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300+ Security Sources to the Lakehouse
Security teams moving to Databricks for security analytics, or evaluating Lakewatch as a SIEM replacement, face the same problem: getting data out of hundreds of security tools and into Delta Lake in a format that's actually useful. Monad handles the ingestion, normalization, and routing so teams get to full coverage faster, without building custom pipelines.
The Solution
Monad streams security telemetry directly into Databricks Delta Lake tables via Unity Catalog. The output handles automatic table creation, schema inference, compressed staging, and OAuth M2M authentication, all validated before data flows. Enterprise security teams at companies like Robinhood and CoreWeave run Monad in production across cloud and on-prem environments. With 300+ pre-built connectors tested daily against live APIs, Monad eliminates the months of custom pipeline work that typically stall Databricks security deployments and SIEM migrations.
De-Risk Your SIEM Migration
SIEM migrations don't happen in a single cutover. Teams need to run their legacy SIEM in parallel while they build confidence in Databricks. Monad makes this practical: route the same data to Databricks and your existing SIEM simultaneously, then shift traffic source by source as you're ready. No duplicate pipelines, no gap in coverage. Every data feed your legacy SIEM had out of the box is covered by Monad's 300+ connectors from day one.
Land Query-Ready Data in Delta Lake
Raw JSON dumped into Delta tables means analysts writing ad hoc parsers in notebooks instead of running detections. Monad can normalize data to common schemas, including OCSF, before it lands in your Lakehouse. Teams that enable normalization get structured data that Lakewatch detection-as-code rules, AI agents, and Spark-based analytics can operate on immediately, with consistent field names across sources.
Cover Cloud and On-Prem in One Platform
Enterprise security environments span cloud services, SaaS tools, and on-prem infrastructure. Getting full visibility means covering all of them. Monad's 300+ connectors reach across your entire stack, cloud and on-prem, managed from a single platform. Every connector is tested daily against live data, not validated once at ship time, so when an upstream vendor ships a breaking change, Monad catches it before your team does.
Production-Grade Throughput, No Maintenance
Monad's Databricks output writes gzip-compressed JSONL files to Unity Catalog Volumes, then bulk-loads them via COPY INTO, tuned for throughput. Batch defaults are optimized at 50,000 records or 10MB per batch with a 30-second publish interval, all tunable. Schema evolution is handled automatically with mergeSchema support. Test Connection validates every required permission before data flows, so teams don't discover access issues in production at 2am.
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