Need advice about which tool to choose?Ask the StackShare community!
Add tool
Apache NiFi vs StreamSets: What are the differences?
Developers describe Apache NiFi as "A reliable system to process and distribute data". 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. On the other hand, StreamSets is detailed as "Where DevOps Meets Data Integration". The industry's first data operations platform for full life-cycle management of data in motion.
Apache NiFi and StreamSets are primarily classified as "Stream Processing" and "Data Science" tools respectively.
Some of the features offered by Apache NiFi are:
- Web-based user interface
- Highly configurable
- Data Provenance
On the other hand, StreamSets provides the following key features:
- Build Batch & Streaming Pipelines in Hours
- Map and Monitor Runtime Performance
- Protect Sensitive Data as it Arrives
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn MorePros of Apache NiFi
Pros of StreamSets
Pros of Apache NiFi
- Visual Data Flows using Directed Acyclic Graphs (DAGs)15
- Free (Open Source)8
- Simple-to-use7
- Reactive with back-pressure5
- Scalable horizontally as well as vertically5
- Fast prototyping4
- Bi-directional channels3
- Data provenance2
- Built-in graphical user interface2
- End-to-end security between all nodes2
- Can handle messages up to gigabytes in size2
- Hbase support1
- Kudu support1
- Hive support1
- Slack integration1
- Support for custom Processor in Java1
- Lot of articles1
- Lots of documentation1
Pros of StreamSets
Be the first to leave a pro
Sign up to add or upvote prosMake informed product decisions
Cons of Apache NiFi
Cons of StreamSets
Cons of Apache NiFi
- HA support is not full fledge2
- Memory-intensive2
Cons of StreamSets
- No user community2
- Crashes1
Sign up to add or upvote consMake informed product decisions
What is 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.
What is StreamSets?
An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
Need advice about which tool to choose?Ask the StackShare community!
What companies use Apache NiFi?
What companies use StreamSets?
What companies use Apache NiFi?
See which teams inside your own company are using Apache NiFi or StreamSets.
Sign up for StackShare EnterpriseLearn MoreSign up to get full access to all the companiesMake informed product decisions
What tools integrate with Apache NiFi?
What tools integrate with StreamSets?
What tools integrate with Apache NiFi?
What tools integrate with StreamSets?
Sign up to get full access to all the tool integrationsMake informed product decisions
What are some alternatives to Apache NiFi and StreamSets?
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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.
Logstash
Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
Apache Camel
An open source Java framework that focuses on making integration easier and more accessible to developers.
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.