Workload

Operational telemetry has the same plumbing problem as security data.

Mach5 can support product-facing telemetry search, incident timelines, derived aggregates, and long-retention analytics for high-volume operational data.

Mach5 workloadsearch, streaming, analytics
Search
low-latency customer-facing queries
Streaming
ingest, transform, enrich, and backfill
Analytics
tenant-aware dashboards and timelines
Derived data
fresh product-serving tables

How it works

A clear path from infrastructure pain to production workload.

Challenge

Where work slows down

Operational telemetry products need fast queries, fresh derived views, long retention, and tenant-aware dashboards. The supporting ingestion and serving layer often becomes a custom platform.

Gap

What legacy stacks miss

Metrics, logs, warehouses, and custom stream processors solve pieces, but teams still own the glue between ingestion, transformation, retention, query serving, and product UX.

Mach5

How teams move faster

Mach5 provides a single infrastructure layer for search, analytics, streaming transformations, and derived data over high-volume telemetry.

Outcomes

What teams get.

Search

Fast telemetry exploration

Serve product search and timelines over high-volume operational events.

Derive

Fresh incident context

Materialize aggregates, summaries, and entity views from streaming data.

Retain

Long-window analysis

Support historical queries without keeping everything in a hot cluster.

Common workflows

Work your team can run.

Incidents

Incident timelines

Build timeline and context views across events, services, hosts, and users.

Dashboards

Tenant dashboards

Serve customer dashboards with predictable query paths.

Pipelines

Streaming transforms

Create derived operational data from incoming telemetry streams.

Related workloads

Explore the related security data workloads.

Start with one workload.

Bring Mach5 the infrastructure pain your team wishes it did not own.

Start with one search, analytics, streaming, backfill, or derived-data workload and prove value before expanding.

Bring us one workload

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