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2022 Trends in Cloud Computing: Poly Cloud Specialization

In this contributed article, editorial consultant Jelani Harper discusses how the undue reliance on the cloud for managing data initially manifested as the hybrid and multi-cloud phenomenon. That movement is continuing apace to reflect a growing number of cloud types.

Veritone Multi Cloud AI Platform Provider Approved for Use Across the Entire US Department of Justice

Veritone, Inc. (NASDAQ: VERI), creator of aiWARE, a hyper-expansive Enterprise AI platform, announced the expanded availability of aiWARE Government for all components of the U.S. Department of Justice (DOJ). Originally sponsored by the Executive Office for United States Attorneys (EOUSA) within the DOJ, Veritone has obtained an expanded, enterprise-wide Authority to Operate (ATO) from the FedRAMP authorization board for its aiWARE™ Government platform.

Panzura Elevates Path to Hybrid-Cloud Adoption with New Panzura Guarantee Across Every Stage of the Customer Journey

Panzura has announced the Panzura Guarantee, a new complementary program to ensure peace-of-mind and ease, across every stage of the customer journey starting with buying and rollout, and throughout the lifetime of a Panzura implementation. It is designed to accelerate hybrid-cloud adoption so organizations can quickly and securely accrue the benefits of cloud-based data management using Panzura’s award-winning technology.

eBook: A Practical Guide to Using Third-Party Data in the Cloud

[Sponsored Post] To help you navigate a proliferating data landscape, AWS Data Exchange would like to present you with a copy of the new eBook, “A Practical Guide to Using Third-Party Data in the Cloud.” Learn how innovative teams are shifting their focus from data-driven business intelligence to accelerating insight-driven decision-making and now are turning to third-party datasets as a differentiator.

Webinar: Using Third-party Healthcare Data to Accelerate Drug Discovery and Clinical Trial Protocol Design

[SPONSORED CONTENT] In this virtual session, AWS Data Exchange will host a panel discussion with thought-leaders from healthcare powerhouse companies like QIAGEN and IBM Watson Health. Learn about the critical role third-party datasets have played in the evolution of the healthcare and life sciences industry. FRIDAY, AUGUST 27 at 11AM PT | 2PM ET

The Data Engineering Cloud: Three Lessons for a New Era

In this contributed article, Joe Hellerstein, Co-Founder & CSO and Jeffrey Heer, Co-Founder & CXO, Trifacta, discuss how companies need to think about data engineering and how to democratize it. The more users are able to build and refine data products, the less chance that there will be a breakdown in communication between the people with questions and the people who analyze the data to get answers.

Surpassing Decentralized Data Management Woes with Data Virtualization

In this contributed article, editorial consultant Jelani Harper discusses how data virtualization enables organizations to surmount obstacles (i.e. data quality, schema, and data integrations that are foundational to data management) and to focus on benefits (i.e. remote collaborations characteristic of working from home, the takeoff of the cloud as the de facto means of deploying applications, and the shift to external sources of unstructured and semi-structured data). Supplementing it with mutable graph data models boosts its applicability to data of all types.

How to Overcome Obstacles in Data Lake and Warehouse Strategies: 3 Best Practices for Enterprise Architects

In this special guest feature, Kimberly Read, the enterprise architect at Faction, suggests that to support the business case for multi-cloud, enterprise architects can benefit by addressing three primary considerations. Multi-cloud initiatives—drawing on services from public and private clouds—can help organizations stay ahead of the curve.

Interview: Prat Moghe, CEO of Cazena

I recently caught up with Prat Moghe, CEO of cloud data lake leader Cazena to get his take on how getting off the ground with cloud data lakes continues to be a major frustration for enterprises. We’re seeing such deployments taking at least six months and millions of dollars of annual spend for in-house development and management. There’s got to be a better way. Gartner has estimated the failure rate of big data projects as high as 80%. What can you do about companies that stubbornly hang on to legacy data strategies, using analytics/BI approaches that put them ever-more behind competitors who are modernizing their data stack with AI/ML/etc? In this interview, we’ll get some valuable perspectives for you to follow in accelerating your time-to-analytics.

Understanding NoSQL Databases: Document Stores

In this contributed article, Alex Williams, Writer/Researcher at Hosting Data UK, discusses document-oriented databases (also called: aggregate databases, document databases or document stores) and how they place each record, and its associative data, inside single documents. This database type is a subset of the NoSQL umbrella, which refers to the growing list of popular database management systems that use ‘non-relational’ models — i.e. databases that don’t rely on Structured Query Language, SQL.