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In this How To article I demonstrate setting up a Docker Compose based implementation of the Community Components of the Confluent Platform complete with the kafka-connect-datagen plugin for Kafka Connect to generate test and/or developement data useful for working with Kafka.
Here I present an end-to-end example of a Serverless event driven architecture using Confluent Cloud for stream processing paired with AWS Lambda for event responsive logic using the Serverless Application Model (SAM) framework. Together this architecture will compose a system for fictitious financial stock quote email alerting.
In this How To article I demonstrate how to use the AWS CLI to create an Amazon Elastic Map Reduce (EMR) Cluster along with some common supplementary resources for experimentation and development on an EMR cluster.
In this article I review key characteristics and functionality of Apache Hive and how you can utilize Amazon Elastic MapReduce (EMR) to provision a Apache Hive Cluster for experimentation and big data processing and analytics.
PyFlink is the Python API for Apache Flink which allows you to develop batch and stream data processing pipelines on modern distributed computing architectures.
In this article I go over how to use Apache Flink Table API in Python to consume data from and write data to a Confluent Community Platform Apache Kafka Cluster running locally in Docker.
In this article I show how to implement a data pipelining solution utilizing Dockerized Kafka Connect and the Snowflake Sink Connector to pull events out of Apache Kafka and into the Snowflake Cloud Data Warehouse.
Schemas and schema registries are the governance tools that ensure vitality of data communications through configurable compatibility guardrails as patterns and rules to evolve schemas in a controlled fashion. In this article I will demonstrate how Confluent Schema Registry can be used in conjunction with JSON Schema to govern and enforce rules for schema evolution in the default backwards compatibility mode.
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