Google Cloud Pub/Sub
Apache Kafka
RabbitMQ
Amazon Kinesis
PieSync
Apache Flink
Confluent
Azure Logic Apps
Spark Streaming
Confluent
Amazon Kinesis
Google Cloud Dataflow
Apache Flink
Leo Platform
Lenses
Striim
Google Cloud Pub/Sub Spark StreamingNo Google Cloud Pub/Sub videos yet. You could help us improve this page by suggesting one.
Based on our record, Google Cloud Pub/Sub should be more popular than Spark Streaming. It has been mentiond 15 times since March 2021. 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.
Secondly, Go is incredibly easy to learn and in my opinion, maintain. This means that if you're a growing company and expect to onboard new teams and team members, having Go as a basis for your systems should mean that new engineers can get up to speed quickly. Below is a small sample application that can connect to Google PubSub, subscribe to a topic, send an event and then clean up. In total, its 82 lines of... - Source: dev.to / 7 months ago
Google Cloud Pub/Sub is a fully-managed, globally scalable and secure queue provided by Google Cloud for asynchronous processing messages. Cloud Pub/Sub has many of the same advantages and disadvantages as SQS due to also being cloud hosted. It has a free and paid tier. - Source: dev.to / 12 months ago
Cloud Pub/Sub: A global messaging service for event-driven architectures. - Source: dev.to / about 1 year ago
Google Cloud Functions is a FaaS offering from Google Cloud Platform (GCP). It allows developers to run their code in response to events, such as changes in a database or the arrival of a message in a Pub/Sub topic. Like AWS Lambda, Google Cloud Functions can be used to build a variety of applications, including serverless websites, data processing pipelines, and real-time data streams. - Source: dev.to / over 1 year ago
That gets triggered when a Pub/Sub topic is fired (from the webhook function). - Source: dev.to / over 1 year 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 / 3 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.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time
