This document discusses and compares Neptune and JanusGraph graph databases. It provides an overview of Neptune's features like multi-AZ deployment and storage in S3. It also describes how to access Neptune using Gremlin and SPARQL query languages. The document then introduces JanusGraph and notes some key differences when using Gremlin APIs with Neptune versus JanusGraph. It shares the results of a performance test loading Amazon product graph data into both systems. Finally, it discusses options for loading and querying data between Neptune, Athena, Kinesis and other AWS services.
This document discusses messaging queues and platforms. It begins with an introduction to messaging queues and their core components. It then provides a table comparing 8 popular open source messaging platforms: Apache Kafka, ActiveMQ, RabbitMQ, NATS, NSQ, Redis, ZeroMQ, and Nanomsg. The document discusses using Apache Kafka for streaming and integration with Google Pub/Sub, Dataflow, and BigQuery. It also covers benchmark testing of these platforms, comparing throughput and latency. Finally, it emphasizes that messaging queues can help applications by allowing producers and consumers to communicate asynchronously.
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.
This document discusses and compares Neptune and JanusGraph graph databases. It provides an overview of Neptune's features like multi-AZ deployment and storage in S3. It also describes how to access Neptune using Gremlin and SPARQL query languages. The document then introduces JanusGraph and notes some key differences when using Gremlin APIs with Neptune versus JanusGraph. It shares the results of a performance test loading Amazon product graph data into both systems. Finally, it discusses options for loading and querying data between Neptune, Athena, Kinesis and other AWS services.
This document discusses messaging queues and platforms. It begins with an introduction to messaging queues and their core components. It then provides a table comparing 8 popular open source messaging platforms: Apache Kafka, ActiveMQ, RabbitMQ, NATS, NSQ, Redis, ZeroMQ, and Nanomsg. The document discusses using Apache Kafka for streaming and integration with Google Pub/Sub, Dataflow, and BigQuery. It also covers benchmark testing of these platforms, comparing throughput and latency. Finally, it emphasizes that messaging queues can help applications by allowing producers and consumers to communicate asynchronously.
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.