CIO

MARCH 13, 2025

Gen AI-related job listings were particularly common in roles such as data scientists and data engineers, and in software development. In the Randstad survey, for example, 35% of people have been offered AI training up from just 13% in last years survey. For example, the District of Columbia has already invested $1.2

Part-Time VPE 205

Part-Time VPE Weak Development Team Fractional VPE Fractional CTO 205

Xebia

OCTOBER 18, 2024

Dbt is a popular tool for transforming data in a data warehouse or data lake. It enables data engineers and analysts to write modular SQL transformations, with built-in support for data testing and documentation. Jaffle Shop Demo To demonstrate our setup, we’ll use the jaffle_shop example.

Open Source 162

Open Source AWS Government Technical Review 162

CIO

MARCH 13, 2025

A great example of this is the semiconductor industry. Educating and training our team With generative AI, for example, its adoption has surged from 50% to 72% in the past year, according to research by McKinsey. For example, when we evaluate third-party vendors, we now ask: Does this vendor comply with AI-related data protections?

Company 186

Company Generative AI Insurance Education 186

Netflix Tech

DECEMBER 14, 2023

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community! In this video, Sr.

Data Engineering 106

Data Engineering Engineering Data Software Engineering 106

CIO

FEBRUARY 19, 2025

Registered investment advisors, for example, have to jump over a few hurdles when deploying new technologies. For example, a faculty member might want to teach a new section of a course. The most common pattern Im seeing is custom-building capabilities and leveraging other systems for data, she says.

Google Cloud 206

Google Cloud Survey CTO Coach Software Development 206

CIO

APRIL 30, 2025

Much of this work has been in organizing our data and building a secure platform for machine learning and other AI modeling. We also built an organization skilled in the data engineering and data science required for AI. Well continue to need data engineering and analytics, data science, and prompt engineering.

Airlines 124

Airlines Strategy ChatGPT Software Review 124

CIO

DECEMBER 12, 2024

Confidence from business leaders is often focused on the AI models or algorithms, Erolin adds, not the messy groundwork like data quality, integration, or even legacy systems. For example, one of BairesDevs clients was surprised when it spent 30% of an AI project timeline integrating legacy systems, Erolin says.

Data 201

Data Survey Artificial Inteligence Education 201

CIO

NOVEMBER 27, 2024

The team should be structured similarly to traditional IT or data engineering teams. For example, there should be a clear, consistent procedure for monitoring and retraining models once they are running (this connects with the People element mentioned above).

Architecture 193

Architecture Artificial Inteligence Data Development Team Review 193

Xebia

APRIL 9, 2025

For example, events such as Twitters rebranding to X, and PySparks rise in the data engineering realm over Spark have all contributed to this decline. In my opinion, sbt (Simple Build Tool) is a perfect example of this evolution. Various business decisions have altered its public perception.

Systems Review 130

Systems Review Programming Technical Review Engineering 130

Cloudera

FEBRUARY 14, 2022

When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale. Each unlocking value in the data engineering workflows enterprises can start taking advantage of. Usage Patterns.

Data Engineering 113

Data Engineering Engineering Data Storage 113

Xebia

DECEMBER 23, 2024

For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources.

Data 130

Data Storage Culture Resources 130

Cloudera

SEPTEMBER 17, 2020

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

Data Engineering 104

Data Engineering Engineering Data Tools 104

Xebia

DECEMBER 23, 2024

For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources.

Data 130

Data Storage Culture Resources 130

CIO

NOVEMBER 27, 2024

Not cleaning your data enough causes obvious problems, but context is key. “A A lot of organizations spend a lot of time discarding or improving zip codes, but for most data science, the subsection in the zip code doesn’t matter,” says Kashalikar. That’s a classic example of too much good is wasted.”

Data 211

Data Enterprise Weak Development Team Software Review 211

O'Reilly Media - Data

SEPTEMBER 24, 2018

Data science is the sexy thing companies want. The data engineering and operations teams don't get much love. The organizations don’t realize that data science stands on the shoulders of DataOps and data engineering giants. Let's call these operational teams that focus on big data: DataOps teams.

Enterprise 259

Enterprise Data Big Data Data Engineering 259

CIO

JANUARY 22, 2025

For example, most people now use AI to take meeting notes. According to Leon Roberge, CIO for Toshiba America Business Solutions and Toshiba Global Commerce Solutions, technology leaders should become more visible to the business and lead by example to their teams. Each company has its own way of doing business and its own data sets.

Weak Development Team 192

Weak Development Team Education Meeting Data 192

Altexsoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering. Feature engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Netflix Tech

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions.

Data Engineering 108

Data Engineering Engineering Data Systems Review 108

TechCrunch

MARCH 7, 2023

Once a successful proof of concept is made, the team often hits a wall regarding its data management. The organization may not collect, store or manage the data in a way that is “AI friendly.” Once a few examples are completed manually, the business can start planning the AI’s path to production.

Weak Development Team 246

Weak Development Team Case Study Data Engineering ChatGPT 246

CIO

DECEMBER 10, 2024

Choreographing data, AI, and enterprise workflows While vertical AI solves for the accuracy, speed, and cost-related challenges associated with large-scale GenAI implementation, it still does not solve for building an end-to-end workflow on its own.

Artificial Inteligence 174

Artificial Inteligence Off-The-Shelf Insurance Analytics 174

TechCrunch

JULY 12, 2022

Machine learning can provide companies with a competitive advantage by using the data they’re collecting — for example, purchasing patterns — to generate predictions that power revenue-generating products (e.g. e-commerce recommendations). “It also enables companies to generate more accurate predictions.

Artificial Inteligence 249

Artificial Inteligence Machine Learning Marketing Data Engineering 249

TechCrunch

APRIL 25, 2022

In thinking about features, it can be helpful to visualize a table, where the data used by AI systems is organized into rows of examples (data from which the system learns to make predictions) and columns of attributes (data describing those examples). They serve as the interface between data and [AI] models.”

Artificial Inteligence 225

Artificial Inteligence Machine Learning Data Engineering Enterprise 225

Cloudera

OCTOBER 19, 2021

A simple example of this would be parameterizing SQL query within the CDW operator. the developer can include placeholders for different parts of the query, for example the SELECT expression or the table being referenced in the FROM section. Using the special syntax {{.}} SELECT . {{ dag_run.conf['conf1'] }}.

Data Engineering 120

Data Engineering Engineering Data Virtualization 120

TechCrunch

OCTOBER 13, 2022

Data engineers have a big problem. Almost every team in their business needs access to analytics and other information that can be gleaned from their data warehouses, but only a few have technical backgrounds. The New York-based startup announced today that it has raised $7.6

Operating System 233

Operating System Technical Review Analytics Systems Review 233

Cloudera

DECEMBER 2, 2024

Today, Cloudera Data Engineering, a data service that streamlines and scales data pipeline development, is available with support for AWS Graviton processors. Cloudera Data Engineering is just the start. Give it a try today.

Sustainability 89

Sustainability AWS Analytics Infrastructure 89

CIO

FEBRUARY 25, 2025

Its about taking the data you already have and asking: How can we use this to do business better? For example, if a customer service rep is empowered with real-time data, they can anticipate a customers needs and offer tailored solutions. Mike Vaughan serves as Chief Data Officer for Brown & Brown Insurance.

Data 136

Data Innovation Insurance Culture 136

CIO

APRIL 1, 2025

Job titles like data engineer, machine learning engineer, and AI product manager have supplanted traditional software developers near the top of the heap as companies rush to adopt AI and cybersecurity professionals remain in high demand. An example of the new reality comes from Salesforce.

Marketing 152

Marketing Software Development Software Development 152

CIO

AUGUST 4, 2022

For example, a football team consisting of 11 quarterbacks would get crushed in a game against talented linemen, running backs and receivers. Interestingly, many companies do just that, creating a disconnect between data science teams and IT/DevOps when it comes to AI development. Great teams incorporate a variety of skill sets.

Artificial Inteligence 214

Artificial Inteligence Artificial Intelligence DevOps Data Engineering 214

CIO

NOVEMBER 7, 2024

For example, I was trying to understand underwriting in our Canadian operations. In that example, it was better to just go and understand what is happening locally. It covers essential topics like artificial intelligence, our use of data models, our approach to technical debt, and the modernization of legacy systems.

Artificial Inteligence 180

Artificial Inteligence Development Generative AI Artificial Intelligence 180

Xebia

FEBRUARY 28, 2025

The development- and operations world differ in various aspects: Development ML teams are focused on innovation and speed Dev ML teams have roles like Data Scientists, Data Engineers, Business owners. Taking into account automating operations related to all of the code, data and model is what makes MLOps different from DevOps.

Technical Review 182

Technical Review Weak Development Team Machine Learning Artificial Inteligence 182

TechCrunch

FEBRUARY 7, 2022

Throughout the COVID-19 recovery era, location data is set to be a core ingredient for driving business intelligence and building sustainable consumer loyalty. Scalable and data-rich location services are helping consumer-facing business drive transformation and growth along three strategic fronts: Creating richer consumer experiences.

Business Intelligence 230

Business Intelligence AWS Data Engineering Sustainability 230

Xebia

NOVEMBER 21, 2024

Here is a basic example to get started with: manifest_checks: - name: check_model_directories include: ^models permitted_sub_directories: - intermediate - marts - staging This check will validate that all your models exist in one of the sub-directories specified in the permitted_sub_directories key. Loaded config from dbt-bouncer-example.yml.

Weak Development Team 130

Weak Development Team Testing Analytics Engineering 130

TechCrunch

NOVEMBER 4, 2022

I know this because I used to be a data engineer and built extract-transform-load (ETL) data pipelines for this type of offer optimization. Part of my job involved unpacking encrypted data feeds, removing rows or columns that had missing data, and mapping the fields to our internal data models.

Data 218

Data Storage Analytics Data Engineering 218

CIO

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 195

Analytics Data Engineering Machine Learning Artificial Inteligence 195

Xebia

MARCH 2, 2025

Deployment isolation: Handling multiple users and environments During the development of a new data pipeline, it is common to make tests to check if all dependencies are working correctly. Let’s see through an example. Therefore, we can just run databricks bundle deploy command, to deploy on dev target.

Resources 130

Resources Testing Metrics Data Engineering 130

CIO

JUNE 7, 2022

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. Data engineer. Data engineers build and maintain the systems that make up an organization’s data infrastructure.

Artificial Inteligence 218

Artificial Inteligence Technical Review Fractional CTO Data Engineering 218

Xebia

MARCH 13, 2025

Mind, data lineage and discoverability become paramount when collaborating on features. Data lineage clarifies what data sources and transformations create a certain feature. You may, for example, want to know what values it can take. This blog post will not focus on data lineage nor discoverability.

Training 130

Training Machine Learning Artificial Inteligence Data 130