Multiple sources and
destinations

This tool support a broad range of sources and support numerous destinations like elasticsearch, kafka, mongodb etc

C​loud native

This product is true cloud-native. And this product inherit all the properties and advantage of Cloud-native

Reproducible pipelines​​

Data pipelines can be created, templatized and can be re-used

Overview

Data is critical to any business. Data can even tell us where your business is heading. And collection and aggregation of this huge data is a tedious task. Even if you were able to collect data, that will not make any sense if it’s not stored properly.

We call this process ETL (Extract, Transform, Load). People who does ETL are called Data engineers. For Data engineers to work on ETL they need help of Platform Engineers to build the infrastructure for deploying the ETL tools. Once the ETL tools are deployed, a Data engineer can work on the incoming data to do the transformation and load it to any Data warehouse.

Once the data is ready, business owners can analyze the stored data and get meaningful insights about their business. Report creation is done by BI developers, they use tools such as tableau, power BI, grafana, kibana etc. These tools have to be installed and configured by the platform engineer.

ops_brew adfolks

Proposed solution

Ops_brew pipelines allows you to create data systems as pipelines which once created can be deployed on any cloud services

Ops_brew is a solution which can be deployed in on prem or cloud. Our solution will include services for creating and monitoring data ingestion pipelines, we will include all the commonly used tools for pipeline creation. User will have the freedom to choose the set of tools for their use cases. ​

  • Multi-source multi-destination data/log collection and aggregation Application.
  • Modular architecture for security​
  • Faster Searching and indexing.​
  • SaaS and PaaS [hybrid and multi-cloud]
  • Platform built on Cloud-Native architecture​
  • Automated infrastructure build and tool deployment.​
  • Templatize pipeline build for reuse and contribute to community.​

Use cases

Fraud detection
Anomality detection
Rule-based Alerting
Quality Monitoring of Telco Networks​
Business Process Monitoring​
Analysis of product updates
Ad-hoc analysis of live data in consumer technology​
Large Scale Graph Analysis​
Real-time search indexing in e-commerce​
Continuous ETL in e-commerce​
Collect and aggregate different types of logs in central location​
Replicate Logs to Multiple Destinations ​
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