Imagine you have a huge treasure chest overflowing with notes scribbled about past adventures. Each note tells a story, but the chest is messy, and finding specific stories takes forever. Azure Synapse Data Explorer is like a magical librarian who organizes the notes, extracts key insights, and tells you exciting tales hidden within.
Here’s what it does:
- Fast Analysis: Quickly analyzes massive amounts of data like logs, sensor readings, and website traffic – billions of entries in seconds!
- Time-Based Adventures: Focuses on data that changes over time, showing trends, patterns, and anomalies like a master storyteller.
- Powerful Search: Find specific information within the data ocean like a skilled treasure hunter. Need to know how many users logged in during a specific hour? No problem!
- Monitoring website traffic: See which pages are visited most, find peak hours, and understand user behavior to improve your website.
- Analyzing IoT sensor data: Track device performance, detect equipment failures before they happen, and optimize maintenance schedules.
- Debugging application performance: Identify slowdowns, find errors, and diagnose issues quickly to keep your applications running smoothly.
What makes Azure Synapse Data Explorer unique?
- Easy ingestion
- No complex data modeling
- No index maintenance
- Democratizing data analytics
- Proven technology at petabyte scale
When to use Azure Synapse Data Explorer?
Use Data Explorer as a data platform for building near real-time log analytics and IoT analytics solutions to:
- Consolidate and correlate your logs and events data across on-premises, cloud, and third-party data sources.
- Accelerate your AI Ops journey (pattern recognition, anomaly detection, forecasting, and more).
- Replace infrastructure-based log search solutions to save cost and increase productivity.
- Build IoT analytics solutions for your IoT data.
- Build analytics SaaS solutions to offer services to your internal and external customers.
Data Explorer pool architecture
Data Explorer pools implement a scale-out architecture by separating the compute and storage resources.
Data Explorer pools support a rich ecosystem for ingesting data using connectors, SDKs, REST APIs, and other managed capabilities. It offers various ways to consume data for ad hoc queries, reports, dashboards, alerts, REST APIs, and SDKs.
Kusto Query Language
KQL has a large, growing community with the rapid adoption of Azure Monitor Log Analytics and Application Insights, Microsoft Sentinel, Azure Data Explorer, and other Microsoft offerings. The language is well designed with an easy-to-read syntax and provides a smooth transition from simple one-liner to complex data processing queries