USE CASES

Where ClaraStream can change the data path.

ClaraStream can be used anywhere there is value in preparing data earlier, avoiding repeated downstream processing, keeping prepared data moving, or making the same data available for more than one use.

The examples on this page show some of the ways ClaraStream can be applied. They are not limits on how the architecture can be used.

REPRESENTATIVE USES

Start with the data problem, not a predefined use case.

ClaraStream is infrastructure for receiving, preparing, and moving data. Where it fits depends on the data path, the environment, and what needs to happen before and after the data is prepared.

The examples below illustrate different ways that architecture can be applied. They are not a complete list of ClaraStream use cases or a statement of current input compatibility. Product selection depends on where preparation and distribution need to occur, not on the use-case category itself.

For current product, input, output, and integration availability, refer to the individual ClaraStream product pages.

EXPLORE THE CLARASTREAM PRODUCTS

Product selection depends on where preparation and distribution need to occur, not on the use-case category itself.

Core
Software-based data preparation
Edge
Software for programmable infrastructure
COMING SOON
Stream
Continuous streaming and distribution
Flow
Core + Stream, delivered together

HOSTING AND COMPUTE PROVIDERS

Create more value from the infrastructure you already operate.

Hosting and compute providers sit close to where customer data is being generated. ClaraStream can add a preparation and movement layer within that infrastructure, creating new ways to prepare data earlier and make it available for downstream use.

Prepare data before it moves farther downstream.

Situation: A customer runs workloads in a provider’s infrastructure, while several systems outside that immediate environment need data generated by those workloads.
With ClaraStream: Core can be deployed within the customer’s server environment to receive and transform the data before it moves farther through the architecture.
What changes: Preparation happens closer to where the data is generated instead of being left entirely to systems farther downstream.

Give several customer systems a shared prepared-data path.

Situation: The same underlying data is needed by an analytics platform, an application, and a storage platform.
With ClaraStream: The data can be prepared once and made continuously available through Stream for those separate downstream uses.
What changes: Each destination no longer needs its own independent preparation path back to the original source.

Add a differentiated infrastructure service.

Situation: A hosting or compute provider wants to offer more than compute capacity by helping customers prepare and move data inside the environment.
With ClaraStream: The provider can incorporate ClaraStream into a defined customer deployment as a data preparation and movement layer.
What changes: Data preparation and continuous distribution can become part of the infrastructure service rather than something every customer must assemble independently.

Start with a focused deployment. Expand as customer requirements develop.

SELF-MANAGED AND HYBRID INFRASTRUCTURE

Stop building a new data path for every new use.

Organizations operating their own servers, private infrastructure, or hybrid environments often have multiple systems that need access to the same underlying data.

Create a shared preparation layer.

Situation: An analytics platform, an internal application, and a storage platform all need the same underlying data, and each has its own parsing or preprocessing logic.
With ClaraStream: Core can receive the data and transform it earlier in the path into prepared data.
What changes: The same preparation work does not have to be recreated separately inside each downstream system.

Add a new application without starting over upstream.

Situation: An organization already uses prepared data for analytics and storage. Later, another application needs access to the same data.
With ClaraStream: The new application can consume the existing prepared stream rather than requiring another collection and transformation path from the source.
What changes: Adding another use does not automatically mean adding another upstream data path.

Serve different purposes at the same time.

Situation: Operations needs current data, an application uses it in an automated workflow, an AI agent needs access to new information, and a data platform needs to retain it.
With ClaraStream: Those downstream uses can consume the same prepared stream concurrently.
What changes: The downstream systems remain independent while sharing the same upstream preparation and distribution path.

Adapt the data for a consumer that needs a different format.

Situation: A ClaraStream deployment uses Apache Arrow as its native prepared-data format, but one downstream application requires JSON.
With ClaraStream: The included JSON output can provide JSON for a downstream application while Core continues to produce prepared data in Apache Arrow.
What changes: One application’s format requirement does not require changing the native ClaraStream data path for every other consumer.

DISTRIBUTED OPERATIONS

Prepare data closer to where the work is happening.

Factories, warehouses, branch locations, retail environments, remote facilities, and other distributed operations generate data away from centralized platforms.

Prepare data before sending it across the organization.

Situation: Data is generated at several remote or regional locations, but centralized applications and analytics platforms need to use it.
With ClaraStream: Core can be deployed on server infrastructure closer to those locations so data is prepared before it moves farther through the organization.
What changes: Preparation moves closer to where the data originates instead of relying entirely on centralized downstream systems.

Avoid several central systems reaching back to the same location.

Situation: A distributed site produces data needed by an operational application, analytics platform, and storage system at a central location.
With ClaraStream: The data can follow one preparation path before being made available to those different uses.
What changes: The site does not need a separate upstream data path for every central system that wants the same underlying data.

Move preparation closer to data entry with acceleration.

COMING SOON

Situation: An infrastructure environment needs Listen + Transform to occur closer to where a high volume of data enters the system.
With ClaraStream Edge: Edge is being developed to perform Listen + Transform on programmable infrastructure closer to the data source. The initial implementation is being developed for the Napatech F2070X DPU.
What changes: Data preparation can move from a conventional server environment into an accelerated environment closer to the point of data entry.

Let different teams use the prepared result differently.

Situation: The same distributed data is useful for day-to-day operations, centralized analysis, an application workflow, and longer-term retention.
With ClaraStream: Those systems can use the prepared data for different purposes without owning the upstream preparation path.
What changes: The data path is shared. The downstream uses are not.

GOVERNMENT AND PUBLIC SECTOR

Move usable data across distributed and mission-focused infrastructure.

Government and public-sector environments often combine owned infrastructure, distributed systems, remote locations, and applications developed for different purposes.

Prepare the same data once for several applications.

Situation: Multiple applications need the same underlying data, but each application has developed its own way of collecting and preparing it.
With ClaraStream: A ClaraStream preparation layer can receive and transform the data before those applications consume it.
What changes: Preparation becomes shared infrastructure rather than duplicated application-by-application work.

Support current use and historical retention from the same data path.

Situation: One system needs current data for an active workflow while another platform needs to retain the same data for later analysis.
With ClaraStream: Both can consume the prepared data according to their own requirements.
What changes: Current use and long-term storage do not require separate preparation paths back to the original data source.

Add another mission application later.

Situation: An existing deployment already makes prepared data available to several systems. A new application is introduced later and needs the same information.
With ClaraStream: The new application can consume the existing prepared data rather than automatically requiring a new path from the source.
What changes: The architecture can accommodate another downstream use without recreating the upstream work.

Put preparation where the infrastructure requires it.

Situation: Some parts of the environment use conventional server infrastructure, while another part may benefit from data preparation on programmable infrastructure closer to the source.
With ClaraStream: Core provides data preparation on conventional server infrastructure. Edge is software designed to perform the same preparation role on programmable infrastructure closer to the source and is currently in active development.
What changes: The preparation function can be placed according to the infrastructure requirement rather than forcing every environment into the same deployment model.

NOT JUST THESE EXAMPLES

The architecture is reusable. The use is up to you.

The examples above show some of the environments and data problems where ClaraStream can change the path. They do not define the only ways ClaraStream can be used.

If there is value in receiving data earlier, transforming it before repeated downstream processing, changing where preparation occurs, moving prepared data continuously, or making the same prepared data available for additional uses, ClaraStream may have a role in that architecture.

Prepare once. Use many.

FROM THE FIELD

How have you used ClaraStream to solve a data problem?

Found a useful, unexpected, or particularly effective way to use ClaraStream? Tell us what you were trying to solve, where ClaraStream fit into the architecture, and what changed.

Your experience may help other ClaraStream users think differently about their own data paths.

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