High Level Architecture

Under the hood of Obsrv

Obsrv fuses multiple technologies together with extreme automation and detailed monitoring coupled with intelligent services to work on any cloud to enable multiple data use-cases via decoupled integrations. The chaining together of these layers give Obsrv its scalability, reliability and efficiency.

Obsrv in a box - Fuse together multiple layers

Following diagram explains the high level architecture of the Obsrv data platform

Obsrv Data Platform

Following are the key components in Obsrv:

1. Connectors: A connector (that can be literally dropped in) has the ability to pull the data from any source either as a stream/event or batch. The connector framework of Obsrv allows one to develop a connector quickly within a couple of days using popular technologies like Apache Spark and Apache Flink and in the language of their choice - java/scala/python. By design the framework takes care of scaling and reliability of the connectors

2. Data Pipeline: Data pipelines are Apache Flink and Apache Spark based jobs that are designed to process data at real-time speed. The data pipeline of Obsrv is extremely elastic and scales from 1 cpu to many cpus with minimal configuration changes and is also customizable and/or extendable.

3. Real-time OLAP Store: If configured, all the data is persisted in a real-time OLAP store called Apache Druid that would drive all real-time use-cases.

4. Data Lake and LakeHouse: As a default configuration, all data is persisted into a data lake (like S3 object store) and a LakeHouse called Apache Hudi. The LakeHouse and data lake drive the exhaust, AI/ML queries, batch aggregate queries and reporting needs.

5. Unified Query Engine: The unified query engine component takes care of all data driven use-cases. It allows the user to query using JSON, SQL and Spark/Trino interfaces.