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Rick Hightower
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ElasticSearch Consulting
ElasticSearch AWS Consulting / ElasticCache
ELK, Kibana, FileBeats, Beats and ElasticSearch
This ElasticSearch is the #1 full text search solution. ElasticSearch is fault tolerant, highly scalable full text search server.
ELK (ElasticCache, Logstash and Kibana) have become the cornerstone in managing systems with its ability to drill down into OS, systems, services and application logs. It allows you to not fly blind when debugging distributed systems. It is hard to imagine doing any sort of distributed system development (uServices, Cassandra, Kafka, etc.) without an ELK stack to aggregate the logs and make them searchable.
Kibana has become a top analytics visualization tool. This tool is beloved by business analysts, data analysts and data scientists for understanding data.
The ElasticSearch search engine is supported by Amazon WebService via ElasticCache. ElasticSearch and Kibana have become the go-to stack for full text search and analytics. ElasticSearch transcends the role of a database as it is a full text search solution with analytics support.
This provide support to help you deploy the ElasticSearch search engine successfully, and deploy to production.
We provide more than just developer training. We provide the training to maximize your developer and DevOps expertise.
http://cloudurable.com/elk-consulting/index.html
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TICK Stack / InfluxDB Consulting

InfluxDB

Telegraf, InfluxDB, Chronograf, and Kapacitor

This InfluxDB is the #1 time-series database solution. InfluxDB is fault tolerant, highly-scalable time-series database.

TICK (Telegraf, InlfluxDB, Chronograf and Kapacitor) has become one of the most common stacks for monitoring systems. It is a key tool for managing systems with its ability to drill down into OS, systems, services and applications KPIs and metrics. It allows you to not fly blind when debugging distributed systems, and understanding KPIs, and trends. It is hard to imagine doing any sort of distributed system development (uServices, Cassandra, Kafka, etc.) without the monitoring that TICK provides.

Chronograf has become a top visualization tool for time series data. This tool is beloved by system admins, developers and data scientists for understanding time series data.

Kapacitor allows alerting based on trends, and rules for KPIs and metrics.

InfluxDB, Grafana and Chronograf have become the go-to stack for monitoring KPIs and system metrics. InfluxDB transcends the role of a database as it is a time series database solution with analytics support.

We provide support to help you deploy the InfluxDB time series database successfully, and deploy to production.

We provide more than just developer training. We provide the training to maximize your developer and DevOps expertise.
http://cloudurable.com/tick-consulting/index.html
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This comprehensive Kafka tutorial covers Kafka architecture and design. The Kafka tutorial has example Java Kafka producers and Kafka consumers. The Kafka tutorial also covers Avro and Schema Registry.
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This comprehensive Kafka tutorial covers Kafka architecture and design. The Kafka tutorial has example Java Kafka producers and Kafka consumers. The Kafka tutorial also covers Avro and Schema Registry.
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Kafka Tutorial: Kafka, Avro Serialization and the Schema Registry: Confluent Schema Registry stores Avro Schemas for Kafka producers and consumers. The Schema Registry and provides RESTful interface for managing Avro schemas It allows the storage of a history of schemas which are versioned. the Confluent Schema Registry supports checking schema compatibility for Kafka. You can configure compatibility setting which supports the evolution of schemas using Avro. Kafka Avro serialization project provides serializers. Kafka Producers and Consumers that use Kafka Avro serialization handle schema management and serialization of records using Avro and the Schema Registry. When using the Confluent Schema Registry, Producers don’t have to send schema just the schema id which is unique. The consumer uses the schema id to look up the full schema from the Confluent Schema Registry if not already cached. Since you don’t have to send the schema with each set of records, this saves time. Not sending the schema with each record or batch of records, speeds up the serialization as only the id of the schema is sent.

If you have never used Avro before, please read Avro Introduction for Big Data and Data Streams.

This article is going to cover what is the Schema Registry and cover why you want to use it with Kafka. We drill down into understanding Avro schema evolution and setting up and using Schema Registry with Kafka Avro Serializers. We show how to manage Avro Schemas with REST interface of the Schema Registry and then how to write Avro Serializer based Producers and Avro Deserializer based Consumers for Kafka.

The Kafka Producer creates a record/message, which is an Avro record. The record contains a schema id and data. With Kafka Avro Serializer, the schema is registered if needed and then it serializes the data and schema id. The Kafka Avro Serializer keeps a cache of registered schemas from Schema Registry their schema ids.

Consumers receive payloads and deserialize them with Kafka Avro Deserializers which use the Confluent Schema Registry. Deserializer looks up the full schema from cache or Schema Registry based on id.
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Kafka Tutorial: Kafka, Avro Serialization and the Schema Registry: Confluent Schema Registry stores Avro Schemas for Kafka producers and consumers. The Schema Registry and provides RESTful interface for managing Avro schemas It allows the storage of a history of schemas which are versioned. the Confluent Schema Registry supports checking schema compatibility for Kafka. You can configure compatibility setting which supports the evolution of schemas using Avro. Kafka Avro serialization project provides serializers. Kafka Producers and Consumers that use Kafka Avro serialization handle schema management and serialization of records using Avro and the Schema Registry. When using the Confluent Schema Registry, Producers don’t have to send schema just the schema id which is unique. The consumer uses the schema id to look up the full schema from the Confluent Schema Registry if not already cached. Since you don’t have to send the schema with each set of records, this saves time. Not sending the schema with each record or batch of records, speeds up the serialization as only the id of the schema is sent.

If you have never used Avro before, please read Avro Introduction for Big Data and Data Streams.

This article is going to cover what is the Schema Registry and cover why you want to use it with Kafka. We drill down into understanding Avro schema evolution and setting up and using Schema Registry with Kafka Avro Serializers. We show how to manage Avro Schemas with REST interface of the Schema Registry and then how to write Avro Serializer based Producers and Avro Deserializer based Consumers for Kafka.

The Kafka Producer creates a record/message, which is an Avro record. The record contains a schema id and data. With Kafka Avro Serializer, the schema is registered if needed and then it serializes the data and schema id. The Kafka Avro Serializer keeps a cache of registered schemas from Schema Registry their schema ids.

Consumers receive payloads and deserialize them with Kafka Avro Deserializers which use the Confluent Schema Registry. Deserializer looks up the full schema from cache or Schema Registry based on id.
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Avro Introduction for Big Data and Data Streaming Architectures: Apache Avro™ is a data serialization system. Avro provides data structures, binary data format, container file format to store persistent data, and provides RPC capabilities. Avro does not require code generation to use and integrates well with JavaScript, Python, Ruby, C, C#, C++ and Java. Avro gets used in the Hadoop ecosystem as well as by Kafka.

Avro is similar to Thrift, Protocol Buffers, JSON, etc. Avro does not require code generation. Avro needs less encoding as part of the data since it stores names and types in the schema reducing duplication. Avro supports the evolution of schemas.
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Avro Introduction for Big Data and Data Streaming Architectures: Apache Avro™ is a data serialization system. Avro provides data structures, binary data format, container file format to store persistent data, and provides RPC capabilities. Avro does not require code generation to use and integrates well with JavaScript, Python, Ruby, C, C#, C++ and Java. Avro gets used in the Hadoop ecosystem as well as by Kafka.

Avro is similar to Thrift, Protocol Buffers, JSON, etc. Avro does not require code generation. Avro needs less encoding as part of the data since it stores names and types in the schema reducing duplication. Avro supports the evolution of schemas.
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