DataOps and Cloud Data Ecosystems: Overcoming the 3 Biggest Hurdles

DataOps and Cloud Data Ecosystems: Overcoming the 3 Biggest Hurdles
The cloud is changing the way we use data and at the same time, the way we use data is changing the cloud. Organizations are adopting multiple cloud data platforms to maximize data’s value, but in doing so are often increasing complexity for data engineering and DataOps teams that may stifle the very reason for investing in those platforms.
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Addressing Big Data Challenge Hadoop way

Xoriant Corporation

Are you unsure of how to leverage Big Data and select among the many technologies surrounding it? Don't worry - you aren't alone. Here and watch this video to know how you can address the Big Data challenges using Hadoop.
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Modern Data Analytics in the Cloud: Achieving an End-to-End Strategy

TDWI

Businesses today need fast, scalable, and agile data and analytics, and cloud-based solutions are proving critical to satisfying these requirements. They enable organizations to rapidly and easily spin up systems and services for collecting, managing, and analyzing data. More important, cloud-based solutions deliver value from “data gravity”the surging volumes of new data created in the cloud by social media, the IoT, multichannel customer behavior, and other activity.
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Eliminating Data Silos: Modern Data Architectures for Analytics

Modern data applications and analytics rely on a wide variety of data both inside and outside the company; organizations depend on enriched data sets for better insights. This need, in part, has driven many companies to move to cloud data warehouses and cloud data lakes. However, it’s no longer simply about migrating to the cloud. It’s about modernizing using a combination of industry-leading services in the cloud and cloud-native data management services to deliver better business decisions, faster.
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Overcoming Data Management Challenges in AI/ML

The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.
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