Stambia Component for Apache Kafka

Apache Kafka is a distributed data streaming platform that moves data from various sources (producers) to various targets (consumers).

Kafka is widely used to build real-time data streaming pipelines and applications.

Stambia Component for Apache Kafka, provides the ability to build your data pipelines, using a simple GUI to bring agility and productivity in your implementation..


Data Integration solution Key Factors?

Manage Kafka without writing code

Apache Kafka is widely adopted, as a data streaming platform, to cater to various data movements between various systems and applications.

Building these real-time data streaming pipelines and applications requires creating and managing complex codes, by the IT teams.

With an effective data integration solution, this effort of writing and managing manual code can be reduced significantly.

Thereby, bringing agility and flexibility in your projects, where users focus on what to do, rather than how to do.

Manage Apache Kafka without writing code

Seamlessly work with Schema Registry

Seamlessly work with Schema Registry

One of the important challenges, in handling evolving data schemas in Kafka, is addressed by Schema Registry.

This sits outside of Apache Kafka and helps in providing flexibility to interact and reduces the operational complexity.

With this, comes the task to also manage these schema registries.

When adopting an integration solution for Kafka, the ability to work seamlessly with Schema Registry to automate efforts, should be one of the key considerations.


Ability to work consistently with different formats

When working with Kafka, there are many data formats in which data gets moved from the source to sink. With the Schema Registry, developers need to work with AVRO, JSON and so on.

An Integration solution, when used, should automate and simplify managing various formats to reduce any human error, due to manual efforts..

 work consistently with different formats such as AVRO or JSON

Benefit from a Unified solution to connect to various technologies

Apache Kafka with a Unified solution to connect to various technologies

In any data project, in your Information System, there will be various other use cases to cater to.

Managing data exchanges using Kafka, could possibly be one of those use cases.

With different kinds of technologies, platforms, databases, file formats, having a Data Integration solution that is Unified, sits at the center of the entire technological landscape, and caters to all sorts of data requirements, makes a lot of things easier for the data engineers.

Ability to work consistently


Stambia mapping and template with Apache Kafka

Stambia component for Apache Kafka allows users to easily manage data movements between sources and targets with simple drag and drop.

The solution is completely templatized to provide agility and flexibility in your projects.

Some of the key feature of this component is the ability to seamlessly manage:


Schema Registry

Metadata for Kafka in Stambia can be configured to connect to Confluent Schema Registry with SSL encryption to secure Kafka cluster.

This Schema then are created as a JSON / Avro Metadata separately.

Stambia Templates for Kafka uses this information to automatically manage the Schema creation and updates during executions.

 Stambia Templates for Kafka : manage the Schema creation and updates during executions


Kafka Topics can be configured in the metadata with Stambia

Topics can be configured in the metadata to have key, value created, where the schema as a JSON / Avro can be created, just with a drag n drop of the respective metadata of the formats.

This way the entire process of managing the evolving schema becomes metadata-driven and reduces complexity.



Similarly, Consumers can be configured to point to a specific topic. This also allows to provide a reject topic where the rejected / erroneous records can be moved without causing a failure.

Kafka Consumers can be configured to point to a specific topic with Stambia
Kafka consumer metadata with Stambia ELT ETL Mapping
In the consumer metadata, mentioning the topic it is subscribed to, provides the schema from the topic itself. Hence when you drag and drop the consumer, you get to see the structure, and further can be mapped to the intended target.

Simplicity and Agility with no need to write manual code

 Apache Kafka component for Stambia with different mappings to Produce and Consume data faster

With the Kafka Component, users can quickly configure the metadata and get going with different mappings to Produce and Consume data wherever required.

The dedicated templates do the heavy lifting of generating the code, as well, managing the Schema Registry.

As a result, the focus is on what to do than how to..


Stambia, A Unified Solution for data projects

Stambia being a Unified solution can cater to various needs of connecting to different technologies and integrating to different architecture.

Apart from managing Kafka, users can work with various source and target databases, applications, big data technologies, data frameworks like Apache Spark and so on.

This results in creating a consistent integration layer and increases productivity of the team.

 Apache Kafka component for Stambia. Withe a Unified Solution for all your real time data projects

Technical specifications and prerequisites




Structured and Sem-Structured


Languages Supported



You can extract data from :

• Any relational database system such as Oracle, PostgreSQL, MSSQL, ...
• Any NoSQL database system such as MongoDB, Elastic, ...
• Any Cloud system such as Amazon Web Service (AWS), Google Cloud Platform (GCP), Microsoft Azure, ...
• Any ERP applications such as SAP, Microsoft Dynamics, ...
• Any SAAS applications such as Salesforce, ...

For more information, consult the technical documentation

Schema Registry

Confluent Schema Registry

Data loading performances

Performances are improved when loading or extracting data through a bunch of options on the connectors allowing to customize how data is processed such as choosing a Spark jdbc load, or specifc data loaders from the database.
Stambia Runtime Version Stambia DI Runtime S17.4.6 or higher

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