Janakiram MSV (Jani) is a practicing architect, research analyst, and advisor to Silicon Valley startups. He focuses on the convergence of modern infrastructure powered by cloud-native technology and machine intelligence driven by generative AI.
To track and process rapidly changing data, developers frequently poll the database to respond to changes. For example, when multiple temperature sensors' telemetry data is ingested into a time-series database, a query must be run on a regular basis to track it.
Similarly, an e-commerce application may need to trigger an event when a large transaction occurs. To track changes in both scenarios, the original database source must be polled on a regular basis. This puts additional strain on the database, which is already under stress from the frontend application's intensive read/write operations. Drasi takes a different approach to data collection by monitoring the logs generated by the database system. Every operation on a table, such as an insert, update, or delete, generates a log entry that is stored externally. Rather than querying the source, Drasi tracks changes using database logs. Because read operations are performed on log entries, the source database is less burdened.This continuously monitors for critical changes by connecting to a variety of data sources within your systems. A source monitors system metrics, database updates, or application logs and collects pertinent information in real time.Drasi employs continuous queries in place of manual, point-in-time queries, in order to continuously assess incoming changes in accordance with predetermined criteria. These queries, which are written in Cypher Query Language, integrate data from multiple sources without needing prior collation.Drasi runs registered automated reactions when changes finish a continuous query. These reactions can trigger a workflow that can send alerts, update other systems, or take corrective action.. The definitions of sources, continuous queries and reactions are applied through a YAML specification to Kubernetes. There is also a command-line interface to deal with the Drasi controller running in Kubernetes.as a Sandbox project. The source code is available on GitHub and the documentation along with tutorials is available on the It is worth noting that Microsoft has developed a platform for converting system logs and audit trails into a graph database and extending that to an event-driven environment. This opens up new opportunities for developers to create modern data processing systems based on large language models. Drasi has the potential to play an important role in RAG by providing real-time data changes as context for LLMs, enabling users and AI agents to make better decisions.Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space.Insults, profanity, incoherent, obscene or inflammatory language or threats of any kindContinuous attempts to re-post comments that have been previously moderated/rejectedAttempts or tactics that put the site security at riskProtect your community.
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