In our opinion, Apache Kafka has a great potential to take place of traditional relational database system (RDBMS) technology which as been very successful so far. But we see complexity of creating and deploying and developing on platform as one major roadblock to its democratization and hence its restricted in practice to very savvy and big companies.
We intent do change that by providing powerful yet intuitive UI tools that integrates best practices of the field, leverages Generative AI to assist in human efforts, using modern infrastructure deployment for our user to operate at higher level of abstraction to create more sophisticated applications.
In the end, we intend to deliver even better UI and cohesive experience you may get from a leading SaaS offering while you owing every component allows you to customize for your needs and at lower cost of ownership.
Schema Designer is the tool part of Aura I9s tool kit for managing Kafka message metadata.
It shows available data entity in schema registry with our unique approach to group them by namespace and map them with topic.
In one web page, you have access to see all schemas and intuitively manage them. You can do following actions,
and many more.
Connector Central of Aura I9s tool kit interacts with Kafka Connect for managing your data flow.
It shows all available connectors group nicely by topics and based on connector type. This single page application is designed for complex environment where all kind of data is flowing from/to Kafka from multiple heterogeneous source and targets. Unlike other data products, its flexible to let user use any connectors they wish and hence providing grate flexibility while let you operate on multiple pipelines at same time.
In nutshell, you can manage, monitor and inspect data of hundreds of connectors that may be fetching and pushing data of your data pipelines.
As of Aug2023, our Kafka connect image comes with following open source connectors that we can distribute without licensing concern.
There are many more connectors free to use connectors which one can use but we can not distribute it directly due to licensing restrictions.
Yes You can install and configure any Open or Licensed connectors with installed Kafka Connect image and use it in our Kubernetes Cluster.
Aura I9S suite is designed to work with all Apache Kafka Deployment and be agnostic about the Kafka stack provider.
Our team has quite a bit knowledge of create custom connectors using opensource connectors and connector framework. we can provide support for such opensource connectors for which code is public or the connector we have developed our self.
We provide per-engineered Kafka stack to lower the complexity for companies who are starting fresh in the field.
Our UI tools are designed to connect to any standard deployment of Apache Kafka that may be in your on-prem setup or in your cloud or a SaaS offering such are Confluent Cloud.
Yes we do support AWS and GCP deployments. However, they are not yet available in Marketplace like our offering in Microsoft Azure Marketplace. Please reach out our sales team for the details.
We can deploy solution on any Docker environment or on Kubernetes.
Kubernetes is proffered way of setup as it guaranties it allows Horizontal scaling of resources and High Availability with ease.
Yes, we support Red Panda as underlying broker technology instead of Kafka.
Link to documentation site is available inside our product on upper right corner.
You can reach out to our technical support team at support@aurainnovations.ai. Our support team is based in North America. Though we don't provide 24x7 support for non critical issues, we intend to provide quality support from our experts who works on product day to day (DevOps philosophy).
Kafka is an open-source distributed streaming platform that was originally developed by LinkedIn and later donated to the Apache Software Foundation. It is designed to handle high volume, real-time data feeds in a distributed computing environment.
Kafka allows producers to publish messages to topics, which are then consumed by consumers. It uses a publish-subscribe model where producers write data to topics, and consumers subscribe to those topics to receive the data. Kafka provides a highly scalable and fault-tolerant architecture that can handle a large volume of data and is widely used in big data processing, real-time analytics, and data streaming applications.
Some of the key features of Kafka include:
Distributed architecture: Kafka can be run on a cluster of multiple machines, allowing for horizontal scaling.
High throughput: Kafka is capable of handling millions of messages per second.
Low latency: Kafka provides real-time data processing capabilities with low latency.
Fault tolerance: Kafka is designed to be highly resilient to failures and provides automatic recovery in case of failures.
Persistence: Kafka stores all messages on disk, providing durable and fault-tolerant storage of data.
Stream processing: Kafka can be integrated with popular stream processing frameworks like Apache Spark, Apache Storm, and Apache Flink.
A schema is a description or definition of the structure of data. It provides a blueprint or template for how data should be organized and the types of data that are allowed. Unlike relational database Kafka separates data from structure with concept of storing schema separately.
Generally, each message contains schema ID that provides structure for the data sent in message. This way Kafka allows great flexibility to evolve message data structure.
In the context of messaging systems, a topic is a named channel or category to which messages are published by producers and from which messages are consumed by subscribers. Topics provide a way to organize messages and allow subscribers to selectively receive only the messages they are interested in.
Kafka engine stores and accesses data sequentially by Topic (and partitions) to achieve a very high performance.
Apache Kafka is designed from the ground up to handle high-volume, high-throughput data streaming and processing. It achieves scalability and performance through a combination of architectural principles, distributed design, and optimization techniques. Here's how Kafka scales to meet the performance requirements of high-volume data:
Distributed Architecture: Kafka is built as a distributed system, where data is partitioned and distributed across multiple brokers (nodes). This allows Kafka to parallelize data processing and storage, enabling linear scalability as more brokers are added to the cluster.
Partitioning: Kafka topics are divided into partitions, and each partition can be hosted on a different broker. This partitioning allows Kafka to spread the load across multiple nodes and enables parallel processing of messages within a topic.
Replication: Kafka supports data replication for fault tolerance and durability. Each partition has multiple replicas distributed across different brokers. This redundancy ensures that data is not lost even if a broker fails.
Producer Parallelism: Producers can write to different partitions of a topic concurrently, enabling high write throughput.
Consumer Parallelism: Consumers can read from different partitions of a topic in parallel, allowing for high read throughput. Additionally, Kafka consumers can be grouped to work together as consumer groups, further enhancing scalability and distribution of data processing.
Hardware Scaling: Kafka clusters can be horizontally scaled by adding more brokers and hardware resources, such as CPUs, memory, and storage. This allows Kafka to handle increasing data volumes by distributing the load across more machines.
Batching: Kafka supports message batching, where multiple messages are sent together in a single batch. This reduces network overhead and improves throughput by reducing the number of individual network requests.
Compression: Kafka provides built-in message compression options, which reduce the amount of data transmitted over the network and stored on disk, improving overall throughput and storage efficiency.
Tuning and Optimization: Kafka offers various configuration options for tuning the system to match the specific workload and hardware characteristics. This includes settings for message sizes, buffer sizes, disk and network I/O, and more.
Connectors and Processing Frameworks: Kafka Connect and Kafka Streams allow you to offload data processing from the core Kafka brokers to dedicated connectors and applications, further distributing the workload and optimizing performance.
Monitoring and Scaling: Kafka provides extensive monitoring capabilities, allowing administrators to track cluster health, performance metrics, and bottlenecks. This data helps in making informed decisions about scaling and optimization.
Caching: Kafka employs various caching mechanisms, like page cache and log segment cache, to improve read and write performance by reducing disk I/O.
By leveraging these architectural and design principles, Apache Kafka is capable of scaling horizontally to meet the demands of high-volume data streaming and processing, making it a popular choice for building robust and scalable data pipelines.
Schema Registry is a centralized service that stores and manages schemas used by producers and consumers in a Kafka messaging system. It provides a way to ensure that all messages sent to Kafka are serialized using a specific schema, and that all consumers can deserialize those messages using the same schema.
In a Kafka messaging system, producers and consumers exchange messages that are serialized using a specific data format such as Avro or JSON. The schema for this data format defines the structure and data types used in the messages. Schema Registry provides a way to register and manage these schemas centrally, so that all producers and consumers in the system can access the correct schema for each message.
When a producer sends a message to Kafka, it first checks the Schema Registry to retrieve the schema for the message's data format. It then uses this schema to serialize the message into the appropriate format before sending it to Kafka. When a consumer receives a message from Kafka, it also checks the Schema Registry to retrieve the schema for the message's data format. It then uses this schema to deserialize the message into the appropriate data structure for processing.
Schema Registry helps ensure that all messages sent to Kafka are serialized and deserialized using the same schema, which helps prevent data compatibility issues between producers and consumers. It also provides versioning and compatibility checking features, which allow producers and consumers to evolve their schemas over time while maintaining backward compatibility with existing messages.
The use of a Schema Registry has several benefits:
Compatibility and Evolution: Producers and consumers can evolve independently while maintaining compatibility. Consumers can handle data produced with older schemas, and producers can publish data with new schemas.
Data Validation: The Schema Registry can enforce validation rules on the data to ensure that it conforms to the registered schema, reducing data quality issues.
Versioning: The ability to manage multiple versions of schemas for different subjects allows for smooth transitions when data formats change.
Centralized Management: Schemas are managed centrally, making it easier to track changes and ensure consistency.
Schema Evolution: Schemas can be evolved over time, adding or modifying fields, while maintaining compatibility with existing consumers.
Apache Kafka is a popular distributed streaming platform that is widely used for building real-time data pipelines and streaming applications. Kafka Connect is a component of the Apache Kafka ecosystem that simplifies the process of integrating Kafka with external data sources and sinks (destinations).
Kafka Connect is designed to address the challenges of data integration by providing a scalable and reliable framework for connecting Kafka topics to various data storage systems, databases, and other data processing tools. It allows you to move data in and out of Kafka topics without writing custom code for each integration scenario.
Key features of Kafka Connect include:
Source Connectors: These connectors allow you to ingest data from external systems into Kafka topics. For example, you can use source connectors to pull data from databases, files, messaging systems, and other sources and publish that data to Kafka topics.
Sink Connectors: Sink connectors move data from Kafka topics into external systems. This is useful for scenarios where you want to persist data from Kafka to databases, data warehouses, or other storage solutions.
Scalability: Kafka Connect is designed to scale horizontally, allowing you to handle large volumes of data and ensure high throughput.
Fault Tolerance: Kafka Connect provides fault tolerance by distributing tasks across a cluster of worker nodes. If a worker node fails, the tasks are automatically rebalanced to other nodes.
Schema Registry Integration: Kafka Connect can work seamlessly with a schema registry, ensuring that the data being moved between systems is properly serialized and deserialized based on the registered schemas.
Connector Management: Kafka Connect includes a REST API and a web-based UI that make it easy to configure, deploy, and manage connectors.
Connect Transformation: You can apply transformations to the data as it flows through connectors. Transformations can include filtering, mapping, enrichment, and more.
Kafka Connect simplifies the process of building and maintaining data pipelines by abstracting many of the complexities associated with data integration. It reduces the need for custom coding and accelerates the development of data streaming solutions.
Kafka Connect includes a set of built-in connectors for popular data sources and sinks, and you can also create custom connectors to integrate with your specific data systems. This extensibility and flexibility make Kafka Connect a powerful tool for building real-time data pipelines that connect diverse data sources and destinations to Kafka topics.
Kafka Connect is a versatile tool that can be used in a wide range of data integration scenarios. Its primary purpose is to simplify the process of moving data in and out of Apache Kafka topics, and it is particularly well-suited for real-time streaming data pipelines. Here are some typical use cases for Kafka Connect:
Database Integration: Kafka Connect can be used to capture changes from databases (CDC - Change Data Capture) and publish them to Kafka topics. This enables real-time data replication, data warehousing, analytics, and data synchronization.
Log Aggregation: Kafka Connect can collect logs from various applications, servers, and services and route them to Kafka for centralized log processing, analysis, and monitoring.
Event Sourcing: Kafka Connect can be used to capture events generated by different components in a system and store them in Kafka, forming the basis for event sourcing architectures.
Streaming ETL (Extract, Transform, Load): Kafka Connect can transform and enrich data in real time before moving it from source systems to data warehouses, data lakes, or other destinations.
Data Ingestion: It can ingest data from various sources, including files (e.g., CSV, JSON, Avro), messaging systems (e.g., JMS, RabbitMQ), and IoT devices, and publish them to Kafka topics for further processing.
Data Replication: Kafka Connect can replicate data between Kafka clusters, data centers, or regions, providing data redundancy and disaster recovery capabilities.
Streaming Analytics: Kafka Connect can feed real-time data streams into analytics and machine learning platforms for instant insights and decision-making.
Micro services Communication: Kafka Connect can enable communication between micro services by facilitating the exchange of events and data.
Integration with External Services: Kafka Connect can interact with various external services, such as cloud platforms, APIs, and web hooks, to ingest or deliver data.
Data Archiving and Backup: Kafka Connect can archive data from Kafka topics to other storage systems for long-term retention and compliance purposes.
Real-time Monitoring and Alerts: Kafka Connect can gather data from monitoring systems and sensors and stream it to Kafka for real-time monitoring and alerting.
Internet of Things (IoT): Kafka Connect can ingest data from IoT devices and sensors, allowing real-time analysis and insights from streaming sensor data.
These are just a few examples of how Kafka Connect can be utilized. Its flexibility and scalability make it suitable for a wide variety of integration scenarios, enabling organizations to build robust and efficient data pipelines that facilitate real-time data movement, transformation, and analysis.
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