Xebia

MAY 16, 2023

Data Engineers were tempted by the pressure of the moment to give up on testing all together. There was no need for generating your own data; just take a percentage of production data. In many cases, these tasks ended up on the shoulders of the Data Engineers themselves. Data Engineer burnouts.

Testing 130

Testing How To Data Engineering Engineering 130

Cloudera

OCTOBER 7, 2022

dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD). Introduction. dbt-impala . dbt-spark-livy.

SDLC 81

SDLC Data Open Source Handbook 81

Cloudera

FEBRUARY 15, 2023

His day-to-day consists of development activities like writing and reviewing code, working on features around release timelines, and participating in design meetings for the team supporting the CDP Data Engineering product. Amogh has the unique experience of working on CDP Data Engineering during his internship.

Software Engineering 83

Software Engineering Software Review Engineering Software 83

O'Reilly Media - Data

JULY 10, 2018

Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them. The hard thing about being an ethical data scientist isn’t understanding ethics. It’s doing good data science. That’s what it means to do good data science.

Data 170

Data Weak Development Team Software Review Culture 170

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

Netflix Tech

MARCH 7, 2024

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

System 90

System Machine Learning Artificial Inteligence Training 90

O'Reilly Media - Ideas

JANUARY 8, 2019

From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data. Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training.

Trends 89

Trends Data Machine Learning Artificial Inteligence 89

O'Reilly Media - Ideas

FEBRUARY 11, 2020

Key survey results: The C-suite is engaged with data quality. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Data quality might get worse before it gets better. An additional 7% are data engineers.

Weak Development Team 129

Weak Development Team Data Technical Review Survey 129

Altexsoft

MARCH 14, 2023

Additionally, this modularity can help prevent vendor lock-in, giving organizations more flexibility and control over their data stack. Offered as open-source with active support by communities. Many components of a modern data stack (such as Apache Airflow, Kafka, Spark, and others) are open-source and free.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

O'Reilly Media - Ideas

NOVEMBER 13, 2018

You need to have the culture, processes, and infrastructure in place before you can deploy many models into products and services. At the recent Strata Data conference we had a series of talks on relevant cultural, organizational, and engineering topics. Culture and organization.

Machine Learning 92

Machine Learning Artificial Inteligence Software Review Conference 92

Cloudera

APRIL 26, 2023

It offers features such as data ingestion, storage, ETL, BI and analytics, observability, and AI model development and deployment. The platform offers advanced capabilities for data warehousing (DW), data engineering (DE), and machine learning (ML), with built-in data protection, security, and governance.

Cloud 64

Cloud Data Load Balancer Storage 64

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

Netflix Tech

NOVEMBER 22, 2019

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. Technology advancements in content creation and consumption have also increased its data footprint.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Netflix Tech

NOVEMBER 22, 2019

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. Technology advancements in content creation and consumption have also increased its data footprint.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Netflix Tech

MARCH 5, 2019

Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. In the Efficiency space, our data teams focus on transparency and optimization.

Infrastructure 73

Infrastructure Scalability Cloud Data 73

ParkMyCloud

DECEMBER 11, 2020

If you know where to look, open-source learning is a great way to get familiar with different cloud service providers. . Coursera includes a number of free courses including topics in Machine Learning, Architecting, Data Engineering, Developing Applications, and the list goes on. .

Google Cloud 97

Google Cloud Training Resources Cloud 97

UruIT

APRIL 27, 2020

MathWork focused on the development of these tools to become experts in high-end financial use and data engineering contexts. Also, its solid presence in data science and machine learning software marketplace has built a strong user base. . H20.ai Following its vision of democratizing intelligence for all, H20.ai

Artificial Inteligence 78

Artificial Inteligence Machine Learning Azure Software Review 78

Apiumhub

APRIL 8, 2021

Lorica has applied his BI, machine learning, and statistical analysis expertise in a variety of domains, including financial engineering, marketing and market research, web analytics, and text mining. Lorica hosts The O’Reilly Data Show podcast; he also blogs on data, technology, and culture at The Gradient Flow.

Artificial Inteligence 109

Artificial Inteligence Technical Advisors Data Machine Learning 109

UruIT

JULY 23, 2019

MathWork focused on the development of these tools in order to become experts on high-end financial use and data engineering contexts. Also, its solid presence in data science and machine learning software marketplace has allowed it to build a strong user base and customer relations. Algorithmia.

Artificial Inteligence 81

Artificial Inteligence Machine Learning Azure Software Review 81

O'Reilly Media - Ideas

DECEMBER 6, 2018

Progress in research has been made possible by the steady improvement in: (1) data sets, (2) hardware and software tools, and (3) a culture of sharing and openness through conferences and websites like arXiv. Novices and non-experts have also benefited from easy-to-use, open source libraries for machine learning.

Technology 79

Technology Artificial Inteligence Machine Learning Hardware 79

O'Reilly Media - Ideas

FEBRUARY 11, 2019

In other surveys we ran, we found “lack of skilled people,” “lack of data,” and cultural and organizational challenges as the leading obstacles cited for holding back the adoption of machine learning and AI. When it come to ethics, it’s fair to say the data community (and the broader technology community) is very engaged.

Big Data 87

Big Data Technology Tools Cloud 87

Altexsoft

OCTOBER 2, 2020

Data integration and interoperability: consolidating data into a single view. Specialist responsible for the area: data architect, data engineer, ETL developer. data engineering, data science, data application development, data exchange. Snowflake data management processes.

Database Administration 64

Database Administration Strategy Data Technical Review 64

O'Reilly Media - Ideas

MARCH 18, 2020

One-sixth of respondents identify as data scientists, but executives—i.e., The survey does have a data-laden tilt, however: almost 30% of respondents identify as data scientists, data engineers, AIOps engineers, or as people who manage them. With an abundance of open source tools, libraries, tutorials, etc.,

Enterprise 134

Enterprise Survey Technical Review Weak Development Team 134

O'Reilly Media - Ideas

APRIL 19, 2021

In the past, company culture has been the most significant barrier to AI adoption. While it’s still an issue, culture has dropped to fourth place. Last year, the major bottleneck holding back adoption was company culture (22%), followed by the difficulty of identifying appropriate use cases (20%).

Enterprise 144

Enterprise Survey Weak Development Team Education 144

Cloudera

DECEMBER 18, 2017

“Becoming data-driven is a multi-year journey, not a simple implementation.” Acquiring and using data in a way that simply wasn’t possible up until very recently, requires a huge cultural shift. Now let’s apply this multi-decade approach to data – currently well recognized as one of the world’s most critical resources.

Data 40

Data Business Transformation Machine Learning Artificial Inteligence 40

O'Reilly Media - Ideas

MARCH 31, 2020

Particularly at a company that’s new to AI, part of an AI product manager’s job is helping the organization build the culture it needs to succeed with AI. These are fundamentally cultural problems. It turns out that type of data infrastructure is also the foundation needed for building AI products.

Product Management 139

Product Management Artificial Inteligence Machine Learning Software Review 139

Abhishek Tiwari

JULY 20, 2019

Both models work on the scale, particularly when the company's core values, priorities, and culture all three are well aligned. It is one of the ways you can organise your engineering teams in a retail environment. The right answer mostly depends on the triad I mentioned earlier - core values, business priorities, and culture.

Engineering 61

Engineering Weak Development Team Software Review Technical Review 61

Netflix Tech

NOVEMBER 22, 2019

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. Technology advancements in content creation and consumption have also increased its data footprint.

AWS 15

AWS Open Source Linux Engineering Management 15

O'Reilly Media - Ideas

JANUARY 25, 2021

There’s been a lot of discussion about operations culture (the movement frequently known as DevOps), continuous integration and deployment (CI/CD), and site reliability engineering (SRE). Cloud computing has replaced data centers, colocation facilities, and in-house machine rooms. Operations or DevOps or SRE.

Programming 145

Programming Cloud Artificial Inteligence Machine Learning 145

AWS Machine Learning - AI

JANUARY 26, 2024

Security organizations can support a culture of responsible disclosure and iterative improvement by participating in discussions and bringing new ideas and information to generative AI stakeholders that relate to their business objectives.

Artificial Inteligence 118

Artificial Inteligence Generative AI Security Applications 118

Daniel Bryant

SEPTEMBER 13, 2019

What was worth noting was that (anecdotally) even engineers from large organisations were not looking for full workload portability (i.e. There were also two patterns of adoption of HashiCorp tooling I observed from engineers that I chatted to: Infrastructure-driven?—?in Bravo @HashiCorp ??

Infrastructure 8

Infrastructure Azure Software Engineering Cloud 8

Coforge

AUGUST 20, 2019

With the above in context, here, I believe, are the three focus areas for building a successful and productive AI/ML foundation in any organisation: #1 Executive Sponsorship and Management Strategy: Adopting AI/ML in the enterprise would essentially mean weaving it into the fabric of the corporate culture.

Machine Learning 8

Machine Learning Artificial Inteligence Enterprise Software Review 8