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.

Data 148

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.

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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

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 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.

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.

Data-Informed Retrospectives

Scrum.org

TL; DR: Data-Informed Retrospectives. The second stage refers to gathering data so that the Scrum Team can have data-informed Retrospectives. Read on and learn how you can avoid falling victim to both scenarios by gathering data continuously and asynchronously. ??

Data 164

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.

DevOps 130

Polish Notation in Data Structure

The Crazy Programmer

In this article, we will look into Polish notation in Data Structures. Polish Notation in Data Structure. The post Polish Notation in Data Structure appeared first on The Crazy Programmer.

Data 148

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.

Data 52

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 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.

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.

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.

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.

Data 52

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.

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.

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

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.

Data 52

Data Mining: use cases & benefits

Apiumhub

Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights.

Data 75

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.

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

Header Linked List in Data Structure

The Crazy Programmer

Firstly, we have to create Header Node first whose data field will be NULL or 0 initially. For implementation, we will use a separate class for Header Node and normal Nodes which contain data respectively. If we want we can maintain any number of data items in the header node.

Data 148

A data-platform is just a normal platform

Xebia

A data-platform is nothing more than a normal (cloud) platform with some additional functionality on top to make it specific to the requirements of the data domain. The post A data-platform is just a normal platform appeared first on Xebia Blog.

Data 130

4 Approaches to Data Analytics

As the analytics landscape continues to evolve, application teams who need to embed dashboards, reports, and other analytics capabilities in their applications can choose from dozens of solutions. How do you differentiate one solution from the next?

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 218

No Single Data Repository Can Be Your Silver Bullet

Data Virtualization

If you are in the data management world, you probably help your company to redefine its data analytics architecture, especially in the context of cloud adoption.

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

How to Modernize Data Integration

DevOps.com

In times of uncertainty, accurate real-time data becomes essential to informed decision-making. Data engineering is […]. The post How to Modernize Data Integration appeared first on DevOps.com.

5 Reasons Why Choosing Apache Cassandra® Is Planning for a Multi-Cloud Future

Discover the reasons why choosing Apache Cassandra as a primary data store ensures that any future migrations to a different cloud provider or adoption of true multi-cloud are simple and easy.

Key Data modeling tools

Apiumhub

Every day quintillion bytes of data are created, and this pace is accelerating at a daily rate. With so much information at our disposal, it is becoming increasingly important for organizations and enterprises to access and analyze the relevant data to predict outcomes and improve services.

Tools 56

Data Types

DevOps.com

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

Data 105

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

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.

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.

Data 52

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

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.

Data 158

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

2021 State of Analytics: Why Users Demand Better

As organizations become more data driven, their analytics requirements grow. Find out how knowledge workers use analytics and explore their needs and preferences.