Data Lake vs Data Warehouse

The Crazy Programmer

Companies everywhere are handling more data than ever and all these terabytes of data need to be stored somewhere. Should you store the data in a database, a data warehouse, or a data lake? What is Data Lake? Querying a Data Lake. Data Lake.

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The data team: a short story

Erik Bernhardsson

The backdrop is: you have been brought in to grow a tiny data team (~4 people) at a mid-stage startup (~$10M annual revenue). As a minor note, I deliberate use the term “data scientist” to mean something very broad. It's your first day as head of the data team at SuperCorp!

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Data Minimization as Design Guideline for New Data Architectures

Data Virtualization

IT excels in copying data. It is well known organizations are storing data in volumes that continue to grow. However, most of this data is not new or original, much of it is copied data. For example, data about a.

Data Virtualization and Data Science

Data Virtualization

If we look at a typical , many of its stages have more to do with data than science. Before data scientists can begin their work regarding data science, they often must begin by: Finding the right data Gaining access.

Gartner Report - Introducing DataOps Into Your Data Management Discipline

Data teams are increasingly under pressure to deliver data to support a range of consumers and use cases. DataOps techniques can address the data delivery challenges through a more agile and collaborative approach to building and managing data pipelines.

When Data Virtualization Makes the Difference

Data Virtualization

In a previous post, I talked about some key data virtualization concepts and the types of professionals who can benefit the most from it. Data virtualization is not only beneficial in certain specific areas, but it can really make a.

Data Movement in Netflix Studio via Data Mesh

The Netflix TechBlog

This happens at an unprecedented scale and introduces many interesting challenges; one of the challenges is how to provide visibility of Studio data across multiple phases and systems to facilitate operational excellence and empower decision making.

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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.

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.

Elevate AI Development by Applying MLOps Principles

DXC

Creating new services that learn from data and can scale across the enterprise involves three domains: software development, machine learning (ML) and, of course, data. Analytics AI artificial intelligence Data Science machine-learning MLOps

How King Crushes New Product Development using Data-Driven Insights

Speaker: Ian Thompson, Head of Business Intelligence at King, and Zara Wells, Strategic Customer Success Manager at Looker

Product Managers looking to leverage data to make informed product design decisions can learn a lot from renowned gaming company King, maker of Candy Crush and many other games - even if their product has seemingly no overlap with games. Don't miss King’s data expert (dare we say king?)

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 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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How Our Data Science Bootcamp Upgraded Ye Zhang’s Career

Coding Dojo

Was interested in gaining data analysis experience to advance her career. The post How Our Data Science Bootcamp Upgraded Ye Zhang’s Career appeared first on Coding Dojo Blog. Pre-Dojo : Worked as a Quality Management Coordinator.

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The Data Driven Enterprise

Arista

The rise of cloud migration for enterprises with mission critical applications is redefining the data center. The reality for any enterprise: a systematic approach balancing workloads in the cloud and premises while securing data.

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.

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 Virtualization and the U.S. Federal Data Strategy

Data Virtualization

Federal Data Strategy, announced last year, is a call for agencies to modernize their data infrastructures. Federal Data Strategy appeared first on Data Virtualization blog. The U.S.

DevOps in a data science world

Xebia

Many organisations have a new ambition to become a data-driven organisation. In essence, this means the organisation wants to make better business decisions based on insights provided by data [4]. Data itself is not able to advise a business for better decision-making.

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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.

Embedded BI and Analytics: Best Practices to Monetize Your Data

Speaker: Azmat Tanauli, Senior Director of Product Strategy at Birst

By creating innovative analytics products and expanding into new markets, more and more companies are discovering new potential revenue streams. Join Azmat Tanauli, Senior Director of Product Strategy at Birst, as he walks you through how data that you're likely already collecting can be transformed into revenue!

Data Virtualization: The Key to a Successful Data Lakes

Data Virtualization

If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lakes projects will actually fail. The post Data Virtualization: The Key to a Successful Data Lakes appeared first on Data Virtualization blog.

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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Relevant Data

DevOps.com

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

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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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Products for Product People: Best Practices in Analytics

Speaker: Andrew Wynn, Senior Product Manager, Looker

As a product manager, you know how helpful custom tailored data solutions can be to doing your job well. But proper data analytics solutions take work to deliver - it's not as simple as just building a dashboard. Who builds products for the product people?

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.

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

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

Data Virtualization: The Key to a Successful Data Lake

Data Virtualization

If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lake projects will actually fail.

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.

Data-driven innovation: Machine Learning & Data Analysis

Apiumhub

Data-driven innovation is trending. Data-driven innovation forms a key pillar in this century. One of the key aspects to solve a data-driven problem is related to the operational challenge that will constitute the hypothesis for using data. Data Analysis Methods.

Data Virtualization: The Key to a Successful Data Lake

Data Virtualization

If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lake projects will actually fail. The post Data Virtualization: The Key to a Successful Data Lake appeared first on Data Virtualization blog.

When Data Virtualization Makes the Difference

Data Virtualization

In a previous post, I talked about some key data virtualization concepts and the types of professionals who can benefit the most from it. Data virtualization is not only beneficial in certain specific areas, but it can really make a.

Data Virtualization: A flexible, Agile, and Cost-Effective Solution

Data Virtualization

The volume of data, both structured and unstructured, continues to grow exponentially, and organizations continue to struggle to leverage all of the data to make the best business decisions.

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.

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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Zero Km Data

Data Virtualization

I see a strong analogy between what inspired the “Zero Km Food” movement, which started in Italy but then spread to other countries, and the way in which data can be managed in its lifecycle from creation, through detection, to.

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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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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.

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Iterate Your Way to a Top Analytics Product Experience

Speaker: Richard Cheng, Associate Product Manager, Mark43

Mark43 is on a mission to bring public safety data management into the 21st century. To fix traditionally paper-heavy and error-prone processes, they needed a secure and easy-to-use product experience that simplified and unified crime data collection and management.