Ingest telemetry
Bring historical or live telemetry into Sift by uploading files or connecting a data stream.
Import data from a file
Upload a telemetry file to create a Run and start analyzing your data.
Choose a streaming method
Select the streaming method that fits your situation.
Stream your first telemetry data with Python
Send telemetry to Sift using Python.
Monitor ingestion health
Verify data flow, troubleshoot ingestion failures, and track pipeline performance.
Visualize and analyze
Explore your telemetry interactively to plot signals, spot patterns, and investigate anomalies.
Analyze a Run
Open a Run in Explore, plot Channels, and build a multi-panel workspace to analyze telemetry.
Investigate a telemetry anomaly
Isolate unusual signal behavior, compare related Channels, and identify the root cause of an anomaly.
Monitor telemetry in real time
Use Live mode in Explore to monitor telemetry as it streams into Sift.
Compare signals over time
Plot multiple Channels in the same workspace to identify correlations, trends, and dependencies between signals.
Compare a Run against a baseline
Compare a new Run against a baseline Run to identify regressions and validate expected behavior.
Standardize analysis across Runs
Apply the same Channel layout and settings to every new Run without rebuilding your workspace from scratch.
Transform telemetry
Derive new signals from your raw telemetry using expressions, without modifying the original data.
Create a derived signal
Compute a new signal from one or more existing Channels using a Calculated Channel expression.
Reuse expression logic
Define expression logic once and reference it across Calculated Channels, Rules, and User-Defined Functions.
Automate telemetry review
Automatically detect issues in your telemetry and track them to resolution.
Detect deviations automatically using Rules
Write a Rule that flags conditions in your telemetry so deviations are caught automatically on every Run.
Set up a repeatable review checklist
Create a reusable checklist of Rules that runs against every new Run automatically.
Detect and review issues in a Run
Review a Run to investigate and close issues.
Triage and close out flagged issues
Assign, update, and resolve Annotations to drive a review to completion.
Track a multi-Run review campaign
Organize Reports from multiple Runs into a Campaign and track overall review progress.
Group Runs into a Family
Create a named group of related Runs to use as a comparison baseline, statistical reference, and automated detection source.
Detect statistical deviations using Family Rules
Write a Rule that automatically flags when a new Run falls outside the statistical envelope of your reference Runs.
Compare Runs visually against a Family baseline
Overlay Family members on a shared aligned time axis in Explore to inspect variance, identify outliers, and compare a new Run against the historical spread.
Export and integrate
Pull your telemetry out of Sift to power external dashboards, analysis tools, and custom pipelines.
Export data to a file
Download telemetry from a Run as CSV, Parquet, or Sun (WinPlot) for use in external tools or archiving.
Export data programmatically
Use the REST API or official client libraries to pull Channel data into custom pipelines and analysis environments.
Export data to MATLAB
Get telemetry from Sift into MATLAB using the Python client or the REST API.
Visualize Sift telemetry in Grafana
Connect Sift as a Grafana data source to build persistent dashboards for live monitoring and historical analysis.
Manage workspace
Govern who can access your telemetry data, manage users and permissions, and keep your workspace organized.
Get alerted when a limit is breached
Set up a webhook to receive notifications in an external system when a Rule is violated or resolved during live data ingestion.
Organize resources with Metadata
Define a structured taxonomy using Metadata keys to organize, categorize, and filter resources including Runs, Campaigns, and Annotations across your workspace.
Manage user access
Invite users, manage their status, and organize them into groups to control access to Sift.
Set up attribute-based access policies
Use Data Access Governance (DAG) to control access to Sift resources.
Connect an Identity Provider
Configure an external Identity Provider to manage users and groups in Sift using SCIM push provisioning.