AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Analytics Vidhya

OCTOBER 10, 2022

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 […].

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AWS Big Data

JANUARY 31, 2023

We have collected some of the key talks and solutions on data governance, data mesh, and modern data architecture published and presented in AWS re:Invent 2022, and a few data lake solutions built by customers and AWS Partners for easy reference.

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IBM Big Data Hub

JULY 5, 2022

In today’s world of complex data architectures and emerging technologies, databases can sometimes be undervalued and unrecognized. Back in the 1960s and 70s, vast amounts of data were stored in the world’s new mainframe computers—many of them IBM System/360 machines—and had become a problem. They were expensive.

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AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. Of those tables, some are larger (such as in terms of record volume) than others, and some are updated more frequently than others.

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Ontotext

NOVEMBER 16, 2023

Figure 1 Shows the overall idea of a data mesh with the major components: What Is a Data Mesh and How Does It Work? Think of data mesh as an operational mode for organizations with a domain-driven, decentralized data architecture.

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Metadata Data-driven Data Quality Data Architecture 52

IBM Big Data Hub

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. This storage architecture is inflexible and inefficient.

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