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Confidential Containers with Red Hat OpenShift Container Platform and IBM® Secure Execution for Linux

IBM Big Data Hub

The protection of data-at-rest and data-in-motion has been a standard practice in the industry for decades; however, with advent of hybrid and decentralized management of infrastructure it has now become imperative to equally protect data-in-use.

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Do You Know Where All Your Data Is?

Cloudera

The stringent requirements imposed by regulatory compliance, coupled with the proprietary nature of most legacy systems, make it all but impossible to consolidate these resources onto a data platform hosted in the public cloud. The post Do You Know Where All Your Data Is? appeared first on Cloudera Blog.

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How to Deliver Data Quality with Data Governance: Ryan Doupe, CDO of American Fidelity, 9-Step Process

Alation

What’s the business impact of critical data elements being trustworthy… or not? In this step, you connect data integrity to business results in shared definitions. This work enables business stewards to prioritize data remediation efforts. Step 4: Data Sources. Step 9: Data Quality Remediation Plans.

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Big Data Ingestion: Parameters, Challenges, and Best Practices

datapine

However, due to the presence of 4 components, deriving actionable insights from Big data can be daunting. Here are the four parameters of Big data: Volume: Volume is the size of data, measured in GB, TB and Exabytes. Big data is increasing in terms of volume and heaps of data is generating at astronomical rates.

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How to Take Back 40-60% of Your IT Spend by Fixing Your Data

Ontotext

Achieving this advantage is dependent on their ability to capture, connect, integrate, and convert data into insight for business decisions and processes. This is the goal of a “data-driven” organization. We call this the “ Bad Data Tax ”.

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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Currently, no standardized process exists for overcoming data ingestion’s challenges, but the model’s accuracy depends on it. Increased variance: Variance measures consistency. Insufficient data can lead to varying answers over time, or misleading outliers, particularly impacting smaller data sets.

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The DataOps Vendor Landscape, 2021

DataKitchen

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. QuerySurge – Continuously detect data issues in your delivery pipelines. Azure DevOps.

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