StreamSets vs Venice
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. | It is a derived data storage platform for planet-scale workloads. It is a high-throughput, low-latency, highly-available, horizontally-scalable, eventually-consistent storage system with first-class support for ingesting the output of batch and stream processing jobs. |
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. | High throughput asynchronous ingestion from batch and streaming sources (e.g. Hadoop and Samza);
Low latency online reads via remote queries or in-process caching;
Active-active replication between regions with CRDT-based conflict resolution;
Multi-cluster support within each region with operator-driven cluster assignment;
Multi-tenancy, horizontal scalability and elasticity within each cluster |
Statistics | |
GitHub Stars - | GitHub Stars 571 |
GitHub Forks - | GitHub Forks 106 |
Stacks 53 | Stacks 0 |
Followers 133 | Followers 4 |
Votes 0 | Votes 0 |
Pros & Cons | |
Cons
| No community feedback yet |
Integrations | |
What are some alternatives to StreamSets, Venice?
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.
