Mara vs StreamSets
Overview
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Detailed Comparison
An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps. | A lightweight ETL framework with a focus on transparency and complexity reduction. |
Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps. | Data integration pipelines as code: pipelines, tasks and commands are created using declarative Python code.; PostgreSQL as a data processing engine.; Extensive web ui. The web browser as the main tool for inspecting, running and debugging pipelines.; GNU make semantics. Nodes depend on the completion of upstream nodes. No data dependencies or data flows.; No in-app data processing: command line tools as the main tool for interacting with databases and data.; Single machine pipeline execution based on Python's multiprocessing. No need for distributed task queues. Easy debugging and and output logging.; Cost based priority queues: nodes with higher cost (based on recorded run times) are run first. |
Statistics | |
Stacks 53 | Stacks 5 |
Followers 133 | Followers 21 |
Votes 0 | Votes 3 |
Pros & Cons | |
Cons
| Pros
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Integrations | |
| No integrations available | |
What are some alternatives to StreamSets, Mara?
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
RabbitMQ
RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
Celery
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Amazon SQS
Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.
NSQ
NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.
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.
ActiveMQ
Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.
ZeroMQ
The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.
Presto
Distributed SQL Query Engine for Big Data
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
