> ## Documentation Index > Fetch the complete documentation index at: https://docs.siftstack.com/llms.txt > Use this file to discover all available pages before exploring further. # Automate telemetry review with Sift > Ingest telemetry. Define checks. Automate reviews. export const HomeCardGrid = ({cards}) => { return
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; }; Create a single source of truth for your telemetry by uploading files or connecting a live data stream. Upload a telemetry file to create a Run and start analyzing your data. Select the streaming method that fits your situation. Send telemetry to Sift using Python. Verify data flow, troubleshoot ingestion failures, and track pipeline performance. Explore your telemetry interactively to plot signals, spot patterns, and investigate anomalies. Open a Run in Explore, plot Channels, and build a multi-panel workspace to analyze telemetry. Isolate unusual signal behavior, compare related Channels, and identify the root cause of an anomaly. Use Live mode in Explore to monitor telemetry as it streams into Sift. Plot multiple Channels in the same workspace to identify correlations, trends, and dependencies between signals. Compare a new Run against a baseline Run to identify regressions and validate expected behavior. Apply the same Channel layout and settings to every new Run without rebuilding your workspace from scratch. Derive new signals from your raw telemetry using expressions, without modifying the original data. Compute a new signal from one or more existing Channels using a Calculated Channel expression. Define expression logic once and reference it across Calculated Channels, Rules, and User-Defined Functions. Automatically detect issues in your telemetry and track them to resolution. Write a Rule that flags conditions in your telemetry so deviations are caught automatically on every Run. Create a reusable checklist of Rules that runs against every new Run automatically. Review a Run to investigate and close issues. Assign, update, and resolve Annotations to drive a review to completion. Organize Reports from multiple Runs into a Campaign and track overall review progress. Create a named group of related Runs to use as a comparison baseline, statistical reference, and automated detection source. Write a Rule that automatically flags when a new Run falls outside the statistical envelope of your reference Runs. 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. Pull your telemetry out of Sift to power external dashboards, analysis tools, and custom pipelines. Download telemetry from a Run as CSV, Parquet, or Sun (WinPlot) for use in external tools or archiving. Use the REST API or official client libraries to pull Channel data into custom pipelines and analysis environments. Get telemetry from Sift into MATLAB using the Python client or the REST API. Connect Sift as a Grafana data source to build persistent dashboards for live monitoring and historical analysis. Govern who can access your telemetry data, manage users and permissions, and keep your workspace organized. Set up a webhook to receive notifications in an external system when a Rule is violated or resolved during live data ingestion. Define a structured taxonomy using Metadata keys to organize, categorize, and filter resources including Runs, Campaigns, and Annotations across your workspace. Invite users, manage their status, and organize them into groups to control access to Sift. Use Data Access Governance (DAG) to control access to Sift resources. Configure an external Identity Provider to manage users and groups in Sift using SCIM push provisioning.