Articles about Big Data

As businesses continue to generate and rely on vast amounts of data, the traditional approach to managing that data is no longer sufficient.

Enter the concept of a data mesh -- a decentralized, domain-driven approach to data architecture that promises to transform how organizations handle and leverage their data. But the question remains: should a business create a data mesh? What value does it add, and what challenges does it help solve?

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New research shows that 30 percent of large enterprises have already made the strategic commitment to a sovereign AI and data platform, and 95 percent say it will be mission critical for them within the next three years.

The research by EDB interviewed more than 2,000 senior executives across 13 countries about how they are planning for the agentic AI world. The initial findings show that 30 enterprises a day are making strategic commitments to becoming sovereign AI and data platforms.

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A new report finds that 76 percent of enterprises admit they've made business decisions without consulting available data because it was too difficult to access, creating a concerning gap between data availability and data-driven decision-making.

The report from Sisense with research firm UserEvidence shows that although 81 percent of organizations believe they have good or full control of their data, 64 percent acknowledge that they can't reliably access it for decision-making.

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A new survey, of 1,000 purchasing decision makers across the US, UK, France, and DACH (Germany, Austria and Switzerland) regions, reveals that while AI investment is the top spending priority for nearly half of businesses, only one-fifth of surveyed companies feel confident their data is AI-ready.

The research from Nasuni finds 96 per cent of respondents say they face challenges migrating their file data, creating a major roadblock for AI initiatives. There's also a misalignment in investment priorities, while nearly half of respondents cite AI as their top spending priority over the next 18 months, only a third will invest in the necessary cloud data management.

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A new survey looking at the challenges faced by database administrators (DBAs) shows 35 percent of organizations cite data integration as a top challenge.

Additionally, the study from Quest Software shows 42 percent of organizations report that data migrations are a significant obstacle to overcome, with complications in ensuring database reliability and performance.

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In DevOps and IT circles, the word 'observability' has been bandied about for the past few years. Observability is one of those hot and trendy terms which also means different things to different people.

Yet the goal is generally the same: how can we observe our environment and then proactively and even automatically make fixes to things that aren't working, are anomalous, suspicious and/or could potentially cause a disastrous outcome? Such outcomes could include a network failure, a security breach, a server reaching capacity, or in the unstructured data management world -- something else entirely.

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According to a new study of 750 business and tech leaders, 58 percent say key business decisions are based on inaccurate or inconsistent data most of the time, if not always.

The research from IT consulting and digital services provider SoftServe shows the majority don’t understand the value of their data with 65 percent of all respondents believing no one at their organization understands all the data collected and how to access it.

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According to a new report, 60 percent of respondents claim that their organization is failing to keep pace with data changes resulting from AI demands.

In addition, the study from Immuta shows that traditional data architecture challenges persist, with nearly half of organizations identifying compliance and privacy as primary data concerns, and 64 percent citing significant challenges in providing timely and secure access to data for authorized users.

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Lots of organizations are rushing to embrace AI and hoping to deliver business value. But AI is only as good as the data that underpins it.

We spoke to Julian LaNeve, chief technology officer of Astronomer, to discuss why it's important to fix the foundations before implementing AI solutions.

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Data comes in many forms, it may be structured, it may be unstructured, it could be sensitive or purely statistical. Whatever it looks like you can only benefit from it if you know what you have, where it can be found and how to access it.

We spoke to Brett Hurt, CEO and co-founder of data.world to discuss how enterprises can understand their data and derive the maximum value from it.

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With the intense interest in AI and its rapid pace of embrace, organizations are under pressure to swiftly evaluate their data architecture. AI architects urgently need solutions that harness AI to boost revenue streams and streamline operational efficiencies, all while navigating the potential barriers to their success.

David Flynn, co-founder and CEO of Hammerspace, a specialist in the use and preservation of unstructured data, recently shed light on the growing complexity IT teams face in managing data pipelines. This complexity is further compounded as organizations integrate LLMs into AI applications, underscoring the significant obstacles to successful AI adoption.

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As we've already seen today organizations are struggling with the increasing demands of data infrastructure. Another new report from MinIO highlights how organizations are leveraging object storage for AI, machine learning (ML), and data-intensive workloads.

The survey of over 650 IT leaders 70 percent of enterprise data is in object storage today and this is expected to grow to 75 percent over the next two years.

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Internal IT teams are being pushed to the brink, balancing the demands of modern data management with limited resources according to a new report.

The study from DataStrike gathered insights from nearly 200 IT leaders and decision-makers, it finds that 55 percent of organizations rely solely on internal IT teams to manage their data infrastructure, with only a small fraction (19 percent) outsourcing these critical functions.

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In today's data-driven world, organizations across different sectors need scalable and efficient solutions for processing large volumes of data.

Kubernetes offers an open-source and cost-effective platform for deploying and managing big data tools and workloads, ensuring optimal resource utilization and minimizing operational overhead. If you want to master the art of building and deploying big data solutions using Kubernetes, then this book is for you.

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GenAI is set to drive significant productivity gains, leading to massive economic growth, but enterprises face the challenge of deploying GenAI systems at scale and safely connecting to data systems while ensuring proper controls and governance.

To address this Securiti is releasing Gencore AI, a first of its kind holistic solution to easily and quickly build safe, enterprise-grade GenAI systems, copilots and AI agents.

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