated view.

Open Source 246

Open Source Metrics Enterprise Business Intelligence 246

Netflix Tech

MAY 9, 2025

A robust reporting system to share campaign reports with advertisers, combined with metrics data collection, helps assess the delivery and effectiveness of the campaign. This system was constructed using ad playback telemetry, which allowed us to gather essential metrics from these ad sessions.

Metrics 71

Metrics Advertising Weak Development Team Systems Review 71

TechCrunch

MARCH 23, 2022

At that time, the scrappy data analytics company had scooped up $3.5 million in funding to develop its tool for what happens after you’ve collected a bunch of data, namely assembling and organizing it so the data can be analyzed. Data collection isn’t the problem: It’s what companies are doing with it.

Open Source 213

Open Source Data Engineering Strategic Planning Analytics 213

CIO

NOVEMBER 27, 2024

Not cleaning your data enough causes obvious problems, but context is key.

Data 211

Data Enterprise Weak Development Team Software Review 211

AWS Machine Learning - AI

NOVEMBER 15, 2024

The Principal AI Enablement team, which was building the generative AI experience, consulted with governance and security teams to make sure security and data privacy standards were met. Model monitoring of key NLP metrics was incorporated and controls were implemented to prevent unsafe, unethical, or off-topic responses.

Generative AI 113

Generative AI AWS Groups Artificial Inteligence 113

TechCrunch

SEPTEMBER 1, 2022

. “Our thesis was that while companies collect mountains of data, the return on investment on it remains low because it’s predominantly used in dashboards and reporting, not daily actions and automation,” Akmal told TechCrunch in an email interview. Falkon’s platform tries to unify a company’s go-to-market data (e.g.

Weak Development Team 223

Weak Development Team Marketing Analytics Business Intelligence 223

AWS Machine Learning - AI

MARCH 13, 2025

MaestroQA also offers a logic/keyword-based rules engine for classifying customer interactions based on other factors such as timing or process steps including metrics like Average Handle Time (AHT), compliance or process checks, and SLA adherence. Success metrics The early results have been remarkable.

Generative AI 113

Generative AI CTO Coach AWS Artificial Inteligence 113

Xebia

OCTOBER 25, 2024

Bo Lemmers, Analytics Engineer here at Xebia, and Mike Kamysz, Data Engineer at The Data Institute kick off the series with: “ Why can’t I just query the raw data? ” When you query raw data, it’s like having a blank canvas. You can create your own metrics, dimensions, and transformations on the fly.

Data 130

Data Metrics Analytics Quality Assurance 130

CIO

NOVEMBER 9, 2022

Having that roadmap from the start helps to trim down and focus on the actual metrics to create. Have a data governance plan as well to validate and keep the metrics clean. As soon as one metric is not accurate it is hard to get the buy-in again, so routinely confirming accuracy on all analytics is extremely important.”

Analytics 205

Analytics Metrics Government Business Intelligence 205

TechCrunch

AUGUST 8, 2022

It’s evidently been tough to break through the industry — Eilon declined to reveal Equalum’s revenue metrics or the size of the startup’s customer base, and Equalum doesn’t plan to hire this year. But he emphasized the lucrativeness of the opportunity. billion in 2029 — up from $11.94 billion in 2022. .”

Company 191

Company Data Cloud Google Cloud 191

CIO

APRIL 12, 2023

Machine learning models (algorithms that comb through data to recognize patterns or make decisions) rely on the quality and reliability of data created and maintained by application developers, data engineers, SREs, and data stewards. What metrics are used to understand the business impact of real-time AI?

Artificial Inteligence 218

Artificial Inteligence Artificial Intelligence Machine Learning Agile 218

TechEmpower CTO

NOVEMBER 13, 2023

This isn’t just our opinion - our startup metrics prove it! To get to what’s right for you, you need a tech partner with a deep understanding of your business needs, software development experience, data engineering skills and AI expertise. Everyone struggles with empty text boxes.

Generative AI 295

Generative AI Artificial Inteligence Real Estate Education 295

Erik Bernhardsson

APRIL 1, 2021

It's one of the largest startups in NYC (by several metrics, like valuation or headcount) and it has a world class engineering team that makes me insanely proud. I've spent most of my career working in data in some shape or form. Data as a subfield of software engineering has a crazy growth rate.

Data Engineering 162

Data Engineering Engineering Blockchain Software Engineering 162

Honeycomb

APRIL 21, 2025

The idea that telemetry data needs to be managed, or needs a strategy, draws a lot of inspiration from the data world (as in, BI and Data Engineering). Your company most likely has a data team that manages the data warehouse(s), data pipelines, data sources, and reporting tools.

Strategy 52

Strategy Data Technical Review Software Review 52

Kentik

NOVEMBER 16, 2015

KDE handles over 10B flow records/day with a microservice architecture that's optimized using metrics. Here at Kentik, our Kentik Detect service is powered by a multi-tenant big data datastore called Kentik Data Engine. And that leads us to metrics. Health checks and series metrics. The life of a query.

Metrics 40

Metrics Microservices Linux Architecture 40

TechCrunch

SEPTEMBER 29, 2022

The startup, built by Stiglitz, Sourabh Bajaj , and Jacob Samuelson , pairs students who want to learn and improve on highly technical skills, such as devops or data science, with experts. Edtech’s search for the magic metric. Some classes, like this SQL crash course , are even taught by CoRise employees. It has a 68 NPS score.

Course 180

Course Technical Review Artificial Inteligence Machine Learning 180

AWS Machine Learning - AI

MARCH 18, 2025

Additionally, the complexity increases due to the presence of synonyms for columns and internal metrics available. I am creating a new metric and need the sales data. With 7 years of experience in developing data solutions, he possesses profound expertise in data visualization, data modeling, and data engineering.

Artificial Inteligence 109

Artificial Inteligence Applications Generative AI Off-The-Shelf 109

Netflix Tech

FEBRUARY 14, 2025

We accomplish this by gathering detailed column-level metrics that offer insights into the state and quality of each impression. These metrics include everything from validating identifiers to checking that essential columns are properly filled.

Systems Review 66

Systems Review Technical Review Data Metrics 66

Cloudera

NOVEMBER 17, 2021

You know the one, the mathematician / statistician / computer scientist / data engineer / industry expert. Some companies are starting to segregate the responsibilities of the unicorn data scientist into multiple roles (data engineer, ML engineer, ML architect, visualization developer, etc.),

Artificial Inteligence 124

Artificial Inteligence Machine Learning Hotels Data Engineering 124

AWS Machine Learning - AI

JUNE 20, 2024

Examples sent to the LLM are based on the database data, which makes it even harder to control the requests sent to the LLM and assure quality. The solution: A data science approach In data science, it’s common to develop a model and fine tune it using experimentation.

Artificial Inteligence 115

Artificial Inteligence UI/UX Generative AI Construction 115

Netflix Tech

SEPTEMBER 10, 2019

ABlaze: The standard view of analyses in the XP UI Suppose you’re running a new video encoding test and theorize that the two new encodes should reduce play delay, a metric describing how long it takes for a video to play after you press the start button. Our data scientists faced numerous challenges in our previous infrastructure.

Analysis 88

Analysis Metrics Software Review Testing 88

Erik Bernhardsson

DECEMBER 6, 2022

I'm deliberately vague about what exact role I mean here: take it to mean data engineers, data scientists, ML engineers, analytics engineers, and maybe more roles. I will be posting a lot more about it! I hope to write a blog bost about this in the future! ↩︎

CTO Coach 242

CTO Coach Fractional CTO Software Engineering Serverless 242

Altexsoft

JULY 23, 2020

It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in data engineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development. MLOps lies at the confluence of ML, data engineering, and DevOps.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

Altexsoft

JUNE 29, 2021

MLEs are usually a part of a data science team which includes data engineers , data architects, data and business analysts, and data scientists. Who does what in a data science team. Machine learning engineers are relatively new to data-driven companies.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

Cloudera

OCTOBER 5, 2020

Cloudera Data Platform Powered by NVIDIA RAPIDS Software Aims to Dramatically Increase Performance of the Data Lifecycle Across Public and Private Clouds. This exciting initiative is built on our shared vision to make data-driven decision-making a reality for every business. Compared to previous CPU-based architectures, CDP 7.1

Enterprise 93

Enterprise Cloud Data Artificial Inteligence 93

Kentik

FEBRUARY 27, 2023

The need for speed The Kentik Data Explorer is Kentik’s interface between you as an engineer, whether that’s network, systems, cloud, security, or SRE, and the database of information you’ve collected with the Kentik platform. But the real key here is that the Kentik Data Explorer was purpose-built for querying a massive database.

Network 98

Network Data WAN Metrics 98

Hacker Earth Developers Blog

JUNE 26, 2019

Also, the candidate should have knowledge of the different metrics used to evaluate the performance of a model. . The candidate should have a basic understanding of business or the industry in which he is applying as a data scientist. To pull data, candidates should be able to understand Relational Databases. Neural Networks .

Data 200

Data How To Artificial Inteligence Machine Learning 200

Netflix Tech

JANUARY 11, 2022

To learn about Analytics and Viz Engineering, have a look at Analytics at Netflix: Who We Are and What We Do by Molly Jackman & Meghana Reddy and How Our Paths Brought Us to Data and Netflix by Julie Beckley & Chris Pham. Curious to learn about what it’s like to be a Data Engineer at Netflix? Sensitivity analysis.

Data 98

Data Metrics Testing Analysis 98

Tenable

NOVEMBER 3, 2022

When our data engineering team was enlisted to work on Tenable One, we knew we needed a strong partner. When Tenable’s product engineering team came to us in data engineering asking how we could build a data platform to power the product, we knew we had an incredible opportunity to modernize our data stack.

Data 98

Data AWS Storage Data Engineering 98

CIO

MARCH 21, 2022

Quantitative analysis: Quantitative analysis improves your ability to run experimental analysis, scale your data strategy, and help you implement machine learning. Product intuition: Understanding products will help you perform quantitative analysis and better predict system behavior, establish metrics, and improve debugging skills.

Analytics 205

Analytics Data Technical Review Analysis 205