Based on our record, Amazon SQS seems to be a lot more popular than Spark Streaming. While we know about 65 links to Amazon SQS, we've tracked only 3 mentions of Spark Streaming. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Event Routers: Services like Amazon SQS (A managed message queuing), Amazon SNS (A pub/sub messaging), AWS Step Functions (An orchestrate serverless workflows) and Amazon EventBridge (A serverless event bus) act as event routers, establishing the paths and flow for messages within the architecture. They enable seamless handling and distribution of events, ensuring that each message reaches its intended destination... - Source: dev.to / 28 days ago
SQS - 1 million messaging queue requests. - Source: dev.to / 4 months ago
The last stage is productionizing the model. The goal of this phase is to create a system to process each image/video, gather the relevant features and inputs to the models, integrate the models into a hosting service, and relay the corresponding model predictions to downstream consumers like the MCF system. We used an existing Safety service, Content Classification Service, to implement the aforementioned system... Source: 6 months ago
For context; the web application is built with React and TypeScript which makes calls to an AppSync API that makes use of the Lambda and DynamoDB datasources. We use Step Functions to orchestrate the flow of events for complex processing like purchasing and renewing policies, and we use S3 and SQS to process document workloads. - Source: dev.to / 7 months ago
Amazon SQS is a fully managed message queue service that provides a reliable and scalable solution for asynchronous messaging between distributed components and microservices. - Source: dev.to / 10 months ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago
Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 1 year ago
Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 2 years ago
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Confluent - Confluent offers a real-time data platform built around Apache Kafka.
RabbitMQ - RabbitMQ is an open source message broker software.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Amazon SNS - Fully managed pub/sub messaging for microservices, distributed systems, and serverless applications
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.