Xebia

JANUARY 19, 2023

If you find what I am about to share interesting, get a copy of this magnificent book. Understand the cultural foundations of Agile, DevOps, and DataOps. The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons.

Data Engineering 130

Data Engineering Engineering Data Technical Review 130

Kentik

APRIL 12, 2023

Network telemetry and DataOps are two concepts that play a critical role in enhancing network visibility. By combining network telemetry and DataOps, organizations can improve their network visibility and gain actionable insights that can help them optimize their network performance, improve security, and enhance the overall user experience.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

CIO

DECEMBER 19, 2023

So, before overcommitting to too many priorities, CIOs should reset and review what constitutes a transformational initiative, take steps to avoid hype-inflated goals, and set reasonable priorities with the executive team for the year ahead. Luckily, many are expanding budgets to do so. “94%

Artificial Inteligence 351

Artificial Inteligence Generative AI Technical Review Software Review 351

CIO

OCTOBER 13, 2023

What to look for in a data architect Most data architects hold degrees in information technology, computer science, computer engineering, or related fields. The data architect is responsible for visualizing and designing an organization’s enterprise data management framework. In some ways, the data architect is an advanced data engineer.

Data 320

Data Database Administration Data Engineering Artificial Inteligence 320

DevOps.com

DECEMBER 8, 2020

Using DataOps and open source together, organizations can reimagine the way they deliver data and software-driven projects Real-time data is the fuel that powers today’s most critical systems and applications, as well as the digital transformation initiatives organizations need to compete effectively.

Open Source 122

Open Source Technology Organization Applications 122

DataRobot

AUGUST 16, 2022

What are companies actually doing today? The survey asked companies how they used two overlapping types of tools to deploy analytical models: Data operations (DataOps) tools, which focus on creating a manageable, maintainable, automated flow of quality-assured data. Survey Questions. Improving Success. Realistic Expectations.

Artificial Inteligence 52

Artificial Inteligence Survey Quality Assurance Open Source 52

CIO

SEPTEMBER 10, 2022

Mohan notes that her team simultaneously created DataOps frameworks that have improved the airline’s ability to ingest and consume new data sources in a matter of hours rather than weeks. According to Reuters , more than 100,000 flights in the US were canceled between January and July, up 11% from pre-pandemic levels. Taking to the cloud.

Airlines 344

Airlines Analytics Travel Weak Development Team 344

DevOps.com

JULY 23, 2020

The whole Data/DataOps space is becoming very hot. For StorPool, it is more of a case of the rest of the world catching up to what the company has been doing for a while now. It is the leading distributed block storage provider globally. In this DevOps Chat, we speak with Boyan Krosnov, co-founder and […].

Storage 101

Storage DevOps Software Data 101

DevOps.com

JUNE 19, 2019

Data Operations, or DataOps for short, is one of those IT buzzwords that lots of people use, yet few can define precisely. Like the cloud or DevOps, DataOps doesn’t make sense until you sit down and really think through what it entails. The post The Taxonomy of DataOps appeared first on DevOps.com.

DevOps 80

DevOps Cloud Data Analysis 80

RapidValue

JULY 21, 2019

From DevOps to DataOps DataOps can be simply stated as “DevOps for data”. DataOps is a methodology that helps organizations combine access to information for the employees that consume data and those that provide them. DataOps is able to handle modern complexity and change and offers key improvements over traditional approaches.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Hu's Place - HitachiVantara

APRIL 11, 2019

What has changed since then? Introducing DataOps Today more customer are focusing on the operational aspects of data rather than on the fundamentals of capturing, storing and protecting data. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data.

Data Engineering 81

Data Engineering Machine Learning Artificial Inteligence Technical Review 81

Altexsoft

JULY 23, 2020

In other words, they dedicate a quarter of their efforts to infrastructure — instead of doing what they can do best. What is worse, up to 75 percent of ML projects never go beyond the experimental phase. What is MLOps and how does it drive business success? MLOps and other Ops: what is the difference? This article.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

Hu's Place - HitachiVantara

MAY 10, 2019

DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective. This is where DataOps comes into play. Regulated data also needs to show lineage, a history of where the data came from and what has been done with it. This is colloquially called data wrangling.

Data 90

Data Machine Learning Artificial Inteligence Weak Development Team 90

Hu's Place - HitachiVantara

MAY 28, 2019

This report said that companies pursuing such projects generally lack an expert understanding of what data is needed for machine-learning models and struggle with preparing data in a way that’s beneficial to those systems. Its time to press your DataOps Advantag e. This is not a criticism of IBM’s AI tools.

Artificial Inteligence 67

Artificial Inteligence Data Artificial Intelligence Data Engineering 67

Dzone - DevOps

SEPTEMBER 14, 2020

This gap between what users need and what data teams can provide is a source of conflict and frustration for many businesses and can prevent an organization from taking full advantage of the strategic benefit of its data. Data teams today need to be able to deliver reliable and relevant insights on-demand.

Organization 73

Organization Data Analytics Strategy 73

Trigent

JUNE 20, 2022

To do this, they are constantly looking to partner with experts who can guide them on what to do with that data. What can a data engineer do for a business? Typically, a data engineer’s day-to-day work revolves around managing Data pipelines, managing Data Lakes, and DataOps activities. The complexity of NLP itself.

Data Engineering 52

Data Engineering Engineering Data Strategy 52

Cloudera

SEPTEMBER 15, 2022

I spoke to Mark Ramsey of Ramsey International to unpack what this all means and how it can help teams create an architecture that delivers business use cases faster. Luke: What is a modern data platform? What are the steps or elements that companies need to think about? What is a data fabric?

Data 78

Data Architecture Analytics Cloud 78

Hu's Place - HitachiVantara

JULY 8, 2019

At a high level DataOps may be described as the process to enable the right data to get to the right place at the right time. Time series data tells you what occurred when so that you can correlate it with other events that may have led to an outage. Where did the data originate and what has happened to the data in its life time?

Data 40

Data IoT Blockchain Analysis 40

Hu's Place - HitachiVantara

JUNE 14, 2019

It was accomplished through the extreme use of DataOps. This was a classical DataOps challenge. What is exciting is the possibilities of new applications that could be developed in the future, using these algorithms for reconstructing objects or concepts out of extremely spares data. So how were they able to get this image?

Virtualization 40

Virtualization Applications Data Innovation 40

Altexsoft

SEPTEMBER 9, 2022

And that’s what we’re going to talk about in this post. What is metadata? These are important for SEO purposes because search engines use them to “understand” what the page is about and how it has to be displayed and ranked in search results. What is metadata management? In the digital domain, it’s called metadata.

Tools 59

Tools Technical Review Software Review Systems Review 59

Datavail

AUGUST 9, 2022

To achieve that goal – a comprehensive use of corporate data to inform and guide all aspects of the enterprise – requires implementing a strategy as the project begins that lets everyone know what’s going to happen, why it’s going to happen, and what will be expected from every employee and leader.

Strategy 40

Strategy Data Disaster Recovery Hardware 40

O'Reilly Media - Data

JUNE 5, 2018

So what should an organization keep in mind before implementing a machine learning solution? It depends on what they’re predicting. Why model development does not equal software development. Artificial intelligence is still in its infancy. Models degrade in accuracy as soon as they are put in production.

Artificial Inteligence 160

Artificial Inteligence Machine Learning Software Review Weak Development Team 160

Datavail

SEPTEMBER 28, 2022

DataOps services. To learn more about data fabric, data mesh, and how we can assist you in planning and implementing these types of solutions, download our white paper, “What Can Data Fabric and Data Mesh Do for Your Data Management Approach?” The Current State of Data-Driven Decision Making. Finally, you must address data quality.

Data 40

Data Business Intelligence Government Analytics 40

TechCrunch

NOVEMBER 2, 2022

That mission has expanded quite a bit over the course of the last couple of years and today, Y42 announced the launch of what it calls its “Modern DataOps Cloud.” The Y42 Modern DataOps Cloud makes this vision a reality — today,” said Dang. Image Credits: Y42.

Data 203

Data Business Intelligence Open Source Government 203

Hu's Place - HitachiVantara

JUNE 11, 2019

So, what is happening? The global big data market is expected to grow at a CAGR of 22.4% during the forecast period and reach USD 200 billion by 2024 according to MarketWatch estimates. Data analytics is expected to be the key driver for this market. Changing CEOs will require several quarters for these companies to rebuild credibility.

Big Data 63

Big Data Analytics Cloud Data 63

O'Reilly Media - Data

SEPTEMBER 24, 2018

Getting DataOps right is crucial to your late-stage big data projects. The organizations don’t realize that data science stands on the shoulders of DataOps and data engineering giants. What we need to do is give these roles a sexy title. Let's call these operational teams that focus on big data: DataOps teams.

Enterprise 199

Enterprise Data Big Data Disaster Recovery 199

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. What is DataOps: brief introduction. How DataOps relates to Agile, DevOps, and MLOps. DataOps vs DevOps. DataOps vs MLOps.

Analytics 75

Analytics DevOps Development Software Review 75

CIO

JANUARY 23, 2024

What gets measured gets managed, or put another way, your results will be determined by what you are measuring,” says Martin Davis, CIO and managing partner at Dunelm Associates. He suggests, “Choose what you measure carefully to achieve the desired results.

KPI 323

KPI Metrics Culture Strategy 323

DataRobot

JUNE 27, 2022

In our previous two posts, we discussed extensively how modelers are able to both develop and validate machine learning models while following the guidelines outlined by the Federal Reserve Board (FRB) in SR 11-7. Assumptions used in designing a machine learning model may be quickly violated due to changes in the process being modeled.

Compliance 59

Compliance Metrics Training Systems Review 59

Altexsoft

OCTOBER 29, 2020

What is ETL Developer: Role Description, Process Breakdown, Responsibilities, and Skills. What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role. What is ETL testing and why do we need it? Businesses today rely heavily on data. Which is a good thing! The important word here is “when”.

Testing 63

Testing Tools Software Review Technical Review 63

CIO

MARCH 7, 2023

What are force-multiplying initiatives? So what should CIOs look to do today to drive digital transformation, identify force multipliers, and define initiatives that enable smarter, safer, and faster business outcomes? During the pandemic, speed remained a priority as CIO shifted to automate workflows and improve employee experiences.

Weak Development Team 360

Weak Development Team Technical Review Software Review SCRUM 360

OpenCredo

JULY 31, 2018

What is a cost centre? To many of you reading this blog post, the notion that IT is often viewed as a Cost Centre will come as no surprise. Yet, for many working deep within IT it is rare to question the value IT provides to the business and consider whether it is a “mere Cost Centre”. Before we continue let’s clarify. A Changing Landscape.

Artificial Inteligence 40

Artificial Inteligence Machine Learning Data Artificial Intelligence 40

O'Reilly Media - Data

APRIL 11, 2018

However, what each position does to create value or data pipelines with big data is very different. What are data scientists and data engineers? A common data scientist trait is that they’ve picked up programming out of necessity to accomplish what they couldn’t do otherwise. It makes the two positions seem interchangeable.

Data Engineering 280

Data Engineering Engineering Data Artificial Inteligence 280

James Shore

MAY 24, 2021

What sort of operations and security needs should you consider? Second Edition cover. This is a pre-release excerpt of The Art of Agile Development, Second Edition , to be published by O’Reilly in 2021. Visit the Second Edition home page for information about the open development process, additional excerpts, and more. Configuration.

Software Review 69

Software Review DevOps Metrics Systems Review 69

DataRobot

SEPTEMBER 13, 2022

Data scientists could be your key to unlocking the potential of the Information Revolution—but what do data scientists do? What Do Data Scientists Do? What data scientists do is directly tied to an organization’s AI maturity level. Driving Innovation with AI: Getting Ahead with DataOps and MLOps. Download Now.

Artificial Inteligence 52

Artificial Inteligence Data Artificial Intelligence Machine Learning 52

Cloudera

JULY 26, 2021

Authorization: Define what users of internal / external organizations can access and do with the data in a fine-grained manner that ensures compliance with e.g., data obfuscation requirements introduced by industry and country specific standards for certain types of data assets such as PII.

Strategy 114

Strategy Development Data Analytics 114

Altexsoft

AUGUST 22, 2022

What’s more, Gartner identifies data fabric implementation as one of the top strategic technology trends for 2022 and expects that by 2024, data fabric deployments will increase the efficiency of data use while halving human-driven data management tasks. What is data fabric? The landscape of enterprise data is fragmented.

Architecture 52

Architecture Artificial Inteligence Technical Review Data 52