Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. In this post, we discuss how to set up a data ingestion pipeline using Kafka connectors. Fake customer and order data is ingested into Kafka topics using the MSK Data Generator. Also, we use the Confluent S3 sink connector to save the messages of the topics into a S3 bucket. The Kafka Connect servers and individual connectors are deployed using the custom resources of Strimzi on Kubernetes.
Apache Kafka has five core APIs, and we can develop applications to send/read streams of data to/from topics in a Kafka cluster using the producer and consumer APIs. While the main Kafka project maintains only the Java APIs, there are several open source projects that provide the Kafka client APIs in Python. In this post, we discuss how to develop Kafka client applications using the kafka-python package on Kubernetes.
Apache Kafka is one of the key technologies for implementing data streaming architectures. Strimzi provides a way to run an Apache Kafka cluster and related resources on Kubernetes in various deployment configurations. In this series of posts, we will discuss how to create a Kafka cluster, to develop Kafka client applications in Python and to build a data pipeline using Kafka connectors on Kubernetes.
In the previous posts, we discussed how to implement client authentication by TLS (SSL or TLS/SSL) and SASL authentication. One of the key benefits of client authentication is achieving user access control. In this post, we will discuss how to configure Kafka authorization with Java and Python client examples while SASL is kept for client authentication.
In the previous post, we discussed TLS (SSL or TLS/SSL) authentication to improve security. It enforces two-way verification where a client certificate is verified by Kafka brokers. Client authentication can also be enabled by Simple Authentication and Security Layer (SASL), and we will discuss how to implement SASL authentication with Java and Python client examples in this post.
To improve security, we can extend TLS (SSL or TLS/SSL) encryption either by enforcing two-way verification where a client certificate is verified by Kafka brokers (SSL authentication). Or we can choose a separate authentication mechanism, which is typically Simple Authentication and Security Layer (SASL). In this post, we will discuss how to implement SSL authentication with Java and Python client examples while SASL authentication is covered in the next post.
We can configure Kafka clients and other components to use TLS (SSL or TLS/SSL) encryption to secure communication. It is a one-way verification process where a server certificate is verified by a client via SSL Handshake. Moreover we can improve security by adding client authentication. In this post, we will discuss how to configure SSL encryption with Java and Python client examples while client authentication will be covered in later posts.
In Part 4, we developed Kafka producer and consumer applications using the kafka-python package without integrating schema registry. Later we discussed the benefits of schema registry when developing Kafka applications in Part 5. In this post, I'll demonstrate how to enhance the existing applications by integrating AWS Glue Schema Registry.
In Part 3, we developed a data ingestion pipeline using Kafka Connect source and sink connectors without enabling schemas. Later we discussed the benefits of schema registry when developing Kafka applications in Part 5. In this post, I'll demonstrate how to enhance the existing data ingestion pipeline by integrating AWS Glue Schema Registry.
The Glue Schema Registry supports features to manage and enforce schemas on data streaming applications using convenient integrations with Apache Kafka and other AWS managed services. In order to utilise those features, we need to use the client library. In this post, I'll illustrate how to build the client library after introducing how it works to integrate the Glue Schema Registry with Kafka producer and consumer apps.