Coder Legion - Recent posts tagged datainfrastructure

CData Says Accuracy Is the Real Barrier to Enterprise AI — And the Numbers Back It Up

Mon, 09 Mar 2026 12:16:27 +0000

Most enterprise AI projects don't fail because of the model. They fail because the data underneath it isn't reliable, accessible, or well-governed. That's the argument CData Software is making — and at the Gartner Data & Analytics Summit this week in Orlando, the company is putting benchmarking data behind it.

CData announced significant enhancements to its Connect AI platform, a managed Model Context Protocol (MCP) server designed to give AI agents accurate, governed access to live enterprise data. The announcement centers on three areas: expanded connectivity through a new Connect Gateway, smarter agent tooling with a new scoped architecture, and tighter enterprise security controls.

The Accuracy Gap Is Bigger Than Most Teams Realize

CData ran an internal benchmark comparing five MCP providers — including unified API, native, gateway, and iPaaS approaches — across 378 queries spanning CRM, project management, data warehouse, and ERP platforms. Every response was scored against a predetermined correct answer. No partial credit.

Connect AI scored 98.5% accuracy (67 out of 68 correct). Competing providers ranged from 65% to 75% — failing on roughly one in three queries.

The gap was modest on simple queries. It widened significantly as complexity increased. The biggest failures across competing providers clustered in four areas: relative date logic, multi-filter queries, semantic interpretation of business terms, and write operations.

Jerod Johnson, CData's Director of Technology Evangelism, explained the date issue clearly: ask an LLM what Q3 means, and it may guess — or get it wrong. CData avoids that problem because its connectors have access to the full source system, including calendars and notes, so the agent doesn't have to infer fiscal calendars. It can look them up.

The write operation failures were telling in a different way. Many competing MCP providers simply don't support write-back actions. As organizations move from AI assistants toward autonomous agents that take real actions, that limitation matters a lot.

Why a 75% Accuracy Rate Is Actually a 75% Failure Rate

The compound effect is where this gets serious. At 75% accuracy per step, a five-step agentic workflow succeeds less than 24% of the time. At 98.5%, that same workflow still completes successfully more than 92% of the time. CData cited Demis Hassabis, CEO of Google DeepMind, noting that even a 1% error rate compounds like interest across thousands of sequential steps.

Johnson put the accuracy thresholds in practical terms:

Knowledge retrieval needs to exceed 85% to be useful
Decision support needs to clear 95%
Autonomous agents need to exceed 98% — because the cost of an error isn't just a wrong answer; it's a wrong action taken without a human in the loop

What's New in Connect AI

The platform enhancements fall across CData's three core pillars: connectivity, context, and control.

Connectivity: A new Connect Gateway extends live data access to on-premises sources behind the firewall — including SAP, SQL Server, and PostgreSQL — without requiring data replication. Connect AI already supports more than 350 data sources.

Context — Agent Tooling: This is the most significant part of the release. CData introduced three types of tools for agents:

Universal Tools — Eight tools that work consistently across all 350+ connected systems using a SQL abstraction layer. Agents don't get buried in hundreds of system-specific options, which reduces tool sprawl and context window bloat.
Source Tools — Tightly defined operations tied to a specific platform like Salesforce or Jira, giving teams control over exactly which actions are permitted.
Custom Tools — Purpose-built queries and operations scoped to a specific workflow, limiting token usage and reducing the risk of unintended data exposure.

Workspaces and Toolkits bundle these tools together. A Workspace defines which data an agent can access. A Toolkit defines which actions it can take. Each combination deploys as a dedicated MCP server — so agents only see and do what they're supposed to.

Control: New governance features include SCIM 2.0 for automated identity lifecycle management and Custom OAuth Applications so organizations can use their own credentials for compliance. The platform enforces per-user authentication with permissions applied at runtime and maintains full audit trails.

The Bigger Problem: Most AI Infrastructure Isn't Ready

CData's own research is blunt: only 6% of enterprises are satisfied with their current data infrastructure for AI. More than half still rely on custom-built connectors and pipelines, and 71% of AI teams spend more than a quarter of their implementation time on data integration alone — time spent on plumbing rather than building anything useful.

CData's identity passthrough approach means if a user connects through Claude or ChatGPT, the system knows who they are inside Salesforce and enforces those permissions automatically. Organizations don't have to rebuild a separate permission layer for AI access. They inherit what they already have.

Bottom Line

MCP is quickly becoming the standard interface between AI agents and business software. But not all MCP providers deliver the same results. CData's benchmark data — self-reported, though the company said it is pursuing third-party validation — shows a substantial accuracy gap that widens as query complexity increases.

For developers and engineers who've spent time wiring together custom integrations and watching AI fail on anything beyond a basic lookup, this is a familiar pain point. The argument CData is making — that the data layer, not the model, determines whether enterprise AI actually works — is one that tends to resonate with the people doing the implementation work.

The full benchmark methodology and results are available at cdata.com/lp/ai-accuracy-whitepaper.

Rebuilding Pocket Portfolio: Next.js 14 + Firebase + Edge

Mon, 03 Nov 2025 11:27:24 +0000

Rebuilding Pocket Portfolio: Next.js 14 + Firebase + Edge

(The practical bits that made “never 0.00” possible)

Pocket Portfolio’s Beta just levelled up — rebuilt for speed, reliability, and developer joy.

Why Rebuild

Goal: make “never 0.00” pricing and fast dashboards boringly reliable.
The MVP served its purpose — now it’s about consistency at scale and developer ergonomics.

What Changed

1. Edge Route Handlers (Quotes + Health)

Runs close to the user (avg TTFB ~250 ms)
Regional caching with intelligent revalidation
No full cold starts under burst traffic

2. React Server Components (RSC)

Dashboard loads lean: minimal JS, fewer waterfalls
Streaming partial updates for faster perceived speed

3. Firebase v10 (Auth + DB)

Unified Auth and Firestore via modular SDK
Cloud Functions only where stateful
Simplified local emulation with .env.test parity

Results

Metric	Old (MVP)	New (Beta)	Improvement
TTFB (EU)	610 ms	420 ms	31 % faster
Quote p95	820 ms	<450 ms	45 % faster
Cold starts (health)	Frequent	Near-zero	Eliminated
Dashboard JS bundle	312 KB	190 KB	39 % smaller

Guardrails

Canary flag for staged rollouts per region
Smoke endpoints for uptime checks
In-UI health badges for trust visibility

Try It

git clone https://github.com/pocket-portfolio/app
cd app
pnpm install
pnpm dev

Then test locally:

curl http://localhost:3000/api/health-price

Open your browser at http://localhost:3000 — you’ll see the RAG widget live.

Web: https://pocketportfolio.app
Beta: https://next.pocketportfolio.app
Repo: https://github.com/PocketPortfolio/Financialprofilenetwork

Architecture Snapshot

User → Edge Route Handler → Regional Cache → Firebase (Auth + Firestore)
       → Cloud Functions (stateful logic) → Data Providers (Yahoo, IEX, AlphaVantage)

Open to PRs

Help polish the beta — these are top of backlog:

Edge error taxonomy (better retries and logs)
Redis TTL tuning for quotes and health
Test seeds for new providers
Improved CI performance dashboards

Fork, branch from dev/beta, and open a PR with feat: or fix: prefix.
We’ll review, benchmark, and deploy.

Join the Conversation

Discord: [CoderLegion / #pocket-portfolio]https://discord.com/invite/Ch9PpjRzwe)
Dev.to Series: Pocket Portfolio — Shipping in Public

TL;DR

Edge + Firebase + RSC = reliable dashboards.
Pocket Portfolio Beta is now fast enough to disappear — and that’s the point.

DDN Infinia delivers sub-millisecond latency, 20x faster than AWS S3, transforms AI app development.

Mon, 07 Jul 2025 10:33:44 +0000

DDN Unveils Infinia 2.0: Sub-Millisecond Data Access for AI Development at Scale

Data intelligence company DDN has emerged from relative obscurity with a compelling story about solving one of AI development's most persistent challenges: getting data to GPUs fast enough to maximize training efficiency. At the recent IT Press Tour, DDN demonstrated how their new Infinia 2.0 platform is reshaping the infrastructure stack for AI applications, particularly for developers building at scale.

The GPU Utilization Problem

DDN's value proposition centers on a critical bottleneck many machine learning engineers face: GPU idle time during model training. As CEO Paul Bloch explained, "When people buy 20,000 GPUs, that's a $3 billion investment. The last thing you want is idle GPUs." DDN's data shows that without optimized storage, GPUs can sit waiting for data up to 60% of the time during training workflows.

The company's solution involves positioning their data intelligence platform between applications and infrastructure to eliminate these bottlenecks. According to DDN, their platform increases GPU efficiency by 30% while reducing data center footprint and power consumption by 10x.

Infinia's Technical Architecture

The technical innovation behind Infinia 2.0 represents a fundamental departure from traditional storage architectures. CTO Sven Oehme detailed how the platform uses a native key-value store at its foundation, with all protocols—S3, GCS, POSIX, SQL—operating as peers rather than layers.

"Traditional storage systems have a block layer, then a file system layer, then an object layer on top," Oehme explained. "In Infinia, all data services are peers accessing the same key-value store directly. This eliminates multiple software layers and dramatically reduces latency."

The architecture enables impressive performance claims: DDN demonstrates 100x faster object listing compared to AWS S3, 25x lower time-to-first-byte latency, and the ability to handle millions of concurrent operations. In production at Elon Musk's xAI facility, Infinia manages nearly 600 petabytes with over one million concurrent jobs.

Developer Integration Points

For developers, DDN provides multiple integration pathways. The platform offers native S3 compatibility while adding support for Google Cloud Storage APIs—making DDN potentially the only on-premises solution that can natively run Google Cloud applications without data transformation.

The company is releasing a comprehensive SDK with bindings for multiple languages including Python, Java, Go, and Rust. This allows developers to build custom data services directly on top of Infinia's key-value store, leveraging its high-performance characteristics.

Particularly relevant for AI development workflows, DDN supports NVIDIA's KV cache offloading, allowing GPU state to be quickly stored and restored. This enables more efficient resource utilization for inference workloads where users may pause and resume sessions.

Multi-Cloud Strategy

DDN's partnership with Google Cloud represents a significant shift for the traditionally HPC-focused company. The Google Cloud Managed Lustre service, powered by DDN's EXAScaler technology, provides developers with persistent parallel file systems that can scale from small experiments to massive training runs.

"It's not a marketplace, this is Google's first-party offering," Bloch emphasized. "Every Google rep is incentivized to sell this." The integration means developers can seamlessly move workloads between on-premises and cloud environments using the same storage APIs and performance characteristics.

Real-World Performance Gains

DDN's most compelling demonstration involved a retrieval-augmented generation (RAG) pipeline running on AWS infrastructure. By simply replacing AWS S3 with Infinia software running on AWS virtual machines, they achieved 22x performance improvement while reducing costs by over 60%.

The demo used unmodified AWS microservices, changing only the storage layer. This suggests developers could potentially achieve significant performance gains without rewriting applications, a crucial consideration for teams with existing AI infrastructure investments.

Industry Context

DDN's emergence coincides with the AI infrastructure market's rapid evolution. The company reports powering over 700,000 GPUs across customer deployments, with individual clusters reaching 200,000 GPUs. Their recent $300 million investment from Blackstone at a $5 billion valuation reflects growing recognition that data infrastructure will be critical as AI moves from training to inference at scale.

"We're seeing customers asking for multiple exabytes in single deployments," Oehme noted. "The data requirements are growing faster than compute in many cases."

Looking Forward

For developers building AI applications, DDN's technology suggests a future where data infrastructure becomes increasingly software-defined and protocol-agnostic. The ability to run the same storage platform from edge devices to exascale supercomputers, while maintaining consistent APIs across cloud and on-premises environments, could significantly simplify the development and deployment of distributed AI systems.

As AI applications move beyond foundational models to production inference workloads serving millions of users, the emphasis on ultra-low latency data access that DDN provides may become table stakes rather than competitive advantage. For now, their technology appears to be pushing the boundaries of what's possible in high-performance data infrastructure.

DDN unveils Infinia 2.0: Sub-millisecond data access for AI development at scale.

Sun, 15 Jun 2025 11:56:23 +0000

DDN Unveils Infinia 2.0: Sub-Millisecond Data Access for AI Development at Scale

The GPU Utilization Problem

The company's solution involves positioning its data intelligence platform between applications and infrastructure to eliminate these bottlenecks. According to DDN, their platform increases GPU efficiency by 30% while reducing data center footprint and power consumption by 10x.

Infinia's Technical Architecture

The architecture enables some impressive performance claims: DDN demonstrates 100x faster object listing compared to AWS S3, 25x lower time-to-first-byte latency, and the ability to handle millions of concurrent operations. In production at Elon Musk's xAI facility, Infinia manages nearly 600 petabytes with over one million concurrent jobs.

Developer Integration Points

For developers, DDN provides multiple integration pathways. The platform offers native S3 compatibility, while also adding support for Google Cloud Storage APIs, making DDN potentially the only on-premises solution that can natively run Google Cloud applications without requiring data transformation.

The company is releasing a comprehensive SDK with bindings for multiple languages, including Python, Java, Go, and Rust. This allows developers to build custom data services directly on top of Infinia's key-value store, leveraging its high-performance characteristics.

Multi-Cloud Strategy

"It's not marketplace—this is Google's first-party offering," Bloch emphasized. "Every Google rep is incentivized to sell this." The integration enables developers to seamlessly move workloads between on-premises and cloud environments, using the same storage APIs and performance characteristics.

Real-World Performance Gains

The demo used unmodified AWS microservices, changing only the storage layer. This suggests developers could potentially achieve significant performance gains without rewriting applications—a crucial consideration for teams with existing AI infrastructure investments.

Industry Context

DDN's emergence coincides with the rapid evolution of the AI infrastructure market. The company reports powering over 700,000 GPUs across customer deployments, with individual clusters reaching 200,000 GPUs. Their recent $300 million investment from Blackstone at a $5 billion valuation reflects growing recognition that data infrastructure will be critical as AI moves from training to inference at scale.

"We're seeing customers asking for multiple exabytes in single deployments," Oehme noted. "The data requirements are growing faster than compute in many cases."

Looking Forward

For developers building AI applications, DDN's technology suggests a future where data infrastructure becomes increasingly software-defined and protocol-agnostic. The ability to run the same storage platform across edge devices, exascale supercomputers, and maintain consistent APIs across cloud and on-premises environments could significantly simplify the development and deployment of distributed AI systems.

As AI applications transition from foundational models to production inference workloads serving millions of users, the emphasis on ultra-low latency data access that DDN provides may become a table-stakes requirement rather than a competitive advantage. For now, their technology appears to be pushing the boundaries of what's possible in high-performance data infrastructure.