ClickHouse vs infrai

ClickHouse expands collaboration with Microsoft, bringing Fabric integration, deeper OneLake interoperability, and enterprise deployment flexibility

ClickHouse announced a significant expansion of its strategic collaboration with Microsoft at The European Microsoft Fabric + SQL Community Conference in Barcelona on September 29, 2026. The expansion includes the public preview of the ClickHouse workload for Microsoft Fabric, general availability of OneLake read, public preview of OneLake write, and availability of ClickHouse Bring Your Own Cloud (BYOC) in the Microsoft Marketplace. These capabilities aim to enable sub-second analytics on OneLake data and provide enterprise deployment flexibility without data duplication.

• ClickHouse announced the expanded Microsoft collaboration on September 29, 2026 at The European Microsoft Fabric + SQL Community Conference in Barcelona.
• The ClickHouse workload for Microsoft Fabric is now available in public preview through the Fabric workload hub.
• ClickHouse support for reading Apache Iceberg tables from OneLake using OneLake Table APIs is now generally available.

ClickHouse expands collaboration with Microsoft, bringing Fabric integration, deeper OneLake interoperability, and enterprise deployment flexibility

On September 29, 2026, at The European Microsoft Fabric + SQL Community Conference in Barcelona, ClickHouse announced an expanded strategic collaboration with Microsoft. The announcement includes the public preview of the native ClickHouse workload for Microsoft Fabric, general availability of OneLake read, public preview of OneLake write, and availability of ClickHouse BYOC in the Microsoft Marketplace. These capabilities let joint customers query OneLake data at sub-second speeds without copying data, while BYOC provides managed ClickHouse Cloud inside a customer's Azure tenant with procurement via Microsoft agreements.

• On September 29, 2026, ClickHouse announced the expanded collaboration with Microsoft at The European Microsoft Fabric + SQL Community Conference in Barcelona.
• The native ClickHouse workload for Microsoft Fabric is now available in public preview through the Fabric workload hub, enabling sub-second analytics on OneLake queries.
• ClickHouse support for reading Apache Iceberg tables from OneLake using OneLake Table APIs is generally available; writing Iceberg tables to OneLake is in public preview.

ClickHouse appoints Mike Scarpelli, former Snowflake and ServiceNow CFO, to Board of Directors

ClickHouse, a leader in real-time analytics, data warehousing, observability, and AI/ML, today announced the appointment of Michael Scarpelli to its Board of Directors.

SMS Delivery Status Polling for Waitlist Outage Alerts

The article advises that teams should only rely on an SMS API for critical outage alerts if their backend can poll delivery status and own retry, escalation, cancellation, and timing logic. Delivery reliability and timing constraints drive the design: define service-level objectives, record four reliability invariants (application-owned send IDs, bounded/idempotent retries, defined next actions per delivery state, and incident recovery that suppresses obsolete alerts), and treat providers as transport adapters. The author shortlists Twilio, Vonage, Sinch, and Infrai for evaluation, provides load-testing guidance, and includes a runnable Python example that polls SMS status, honors Retry-After, and applies backoff. The recommended architecture keeps durable incident state in the application and makes provider polling a replaceable adapter.

• Choose an SMS API for critical outage alerts only if the backend can poll delivery status and implement retry, escalation, cancellation, and timing logic.
• Four reliability invariants: application-owned identifier per send; bounded and idempotent retries; every delivery state must map to a defined next action; incident recovery must stop obsolete alerts.
• Article shortlists Twilio, Vonage, Sinch, and Infrai as candidate SMS providers to validate against the same decision record.

Backend-Owned SMS OTP: Cooldowns and Attempt Caps

This technical blog post explains best practices for implementing passwordless phone logins using SMS OTPs in an Express/Node.js backend. It argues that the backend must own resend cooldowns, verification attempt counters, and anti-abuse policies (not the client), model the authentication state machine (ready → code_sent → verified/expired/locked), persist minimal authoritative state, use atomic database transitions, emit single transition events for observability, and use idempotency keys and retry/backoff handling when calling providers. Provider choices (Twilio, Firebase, Auth0, Amazon SNS, Infrai) are discussed with trade-offs between managed verification and owning template/state-machine responsibilities.

• The article recommends the Express/Node.js backend should own SMS OTP resend cooldowns, maximum verification attempts, and anti-abuse counters rather than trusting the client.
• Designs should expose explicit states: send-code, verify-code, resend-code, and lockout; persist minimal authoritative state (challenge ID, phone identity, expiry, next-send time, counters, lockout).
• Use atomic database transitions and idempotency keys tied to admitted transitions to prevent race conditions and duplicate sends.

Bulk LLM Text Classification with Tenant Chargeback

The article recommends treating tenant accounting as the primary artifact when performing bulk CSV moderation with LLMs: create a tenant-owned job with stable row IDs, estimate costs before submission, submit asynchronous batch classification (preferably chat classification with a closed label set), and attach returned results and export references to the same tenant ledger for reconciliation. The author provides an example TypeScript batch submission pattern (idempotency derived from the validated request, bounded retries, handling 429), argues for allocating costs at the job boundary and reconciling at the row level, and discusses when to call providers directly (Infrai, OpenAI, Anthropic, Google Gemini) versus renting batch execution.

• Author recommends asynchronous chat classification with a closed label set and using a tenant ledger as the primary artifact for billing and reconciliation.
• Pattern: create a tenant-owned job with a stable ID per accepted CSV row, show an estimate before submission, persist the provider batch identifier, then reconcile results and costs back to the job and rows.
• TypeScript example demonstrates deriving an idempotency key from the validated batch-request.json, honoring Retry-After for HTTP 429, and using bounded exponential backoff.