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How is Web 3.0 Shaping the Future of Finance?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. We don’t have a native value settlement layer, nor do we have control over our data. Our data architectures are still founded on the idea of stand-alone computers, where data is centrally stored and maintained on a […].

Finance 291
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Building a Lakehouse – Try Delta Lake!

Analytics Vidhya

Introduction Enterprises have been building data platforms for the last few decades, and data architectures have been evolving. Let’s first look at how things have changed and how […].

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How Do You Design New Data Architectures?

Data Virtualization

Organizations are rethinking their current data architectures. This article. Unfortunately, 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.

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Breaking State and Local Data Silos with Modern Data Architectures

Cloudera

But while state and local governments seek to improve policies, decision making, and the services constituents rely upon, data silos create accessibility and sharing challenges that hinder public sector agencies from transforming their data into a strategic asset and leveraging it for the common good. . Modern data architectures.

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Warehouse, Lake or a Lakehouse – What’s Right for you?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Most of you would know the different approaches for building a data and analytics platform. You would have already worked on systems that used traditional warehouses or Hadoop-based data lakes. Selecting one among […].

Data Lake 350
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Active Data Architecture: The Need of the Hour

Data Virtualization

Reading Time: 3 minutes As organizations continue to pursue increasingly time-sensitive use-cases including customer 360° views, supply-chain logistics, and healthcare monitoring, they need their supporting data infrastructures to be increasingly flexible, adaptable, and scalable.

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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. Data teams are confused as to whether they should get on the bandwagon of just one of these trends or pick a combination. First, we describe how data mesh and data fabric could be related.