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Learn MorePros of Google Cloud Dataflow
Pros of Google Cloud Functions
Pros of Google Cloud Run
Pros of Google Cloud Dataflow
- Unified batch and stream processing7
- Autoscaling5
- Fully managed4
- Throughput Transparency3
Pros of Google Cloud Functions
- Serverless Applications7
- Its not AWS5
- Simplicity4
- Free Tiers and Trainging3
- Simple config with GitLab CI/CD2
- Built-in Webhook trigger1
- Typescript Support1
- Blaze, pay as you go1
- Customer Support1
Pros of Google Cloud Run
- HTTPS endpoints11
- Fully managed10
- Pay per use10
- Concurrency: multiple requests sent to each container7
- Deploy containers7
- Serverless7
- Custom domains with auto SSL6
- "Invoke IAM permission" to manage authentication4
- Cons0
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Cons of Google Cloud Dataflow
Cons of Google Cloud Functions
Cons of Google Cloud Run
Cons of Google Cloud Dataflow
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Cons of Google Cloud Functions
- Node.js only1
- Typescript Support0
- Blaze, pay as you go0
Cons of Google Cloud Run
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What is Google Cloud Dataflow?
Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
What is Google Cloud Functions?
Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running
What is Google Cloud Run?
A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.
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What companies use Google Cloud Functions?
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What tools integrate with Google Cloud Dataflow?
What tools integrate with Google Cloud Functions?
What tools integrate with Google Cloud Run?
What tools integrate with Google Cloud Dataflow?
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What are some alternatives to Google Cloud Dataflow, Google Cloud Functions, and Google Cloud Run?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Akutan
A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.