Benefits of Data Virtualization to Data Scientists

Data Virtualization

The business value of applying data science in organizations is incontestable. Data science work can be divided into analytical and data preparation work. Examples of data preparation activities.

Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers. Overly simplistic venn diagram with data scientists and data engineers. Yes, both positions work on big data.

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Modernizing Data Architectures

Data Virtualization

Recently, we have seen the rise of new technologies like big data, the Internet of things (IoT), and data lakes. But we have not seen many developments in the way that data gets delivered. Modernizing the data infrastructure is the.

Don't put data science notebooks into production

Martin Fowler

We've come across many clients who are interested in taking the computational notebooks developed by their data scientists, and putting them directly into the codebase of production applications. My colleague David Johnston points out that while data science ideas do need to move out of notebooks and into production, trying to deploy that notebooks as a code artifact breaks a multitude of good software practices.

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Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? In which format? How will you validate and prepare the data? What about streaming data? Can data scientists discover and use the data? Can business people create reports via drag and drop? Can operations monitor what’s going on? Will the data lake scale when you have twice as much data? Is your data secure? In this session, we address common pitfalls of building data lakes and show how AWS can help you manage data and analytics more efficiently.

Snowflake: The Cloud Data Platform

CTOvision

Snowflake’s cloud data platform was designed to shatter the barriers that have prevented organizations of all sizes from unleashing the true value from their data. Big Data Companies Cloud Computing Companies Company

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Data Virtualization in the Cloud

Data Virtualization

The data landscape is constantly changing. Every day, we deal with tons of data in different formats from different applications, and it’s stored both on-premises and in the cloud.

Why You Need Continuous Data Protection Software

Storagecraft

Data protection is critical. Unfortunately, many businesses lean on ineffective methods to back up and recover data. Some rely on file-based cloud solutions that offer some level of protection, but really can’t guarantee you won’t lose data.

To Accelerate Digital Transformation, Accelerate Data Transformation

DevOps.com

Successful digital transformations are not possible without successful data transformation. Data and […]. The post To Accelerate Digital Transformation, Accelerate Data Transformation appeared first on DevOps.com.

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Self-serve data platform

Martin Fowler

One of the main concerns of distributing the ownership of data to the domains is the duplicated effort and skills required to operate the data pipelines technology stack and infrastructure in each domain. Luckily, building common infrastructure as a platform is a well understood and solved problem; though admittedly the tooling and techniques are not as mature in the data ecosystem.

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5 Things a Data Scientist Can Do to Stay Current

DataRobot together with Snowflake – a leading cloud data platform provider — is helping data scientists stay current with the latest technology and data science best practices so that they can excel in an increasingly AI-driven workplace. Five Things a Data Scientist Can Do to Stay Current offers data scientists guidance for thriving in AI-driven enterprises.

DevOps’ Data Storage Problem

DevOps.com

When the value of big data was finally embraced, thanks to new analysis capabilities developed in the late nineties and early aughts, the industry adapted its mindset toward storage by investing in on-premises data centers to help store the data that would drive better business decisions.

Data Types

DevOps.com

The post Data Types appeared first on DevOps.com. Blogs ROELBOB Build data types deployment humor parody programming satire

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Domain-driven data architecture

Martin Fowler

Zhamak explains the first part of the data mesh concept - using the ideas behind Domain-Driven Design to structure the data platform. more…. skip-home-page

Holistic Data Management

Data Virtualization

In this era of data-driven companies, there is a lot of talk about data management, but it is my impression that we do not talk about it in a perfectly harmonious way, that we privilege some aspects of the phrase.

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Data Science Fails: Building AI You Can Trust

The new DataRobot whitepaper, Data Science Fails: Building AI You Can Trust, outlines eight important lessons that organizations must understand to follow best data science practices and ensure that AI is being implemented successfully.

AppDynamics Expands APM Data Consumption

DevOps.com

AppDynamics, a unit of Cisco Systems, this week extended the observability capabilities of its namesake application performance management (APM) platform to ingest data from open source tools and agentless services alongside metric collected via its agent software.

Announcing the 2020 Data Impact Award Winners

Cloudera

During the first-ever virtual broadcast of our annual Data Impact Awards (DIA) ceremony, we had the great pleasure of announcing this year’s finalists and winners. We are delighted to officially publish this year’s Data Impact Award winners. Data Impact Achievement Award.

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Relevant Data

DevOps.com

The post Relevant Data appeared first on DevOps.com. Blogs ROELBOB

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Leveraging the Denodo Platform to Streamline Our Data Infrastructure

Data Virtualization

AXA XL is a subsidiary of the global insurance and reinsurance company Axa. We are headquartered in Stamford, Connecticut, and we have more than 100 offices across six continents. In this post, I’d like to share our experience with the.

How Banks Are Winning with AI and Automated Machine Learning

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

PowerProtect Data Manager – Modern Cloud Data Protection Innovation

Dell EMC

At the core of this transformation is data. Data enables organizations to tailor the digital experience to engage and meet the needs of their customers, partners and employees. It is critical to ensure that data is always protected, secure and available wherever it lives.

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Data Value at the Edge

Dell EMC

Fueled by an abundance of smart devices and IoT sensors, worldwide data creation has been growing exponentially, driving our customers and partners to innovate. Data Analytics Opinions Servers 5G Dell Technologies Edge Internet of Things Modular Infrastructure Rugged

Dun & Bradstreet: A leader in commercial data and analytics

CTOvision

We track Dun & Bradstreet in the CTOvision tech directory as a a Big Data company. Dun & Bradstreet is recognized as the global leader in commercial data and analytics, […]. Big Data Companies Carahsoft

Increase the Performance of Your Logical Data Fabric with Smart Query Acceleration

Data Virtualization

Gartner hybrid cloud logical data fabric multi-cloud OLAP databases Redshift smart query acceleration Smart Query Acceleration for Analytics TPC-DS virtual data layer

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The Rise of Embedded Self-Service Analytics

Speaker: Chris Von Simson & Nat Venkataraman

Can your users get the data and analytics they need without leaving your application? Watch this webinar with Chris von Simson of Dresner Advisory Services as he shares why user enablement has emerged as the theme in today’s embedded analytics landscape.

Connect the Data Lifecycle: The power of data

Cloudera

While cloud is the vehicle, it’s what sits on it that makes it so valuable — data. Regardless of where it is stored, whether it’s data-at-rest or data-in-motion, it’s how it’s linked together that enables business leaders to derive intelligence from data.

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Data Champions: Balancing IT and Business Needs

Cloudera

Underlying digital transformation and investment decisions is a precious asset: data. Now more than ever, decision-makers are looking to do more with their data. This is what the Data Champions category at the Data Impact Awards is all about.

Data 101

Maximizing data privacy: Making sensitive data secure by default

CTOvision

Read Ayal Yogev explain how companies can maximize data security by making sensitive data secure by default on Help Net Security : Consider the case of contact tracing, which has […].

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Fast Provisioning of data through Data Virtualization in the Era of ever-increasing Data Fluidity

Data Virtualization

We are in the midst of a significant transformation in each and every sphere of business. We are witnessing an Industrial 4.0 revolution across the industrial sectors. The way products are getting manufactured is being transformed with automation, robotics, and.

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Machine Learning for Builders: Tools, Trends, and Truths

Speaker: Rob De Feo, Startup Advocate at Amazon Web Services

Machine learning techniques are being applied to every industry, leveraging an increasing amount of data and ever faster compute. But that doesn’t mean machine learning techniques are a perfect fit for every situation (yet). So how can a startup harness machine learning for its own set of unique problems and solutions, and does it require a warehouse filled with PhDs to pull it off?

Employer data goes AWOL under Covid-19 lockdowns

The Parallax

Covid-19-era data breaches go beyond unemployment insurance fraud, medical-research hacks, and other hot topics. And unfortunately for public organizations and private companies, the data loss — from theft or otherwise — is getting worse. That’s according to several studies published this month, including the Digital Guardian Data Trends Report , published today, which paints an increasingly dire picture for organizations.

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Addressing the data storm with the Enterprise Data Cloud

Cloudera

For some, this may look like a new category at this year’s Data Impact Awards. However, the Enterprise Data Cloud category marks the evolution of what was once the Data Anywhere category. Digital-first companies, for example, saw an influx of new data.

A Smart Approach to Logical Data Warehousing, with Azure Synapse and the Denodo Platform

Data Virtualization

Organizations are leveraging cloud analytics to extract useful insights from big data, which draws from a variety of sources such as mobile phones, Internet of. Organizations all over the world are migrating their IT infrastructures and applications to the cloud.

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How To Build a Business Data Map

DevOps.com

The need to build a business data map. The post How To Build a Business Data Map appeared first on DevOps.com. Blogs DevOps Practice Enterprise DevOps compliance data data mapping

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How Banks Are Winning with AI and Automated Machine Learning

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.