Data Management Challenges for the Modern Enterprise

Data Virtualization

Data is the fuel of the digital economy, so data-centric organizations have a distinct advantage. To remain competitive, organizations must have a data management strategy in place to effectively ingest, store, organize, and analyze data while ensuring that it is.

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Data Mesh Principles and Logical Architecture

Martin Fowler

Last year, my colleague Zhamak Dehghani introduced the notion of the Data Mesh , shifting from the notion of a centralized data lake to a distributed vision of data.

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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.

Understanding Data Storage: Lakes vs. Warehouses

DevOps.com

Now more than ever, companies are looking for new ways to incorporate data analytics into their daily operations and leverage data-driven insights to improve business functions. The post Understanding Data Storage: Lakes vs. Warehouses appeared first on DevOps.com.

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.

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.

The Importance of Data in Software Development

Agile Alliance

The post The Importance of Data in Software Development first appeared on Agile Alliance. Process agile development data software development testing

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.

Data 219

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.

Data 220

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

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.

Data Types

DevOps.com

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

Data 77

Successful Data Virtualisation: more than the right choice of platform

Data Virtualization

Learn in 12 minutes: What makes a strong use case for data virtualisation How to come up with a solid Proof of Concept How to prepare your organisation for data virtualisation You’ll have read all about data virtualisation and you’ve.

Data 56

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.

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

Cloud 103

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.

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.

Types of Data Structures

The Crazy Programmer

Data structures are a very important programming concept. They provide us with a means to store, organize and retrieve data in an efficient manner. The data structures are used to make working with our data, easier. There are many data structures which help us with this. Types of Data Structures. Primitive Data Structures. These are the structures which are supported at the machine level, they can be used to make non-primitive data structures.

Data 212

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.

Data 52

Inside The Mind Of A Data Scientist

Hacker Earth Developers Blog

Approach: Said port hires a data scientist to look at the numerous variables affecting ship movement and operational efficiency. Conclusion: Our data hero announces that the port will have to hire at a rate of 3% every year to keep up with increasing volume.

Data 130

Business Monitoring Systems: Using ML to Analyze Metrics

This whitepaper discusses how automated business monitoring solutions like Yellowfin Signals revolutionize the way users discover critical and relevant insights from their data.

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.

What Role Do Data Engineers Play in Data Security?

Dataiku

While we know that data engineers are very different than data architects — as the latter conceptualize data frameworks and the former build and maintain them — the data engineer function has evolved quite a bit in recent years. Data Basics Featured

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.

Data 95

Relevant Data

DevOps.com

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

Data 86

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.

Optimizing data warehouse storage

The Netflix TechBlog

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Some of the optimizations are prerequisites for a high-performance data warehouse.

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.

Data 89

DevSecOps Implementation: It’s About the Data

DevOps.com

Databases exist to pool related data together. Even NoSQL type databases, which some saw as an opportunity for wildly disparate data […]. The post DevSecOps Implementation: It’s About the Data appeared first on DevOps.com.

Data 83

Protecting Data from Insider Threats

DevOps.com

Certainly, as organizations become data-driven, embrace data democratization and release new applications at breakneck speed, the probability of an insider threat is rising sharply. The post Protecting Data from Insider Threats appeared first on DevOps.com.

Data 87

What is Contextual Analytics? The Next Evolution of Embedded Analytics

Download this white paper to learn what contextual analytics is, how BI platforms like Yellowfin revolutionize the way users discover insights from their data with native contextual analytics, and how it adds value to your software solution by elevating the user experience.

Streamlining External Data Access to Enrich Analytics

Data Virtualization

Including external data in many forms of analytics and reporting can provide significant enrichment. New business insights can be discovered that would be impossible to unearth by working exclusively with internal data. The good news is that external data is.

5 things on our data and AI radar for 2021

O'Reilly Media - Ideas

The data that powers ML applications is as important as code, making version control difficult; outputs are probabilistic rather than deterministic, making testing difficult; training a model is processor intensive and time consuming, making rapid build/deploy cycles difficult.

Data 93

Delivering the enterprise data cloud

O'Reilly Media - Data

This is a keynote highlight from the Strata Data Conference in New York 2019. AI & ML Data Strata NY 19Watch the full version of this keynote on the O’Reilly online learning platform. You can also see other highlights from the event.

3 Steps to Faster Insights in Data Analytics

Data Virtualization

In recent years, we have seen wide adoption of data analytics. Some issues that have been most often cited for this include: Poor data quality: While preparing. However, most organizations continue to find it challenging to quickly yield actionable insights.

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?

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.

Data 87

Immuta Automates Data Access Governance for Cloud Data Ecosystems

CTOvision

Immuta, the automated data governance company, today announced the availability of new features for its Automated Data Governance platform including new, native integrations with Starburst Presto and Presto.

Faster data migrations in Postgres

The Citus Data

In my day to day, I get to work with many customers migrating their data to Postgres. A large chunk of the migrations that I help people with are homogenous Postgres-to-Postgres data migrations to the cloud.

Data 90

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

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.