What is Condense?
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Introduction
Condense is a managed application offered by Zeliot. It enables ingestion of real-time data directly from the source at scale, allows users to write rules, build ML models and run the models in real-time on the streaming data and offers prebuilt sink connectors to connect the data to the downstream applications. This enables the users to build more complex AI applications over the refined, inferred and governed data. It provides a no-code low-code user interface to ease the development and monitoring. This is availed to users in their cloud environment through the cloud marketplace, which is a first of its kind.
How can Condense be truly verticalized for each domain?
Condense works on the data principles of Ingestion, Transformations and Destination.
Ingestion enables data to be bought into the cloud platform from various sources, mainly real-time sources like edge devices, APIs, event streams etc into a common playground. At this layer Condense offers pre-configured modules to ingest the data directly, these modules are today more relevant to the mobility domain and the idea is to extend it to other domains in the future.
Transformations layer of the platform is very the data gets processed as per the requirements. Each rule on how the data should be processed can be written/configured by the user. Condense here also offers pre-built transformations that are again relevant to mobility. These transformations range from a basic overspeeding algorithm to complex data models for predictive maintenance, vehicle classification on health, geofence computation and many others. The goal is to offer a robust transformation layer so that users will have the ability to parse the data, build the model and create inferencing over real-time data.
Destinations help us to deliver the data for the downstream applications. Condense offers pre-integrated sink connectors like databases, message queues, APIs, etc.
In summary, the transformations layer allows Condense to truly be verticalized for each domain such as Mobility, Healthcare, Media and Streaming, Telecom, and others. The idea is to offer a verticalized platform that handholds customers ~40% more than the traditional horizontal data platforms.
Data collection from real-time sources to AI&ML in real-time
The emergence of cloud computing started the day since Amazon decided to rent out the private cloud to other enterprises on a pay-as-you-go model, since then various technologies and services have accelerated the process of data analytics. The emergence of Databricks, Snowflakes and many other cloud services has only strengthened the domain. But most of these services as of today are operating on batch data, the data in its true sense is not in real-time.
Kafka as an open-source technology has laid a very strong foundation for true real-time data applications. The industry as we are experiencing is moving towards a future which is 100% real-time. So, for the users to be able to build real-time applications, aggregating the data from varied sources like IoT, vehicles, APIs, etc with the least latency is the first step. Once the data is available, a framework to build new-age AI & ML-based applications is the second step. Zeliot’s Condense aims to fill this gap, Condense can collect the real-time data directly from the source efficiently at a very high scale, process it, and then provide a library of pre-built transformations and a framework to run their own models all in one place.
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