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Whitepapers

Whitepapers Datanami's white paper database contains reports from the leading thought-leaders and idea generators in the Datanami industry.

The New Data Scale Challenge: Results from the 2021 DataAware Pulse Survey

Source: Ascend.io
Release Date: Nov 4, 2021

In the second annual DataAware Pulse Survey, it was more clear than ever that data teams are facing a new and painful scaling challenge: scaling team productivity. The growing requirements businesses are placing on data teams are quickly outpacing team growth, and data engineers, analysts, scientists, and more are now struggling to keep pace. Read more…

7 Reference Architectures for Real-Time Analytics

Source: Rockset
Release Date: Oct 28, 2021

We experience real-time analytics everyday. The content displayed in the Instagram newsfeed, the personalized recommendations on Amazon, the promotional offers from Uber Eats are all examples of real-time analytics.

Real-time analytics encourages consumers to take desired actions from reading more content, to adding items to our cart to using takeout and delivery services for more of our meals. Read more…

Close the Information Gap: How to Succeed at Analytics in the Cloud

Source: Matillion
Release Date: Sep 23, 2021

What makes the difference between an average enterprise and a market leader? A strong data and analytics culture, producing vital information that informs decision-making and behavior throughout the business.

But a failure of analytics and processes around data can open up Information Gaps that divide teams and silo data, leaving enterprises in the dark and struggling to catch up as their data-savvy competitors seize new opportunities and widen their lead in the market. Read more…

Guide to Self-Service Data Preparation

Source: ALTAIR
Release Date: Sep 1, 2021

Whether you call it data preparation, mining, extracting, cleaning, joining, blending, or masking, it’s all data transformation. Professionals can spend hours every week working with their data, trying to join data from disparate sources, re-keying info from static reports or PDFs, or formatting data for accurate reporting. Read more…

The Forrester Wave™: Data Governance Solutions, Q3 2021

Source: Alation
Release Date: Aug 26, 2021

Does your data governance solution support the unique needs of your business?

Those hunting for a solution would be wise to reference the new Forrester Wave: Read more…

Cloud Data Security v1.0 Comparison: Immuta and Apache Ranger

Source: Immuta
Release Date: Aug 20, 2021

In a head-to-head comparison by GigaOm, Immuta’s attribute-based access control (ABAC) required 75X fewer policy changes than Ranger’s role-based access control (RBAC) to accomplish the same security objectives. The downstream impact of this is enormous, with the attribute-based approach increasing cloud ROI by more than $300,000. Read more…

Guide to the Lakehouse: Connecting data and teams to bridge the information gap

Source: Matillion
Release Date: Jul 30, 2021

To compete and seize opportunities, businesses need to leverage both structured and unstructured data – at scale. Data teams require a common streamlined method to access data, something difficult to achieve with a separate cloud data warehouse and data lake. Read more…

Modernize Your Data Warehouse

Source: Matillion
Release Date: Jul 1, 2021

Modern data warehouses, provisioned and procured in the cloud, can solve some of the most difficult challenges inherent in today’s data landscape: poor performance, high costs, increased variety, and increased distribution, all resulting in overall system fragility. Read more…

Immuta Data Engineering Survey: 2021 Impact Report

Source: Immuta
Release Date: Jun 25, 2021

We’re on the verge of a perfect storm for modern data access governance. As organizations lean into data and the cloud, new research shows that the majority will adopt multiple cloud compute technologies within the next two years. Read more…

The Value of Symbolic AI in Practical Natural Language Use Cases

Source: expert.ai
Release Date: Jun 24, 2021

Business executives have notoriously struggled to assess the business value of AI. They understand the potential value of it, but the general lack of institutional AI knowledge has made the evaluation process rather uncertain. Read more…

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