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Samza vs Scheduler API: What are the differences?
What is Samza? A distributed stream processing framework. It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.
What is Scheduler API? An API for scheduling queue messages. It is a simple API to delay SQS messages. Call our APIs and we'll publish your messages when you need them.
Samza and Scheduler API can be categorized as "Message Queue" tools.
Some of the features offered by Samza are:
- HIGH PERFORMANCE
- HORIZONTALLY SCALABLE
- EASY TO OPERATE
On the other hand, Scheduler API provides the following key features:
- scheduling
- cancelling scheduled SQS messages
- changing the delay for already scheduled messages
Samza is an open source tool with 620 GitHub stars and 269 GitHub forks. Here's a link to Samza's open source repository on GitHub.
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What is Samza?
It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.
What is Scheduler API?
It is a simple API to delay SQS messages. Call our APIs and we'll publish your messages when you need them.
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Jobs that mention Samza and Scheduler API as a desired skillset
What companies use Samza?
What companies use Scheduler API?
What companies use Samza?
What companies use Scheduler API?
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What tools integrate with Samza?
What tools integrate with Scheduler API?
What tools integrate with Scheduler API?
No integrations found
What are some alternatives to Samza and Scheduler API?
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Apache Storm
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
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 Streams
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.