FAQs

Gathr provides it’s users access to a library of more than 150 built-in connectors including multiple messaging queues, file systems, cloud sources, JDBC, No-SQL databases, Indexing stores, streaming sources and more.

Users can use a custom operator and configure their own sources, or reach out to our support team at support@gathr.com and we will create a source operator for your needs and populate to your environment.

Yes it is possible to route messages to different operators. We have an out of the box filter processor that can be used to apply the criteria and direct the messages to different operators.

Yes. You can use the sub-system integration feature to integrate multiple streaming pipelines.

We have an integrated CEP engine that allows us to perform complex event processing in real-time. The CEP engine enables us to correlate events from multiple sources and detect pattern amongst them. It also allows us to perform patter detection on a single source of data.

Yes. You can apply statistical functions on a moving time window in real-time. There is an out of the box Statistical CEP processor that can be used to run statistical functions on both fixed and moving windows in real-time.

Yes definitely. The best part of Gathr as a framework is the amount of flexibility it provides. You can write your own business logic and integrate it in the pipeline via the ‘Custom processor’.

Gathr bundles its own ‘Cluster Provisioning and Deployment’ tool. This tool allows you to install the product along with its pre-requisites in simple and easy steps. A complete installation guide is available to walk you through the install process.

Yes.

Yes.

Gathr is bundled with a very useful feature called import and export of pipelines. Once you test your pipelines on the development cluster you can export these pipelines by just a click of a button. These exported pipelines can then be imported into the destination environment without the need of building/configuring any of your pipeline.

Gathr supports all major cloud vendors. It also provides provision to host your application on a hybrid cloud or a hybrid Cloud and on-premise infrastructure. For more details regarding the same please reach out to the Gathr support team at support@gathr.one

Write to support@gathr.one and you representatives will get in touch with you for your request.

Gathr helps all it’s customers with customisations as needed to suit their use-case. Raise request for the same and our representatives will get in touch with you for the technical and commercial details around customization requests. Write to support@gathr.one

Gathr’s support team helps users with custom use-case development in the trial period as well. Raise a request at support@gathr.one

You should use Gathr if you need to perform heavy duty data analytics involving ETL, ELT, data integrations, multi-pipeline orchestration, machine learning, and advanced data analytics.

Yes, Gathr has a concept of workspaces that can be leveraged for multi-tenancy. User activity in one workspace is isolated from other workspaces in the system.

Gathr has built in support for Complex Event Processing. The CEP operators can be used for pattern matching using “SQL like” queries.

Developers can define temporal patterns such as A followed by B followed by C or detect absence of an event in a pattern etc. Developers can write queries either using the CustomCEP processor or register a query through REST UI to enable dynamic configuration of queries and actions such as update/delete.

The user can also trigger actions based on the output of such temporal queries.

Gathr has robust set of tools to build and operationalise machine learning models at scale. Following are the advanced features supported by Gathr play a key role in building a robust analytics solutions

1. Visual interface of data preparation and representation of data in a graphical pane to derive correlation and feature engineering
2. Training Models on multiple prominent technologies like SparkML, Notebooks, Python, H2O and similar tools
3. Model Testing by distributing data into various training and testing sets, and model performance interface with multiple indicators to give a holistic view of various performance metrics and in depth view of model's performance.
4. Hyper parameter tuning to tweak important metrics of the model and fine tune the output accuracy
5. Workflows in Gathr enable scheduling of training pipelines on desired frequency for continuously trained machine learning models
6. Automated deployment of models with Single click deployment of models for scoring
7. Trained models can be very easily dropped on the canvas for inference in scoring pipelines for anomaly detection
8. Support for model management, versioning and retraining
9. Drift Detection to track deteriorating model performance and alerting users for appropriate actions
10. Model Snapshotting and Reproducibility for managing history and auditing

Gathr is a structured and thoughtfully created package with a proven track of handling data analytics tactfully with superior quality. The architecture has been designed with patented extensibility allowing users to go beyond and fully leverage the platform to solve all their data use-cases. Developing an efficient package in itself gets you setup and cuts down your cycles to build data applications faster than ever before.