Five Modern Data Architecture Trends

David Menninger's Analyst Perspectives

I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.

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.


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Creating an AI-Ready Data Architecture in the Cloud


But the data architectures that feed into them are just as vital. Sophisticated ML-as-a-service platforms such as Facebook’s FBLearner Flow are ideal for delivering AI-at-scale. Automation is what AI algorithms do best. So, it should be no surprise that the world’s most advanced AI-using enterprises are using the technology to automate the process of experimenting with and scaling AI itself. Artificial Intelligence & Machine Learning

How Do You Design New Data Architectures?

Data Virtualization

Organizations are rethinking their current data architectures. Business Data Architecture Data storage data stores data virtualization ETL Modern Data Architecture Snowflake SQL technologyUnfortunately, the majority considers it a challenge. Obviously, one of the reasons is that they don’t do this every day. Also, insights about how to design them has changed over time. This article.

Critical Components of Big Data Architecture for a Translation Company

Smart Data Collective

Big data technology has been instrumental in helping organizations translate between different languages. We covered the benefits of using machine learning and other big data tools in translations in the past. How Does Big Data Architecture Fit with a Translation Company?

Data Architecture Movements in 2020


Data is commonly referred to as the new oil, a resource so immensely powerful that its true potential is yet to be discovered. We haven’t achieved enough with data research and other statistical modeling techniques to be able to see data for what it truly is and even our methods of accruing data are rudimentary […].

The Difference Between Data Architecture and Enterprise Architecture


Although there is some crossover, there are stark differences between data architecture and enterprise architecture (EA). That’s because data architecture is actually an offshoot of enterprise architecture. In simple terms, EA provides a holistic, enterprise wide overview of an organization’s assets and processes, whereas data architecture gets into the nitty gritty. The Value of Data Architecture.

New Data Architectures are too Data-Store-Centric

Data Virtualization

Too often the design of new data architectures is based on old principles: they are still very data-store-centric. They consist of many physical data stores in which data is stored repeatedly and redundantly.

Data Professional Introspective: Data Architecture and the Role of Business


The phrase “data architecture” often has different connotations across an organization depending on where their job role is. For instance, most of my earlier career roles were within IT, though throughout the last decade or so, has been primarily working with business line staff. When I present at conferences, seminars, or DAMA chapters, I ask […].

Data Architecture: 2.5 Types of Modern Data Integration Tools

Perficient Data & Analytics

As we move into the modern cloud data architecture era, enterprises are deploying 2 primary classes of data integration tools to handle the traditional ETL and ELT use cases. The first type of Data integration tools are GUI-Based Data Integration solutions. Also, GUI-based data integration tools have a proven record of increasing developer productivity. They are good for organizations that have: Many data integration pipelines to manage.

Top 7 characteristics of a modern data architecture

IBM Big Data Hub

A modern data architecture (MDA) must support the next generation cognitive enterprise which is characterized by the ability to fully exploit data using exponential technologies like pervasive artificial intelligence (AI) , automation, Internet of Things (IoT) and blockchain

Data Architecture Crash Course: Key Terms


We’ve set out to demystify the jargon surrounding data architecture to enable every team to understand how it impacts their objectives. Hadoop Enterprise Ai Data Transformation data architectureNot sure what Hadoop actually is? A little fuzzy on what the difference is between cloud and on-prem storage?

Responsibility of Data Architecture in Data Governance

Perficient Data & Analytics

The data architecture capability will supply the components and standards necessary to implement other capabilities coherently and enable them to work together. A primary responsibility of data architecture is to define and have an accepted enterprise-wide set of models, standards, glossaries and hierarchies which allow a standard description of data across business lines, products and functional areas.

How to Develop the Essential Data Architecture for Your Digital Transformation Strategy


On the other hand, any business that does… The post How to Develop the Essential Data Architecture for Your Digital Transformation Strategy appeared first on Treehouse Tech Group. Data Strategy Digital Transformation

Trends in Enterprise Data Architecture and Model Deployment


The goal of this survey was to uncover trends in data architecture in the enterprise, specifically in the context of operationalizing machine learning models. In late 2019, Dataiku enlisted Gerson Lehrman Group (GLG) to administer an anonymous survey to 200 IT professionals across a range of industries. What did we find? Scaling AI Featured

Why Your Startup Needs Data Science


Top-quality data currently represents one of the most important resources for any company. Startups that lack familiarity with important tendencies and trends in their industry need to have this crucial data […].

The Lifecycle of Data

The Data Administration Newsletter

Most data is not static. No, data has a life in which it changes, is used for perhaps multiple purposes, and gets moved all over the place. So, it makes sense to think about the lifecycle of your data at your organization.

Three Key Takeaways from CDAO Europe 2019


‘Ethics’, ‘strategy’ and ‘collaboration’ were the words on everyone’s lips when 115 data and analytics leaders descended on Berlin for CDAO Europe 2019 last week. Data Strategy Data Ethics Data Architecture

Are Your Data Skills Up to Date?

The Data Administration Newsletter

But as technology becomes more complex, the data that it mines and uses becomes more plentiful. These technologies collect, use, and report tons of data that help improve our daily […]. The information age has been upon us for most of the new millennium.

Partly Cloudy with a Scattered IT Environment

The Data Administration Newsletter

How many times, when you were a kid, did a thunderstorm cause an immediate mix of emotions: the fear of the continuous boom of the thunder as the clouds rolled in, the calming sensation of the smell of freshwater in the breeze and wind, and the awe and wonder as the lighting streaked through the […].

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Knowledge Graphs vs. Property Graphs – Part 1


Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. We are in the era of graphs. Graphs are hot.

Storing Data in the Cloud

The Data Administration Newsletter

In the cloud-era, should you store your corporate data in Cosmos DB on Azure, Cloud Spanner on the Google Cloud Platform, or in the Amazon Quantum Ledger?

Build an enterprise-ready, modern data architecture based on open source

IBM Big Data Hub

This isn’t surprising given the influx of multiple types of data from disparate sources and the popularity of open source methods to capture and turn it into insight Gartner analysts Merv Adrian and Donald Feinberg in a February 2018 report predict that “by 2022, more than 70 percent of new applications developed by corporate users will run on an open source database management system.”

Knowledge Graphs vs. Property Graphs – Part II


Differences in Terminology and Capability Building on the terms and concepts introduced in Part I of this white paper, Part II digs deeper into the difference in the meaning of some key terms used in both Property Graphs and Knowledge Graphs, including LABELS, TYPES, and PROPERTIES.

The Data-Centric Revolution: Data-Centric vs. Centralization


They had seemingly achieved many of the benefits of becoming data-centric through decentralization: all […].

Automating Your Corporate Data Estate


In the world of data, automation plays a well-honed role in rapidly developing modern data estates. Before we proceed any further, let’s establish an understanding about the purpose of a corporate data estate. Automation has revolutionized industries and business for years.

Knowledge Graphs vs Property Graphs – Part III


Part 3 completes this article series by discussing some important topics beyond the critical differentiators in the terminology and capabilities of Property Graphs and Knowledge Graphs covered in Parts 1 and 2.

Everything is Big Data


The term “big data” has been bandied about for a number of years now, and it has gotten to the point where it has been used so much that it is a part of IT culture.

Data Management 20/20: Anatomy of a Business Glossary Definition


The business glossary is simple in concept, but it can be a challenge to structure, define and maintain shared business terminology. Consistent business meaning is important because distinctions between business terms are not typically well defined or documented.

Open Data & Democracy

The Data Administration Newsletter

The open data initiatives that mushroomed in the late 2000s were expected to foster democratic rule in new and more established democracies. […].

Data Distancing – What and Why?


Desperate situations call for desperate measures. The COVID-19 pandemic has changed our lifestyle. If you switch on the television, chances are 9 out of 10 channels are talking about COVID-19 and how the numbers are going up across the world.

Digital Twin at a Glance – Revolutionizing the Industrial Domain


Prominent entities across a myriad of sectors are preparing for the digital revolution by integrating a host of technologies such as IoT, AI, Big Data, digital twins, and robotics, in their processes, products, and workflows.

IoT 52

Introduction to Blockchain for DBAs


Big Data Articles Big Data News, Articles, & Education Data Architecture Articles Data Architecture News, Articles, & Education Data Articles Data Education Data Operations Articles Data Operations News, Articles, & Education Data Strategy Articles Data Strategy News, Articles, & Education Data Topics blockchain database administration databases DBA DBMS mullins

What DBAs Should Do Before Taking a Vacation


Data Architecture Features Data Architecture News, Articles, & Education Data Education Data Features Data Management Features Data Management News, Articles, & Education Data Modeling Features Data Modeling News, Articles, & Education Data Operations Features Data Topics accountability DBA kline performance

The Data-Centric Revolution: Report From the Front Lines


Earlier this month we hosted the second annual Data-Centric Architecture Forum (#DCAF2020) in Fort Collins, CO. Last year, (2019) we hosted the first Data-Centric Architecture conference. In 2019, the focus was on getting a sketch of a reference architecture (click here to see). These are the highlights. Trip report). We convened shortly after the event […].

How to Develop a Sustainable Digital Platform


Business Intelligence Articles Business Intelligence News, Articles, & Education Data Architecture Articles Data Architecture News, Articles, & Education Data Articles Data Education Data Operations Articles Data Operations News, Articles, & Education Data Strategy Articles Data Strategy News, Articles, & Education Data Topics common information model digital platform sustainability swende vedaa

Make An Impact: The Data Catalog is a 1980’s Solution for 2020’s Problems


Remember the Sears catalog? Hundreds of pages of clothes quickly flipped past to get to the toys! Who didn’t spend countless hours trying to find the perfect suggested gift for your next birthday or holiday? In an Amazon and eBay world, thinking about old-school catalogs seems so quaint.

Conceptual Modeling Requires Conceptual and Critical Thinking


Data Architecture Articles Data Architecture News, Articles, & Education Data Articles Data Education Data Modeling Articles Data Modeling News, Articles, & Education Data Operations Articles Data Operations News, Articles, & Education Data Strategy Articles Data Strategy News, Articles, & Education Data Topics conceptual model critical thinking data model greefhorst

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.

The Mainframe is a Modern Platform


Mainframes also can easily handle large amounts of data—multiple terabytes—such as is needed for analytical processing and machine learning. Indeed, the mainframe supports both traditional and modern applications and data management requirements, making it a key component of […]. Mainframes continue to excel at traditional tasks such as high-speed transaction processing and running large batch workloads.

Differences Between ‘Available’ and ‘Accessible’


Big Data Blogs Big Data News, Articles, & Education Business Intelligence Blogs Data Architecture Blogs Data Architecture News, Articles, & Education Data Education Data Operations Blogs Data Operations News, Articles, & Education Data Strategy Blogs Data Strategy News, Articles, & Education Data Topics accessibility availability data science dataops lysecki

Broken Data – What You Don’t Know Will Hurt You – Part 1


The first step to fixing any problem is to understand that problem—this is a significant point of failure when it comes to data. Most organizations agree that they have data issues, categorized as data quality. Organizations typically define the scope of their data problems by their current (known) data quality issues (symptoms). However, this definition is […].

The Unfolding of the Data-Centric Paradigm – Part 1


There is a movement to upend traditional thinking about information systems by putting data and meaning at the center of strategy, architecture, and system development sequencing. It is the most recent of a series of data-centric waves which, over several decades, have attempted to remedy the entrenched application-centric paradigm.