Category Archives: Kafka

Robust Message Serialization in Apache Kafka Using Apache Avro, Part 3

Categories: Avro CDH How-to Kafka

Part 3: Configuring Clients

Earlier, we introduced Kafka Serializers and Deserializers that are capable of writing and reading Kafka records in Avro format. In this part we will going to see how to configure producers and consumers to use them.

Setting up a Kafka Topic for use as a Schema Store

KafkaTopicSchemaProvider works with a Kafka topic as its persistent store. This topic will contain at most thousands of records: the schemas. It does not need multiple partitions,

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Robust Message Serialization in Apache Kafka Using Apache Avro, Part 2

Categories: Avro CDH How-to Kafka

Implementing a Schema Store

In Part 1, we saw the need for an Apache Avro schema provider but did not implement one. In this part we will implement a schema provider that works with Apache Kafka as storage.

In-Memory SchemaStore

First we can implement an in-memory store for schemas. This is useful to understand the requirements for such a store and as the cache of the Kafka backed store. A SchemaStore has to be quick in looking up VersionedSchema entries.

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Robust Message Serialization in Apache Kafka Using Apache Avro, Part 1

Categories: Avro CDH How-to Kafka

In Apache Kafka, Java applications called producers write structured messages to a Kafka cluster (made up of brokers). Similarly, Java applications called consumers read these messages from the same cluster.  In some organizations, there are different groups in charge of writing and managing the producers and consumers. In such cases, one major pain point can be in the coordination of the agreed upon message format between producers and consumers.

This example demonstrates how to use Apache Avro to serialize records that are produced to Apache Kafka while allowing evolution of schemas and nonsynchronous update of producer and consumer applications.

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Scalability of Kafka Messaging using Consumer Groups

Categories: Data Ingestion Flume Kafka Use Case

Traditional messaging models fall into two categories: Shared Message Queues and Publish-Subscribe models. Both models have their own pros and cons. Neither could successfully handle big data ingestion at scale due to limitations in their design. Apache Kafka implements a publish-subscribe messaging model which provides fault tolerance, scalability to handle large volumes of streaming data for real-time analytics. It was developed at LinkedIn in 2010 to meet its growing data pipeline needs. Apache Kafka bridges the gaps that traditional messaging models failed to achieve.

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Cloudera Enterprise 5.12 is Now Available

Categories: Altus CDH Cloud Cloudera Manager Cloudera Navigator Data Science Hue Impala Kafka Kudu

Cloudera is pleased to announce that Cloudera Enterprise 5.12 is now generally available (GA). The release includes enhancements for running in cloud environments (with broader ADLS support and improved AWS Spot Instance support), usability and productivity improvements for both data science and analytic workloads, as well as performance gains and self-service performance management across a range of workloads.

As usual, there are also a number of quality enhancements, bug fixes, and other improvements across the stack.

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