Smart Data Collective
SEPTEMBER 15, 2021
It is essential to process sensitive data only after acquiring a thorough knowledge of a stream processing architecture. The data architecture assimilates and processes sizable volumes of streaming data from different data sources. This very architecture ingests data right away while it is getting generated.
Astera
NOVEMBER 30, 2023
With its foundation rooted in scalable hub-and-spoke architecture, Data Vault 1.0 provided a framework for traceable, auditable, and flexible data management in complex business environments. Building upon the strengths of its predecessor, Data Vault 2.0 What’s New in Data Vault 2.0? The Data Vault 2.0
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Dataversity
DECEMBER 14, 2020
It was only a few years ago that BI and data experts excitedly claimed that petabytes of unstructured data could be brought under control with data pipelines and orderly, efficient data warehouses. But as big data continued to grow and the amount of stored information increased every […].
Astera
NOVEMBER 16, 2022
The 2022 Global Hybrid Cloud Trends Report by Cisco shows that 82% of organizations have adopted the hybrid cloud, which isn’t surprising given the growing popularity of hybrid data architectures among modern IT professionals. This way, you can modernize your data Infrastructure with minimal risk of data loss.
Tableau
JUNE 8, 2021
In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.
Astera
OCTOBER 19, 2023
Flexibility and Adaptability Flexibility is the tool’s ability to work with various data sources, formats, and platforms without compromising performance or quality. Altair Monarch Altair Monarch is a self-service tool that supports desktop and server-based data preparation capabilities.
Tableau
JUNE 8, 2021
In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.
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