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Apache NiFi vs KSQL: What are the differences?
Apache NiFi: 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; KSQL: Open Source Streaming SQL for Apache Kafka. KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.
Apache NiFi and KSQL belong to "Stream Processing" category of the tech stack.
KSQL is an open source tool with 2.37K GitHub stars and 493 GitHub forks. Here's a link to KSQL's open source repository on GitHub.
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Learn MorePros of Apache NiFi
Pros of KSQL
Pros of Apache NiFi
- Visual Data Flows using Directed Acyclic Graphs (DAGs)17
- Free (Open Source)8
- Simple-to-use7
- Scalable horizontally as well as vertically5
- Reactive with back-pressure5
- Fast prototyping4
- Bi-directional channels3
- End-to-end security between all nodes3
- Built-in graphical user interface2
- Can handle messages up to gigabytes in size2
- Data provenance2
- Lots of documentation1
- Hbase support1
- Support for custom Processor in Java1
- Hive support1
- Kudu support1
- Slack integration1
- Lot of articles1
Pros of KSQL
- Streamprocessing on Kafka3
- SQL syntax with windowing functions over streams2
- Easy transistion for SQL Devs0
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Cons of Apache NiFi
Cons of KSQL
Cons of Apache NiFi
- HA support is not full fledge2
- Memory-intensive2
- Kkk1
Cons of KSQL
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- No public GitHub repository available -
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 KSQL?
KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.
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What companies use Apache NiFi?
What companies use KSQL?
What companies use Apache NiFi?
What companies use KSQL?
See which teams inside your own company are using Apache NiFi or KSQL.
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What tools integrate with Apache NiFi?
What tools integrate with KSQL?
What tools integrate with Apache NiFi?
What tools integrate with KSQL?
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What are some alternatives to Apache NiFi and KSQL?
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.