Systems | Development | Analytics | API | Testing

fivetran

Why every data role needs Open Data Infrastructure

May 6, 2026 By Jamie Cole In Fivetran
Analysts, data engineers, ML engineers, and data scientists don’t work the same way; they shouldn’t have to. Today’s data ecosystem includes more roles, more tools, and more specialized workflows than ever before. The days of limiting access to a single warehouse or lake — controlled by a small group of data engineers or analysts — are over.
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Scaling AI with Trust: Real-Time Access to Governed Data

May 6, 2026 By Cloudera, Inc. In Cloudera
Most AI strategies aren't failing because of models—they’re failing because data is fragmented, siloed, and hard to access. In fact, nearly 8 and 10 organizations say incomplete data access is holding them back. Moving the data drives up cost, introduces latency, and increases compliancy and security risks. Cloudera has introduced the Workflow Data Fabric Zero Copy Connector for ServiceNow to solve this. It allows you to securely leverage nearly 30 exabytes of data under management to power agented workflows without moving the data from wherever it lives.
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Ep 72 | The Data Governance Coach: From Data Error to Insight

May 6, 2026 By Cloudera, Inc. In Cloudera
In the world of enterprise AI, the pressure on data has changed. What used to be “good enough” now gets amplified by faster decisions, and therefore, faster mistakes. Governance is fundamental in ensuring data trust and integrity. In this episode of The AI Forecast, Paul Muller sits down with The Data Governance Coach, Nicola Askham, to share her pragmatic perspective and assert that governance only delivers value when it’s simple enough for people to use and embedded into day-to-day work.
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databox

New: Close The Gaps In Your Reporting Stack With Custom Integrations

May 6, 2026 By Monise Branca In Databox
Most teams work across dozens of tools, and not all of them connect to their reporting workflows out of the box. There are always sources that fall outside the native integrations list: an internal tool your team built, a platform specific to your industry, or a piece of software that a vendor hasn’t prioritized supporting yet. When that data isn’t directly available, teams get it in however they can.
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ClearML

Secrets, Credentials, and the Kubernetes Attack Surface in AI Environments

May 5, 2026 By Adam Wolf In ClearML
Every AI workload needs credentials: cloud storage keys, model registry tokens, database passwords, and API keys for external services. How those credentials are managed in Kubernetes determines whether they stay secret or become the entry point for a serious breach. ClearML Vaults addresses this directly by separating credential ownership from credential use at the platform level. This is the second post in our four-part series on Kubernetes Security for Enterprise AI Environments.
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zymr

Building Compliant Banking Platforms in a Multi-Cloud Environment: Architecture, Risks & Best Practices

May 5, 2026 By Haresh Kumbhani In Zymr
Banks are under pressure. Not just to innovate, but to do it safely. Customers expect seamless digital experiences. Regulators expect absolute control. And somewhere in between, banks are trying to modernize systems that were never designed for this level of speed or scrutiny. This is where Compliant Banking Platforms come into play. Today, financial firms have already embraced hybrid or multi-cloud strategies to balance costs and meet stringent compliance requirements.
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hitachi vantara

Turning Virtualization Modernization Into Business Outcomes

May 5, 2026 By Dinesh Singh In Hitachi Vantara
As enterprises navigate rising virtualization costs and increasing infrastructure complexity, many are rethinking their approach to modernization. One organization leading this transformation is Alior Bank, a forward-looking financial institution that successfully modernized its IT environment to improve agility, resilience, and cost efficiency.
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dreamfactory

Multi-Version API Management for AI Workflows | DreamFactory

May 5, 2026 By Kevin Hood In DreamFactory
Last Updated: May 2026 Asking the right questions when building an API for AI systems is critical, especially when updates risk breaking existing integrations. Here's the deal: API versioning ensures your AI workflows stay stable while introducing new features. By supporting multiple API versions, you can test updates, maintain compatibility, and avoid disruptions.
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confluent

Why Real-Time Stream Processing Beats Batch ETL for AI Data Freshness in 2026

May 5, 2026 By Manveer Chawla In Confluent
AI has evolved fast. We've gone from static, predictive models to dynamic, interactive agents. But most organizations still run data pipelines that haven't kept up. Consider what’s happening in modern AI architecture. Teams deploy high-performance engines like large language models (LLMs) and real-time fraud detectors, then feed them data that's hours or days old.
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confluent

Integrating AI Into Apache Kafka Architectures: Patterns and Best Practices

May 5, 2026 By Manveer Chawla In Confluent
Adding large language models (LLMs) and artificial intelligence (AI) to real-time event streams comes down to one thing: picking the right boundary between data transport and model compute. Where you run inference determines your system's resilience, latency, and cost. This article is for data engineers, streaming architects, and developers who want to add AI capabilities to their Apache Kafka event backbone without destabilizing production consumer groups or blowing through API rate limits.
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