Apache NiFi vs Apache Oozie

Overview

Apache Oozie

Stacks40

Followers76

Votes0

Apache NiFi

Stacks393

Followers692

Votes65

Apache NiFi vs Apache Oozie: What are the differences?

Data Flow vs Workflow Management: Apache NiFi is a data flow management tool that focuses on the automation of data movement between systems. It is designed to handle real-time data streaming and allows the creation of complex data flows using a graphical user interface. On the other hand, Apache Oozie is a workflow scheduler system that is used to manage Hadoop jobs. It provides a way to define dependencies between jobs and schedule their execution accordingly.
Real-Time vs Batch Processing: Apache NiFi is more suitable for real-time data processing scenarios where data needs to be ingested, processed, and delivered in near real-time. It supports streaming data and can handle data ingestion from various sources. In contrast, Apache Oozie is typically used for batch processing jobs that require a predefined workflow with dependencies between tasks.
User Interface: Apache NiFi provides a user-friendly graphical interface that allows users to design, monitor, and manage data flows visually. It simplifies the process of creating complex data pipelines without the need for extensive coding. Apache Oozie, on the other hand, relies on XML-based configuration files to define workflows, which may require more technical expertise.
Extensibility: Apache NiFi has a modular architecture that allows users to extend its functionality by adding custom processors, controllers, and reporting tasks. It supports a wide range of plugins and extensions that can be easily integrated into data flows. In comparison, Apache Oozie's functionality is more limited and focused primarily on job scheduling within the Hadoop ecosystem.
Scalability: Apache NiFi is designed to be highly scalable and can handle large volumes of data across distributed systems. It supports clustering and provides mechanisms for fault tolerance and high availability. Apache Oozie can also scale to some extent by deploying multiple instances for workload distribution but may not be as flexible in handling dynamic data flows.
Use Cases: Apache NiFi is commonly used for data ingestion, ETL (extract, transform, load) processes, IoT (Internet of Things) data management, and real-time analytics. It is well-suited for scenarios that require handling streaming data and building data pipelines. In contrast, Apache Oozie is preferred for batch processing tasks, such as running MapReduce jobs, Spark jobs, Hive queries, and other Hadoop ecosystem jobs that have dependencies and workflow scheduling requirements.

In Summary, Apache NiFi is ideal for real-time data flow management and handling streaming data, while Apache Oozie is more suitable for batch processing workflows and job scheduling within the Hadoop ecosystem.

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Detailed Comparison

Apache Oozie	Apache NiFi
It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path.	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.
-	Web-based user interface; Highly configurable; Data Provenance; Designed for extension; Secure
Statistics
Stacks 40	Stacks 393
Followers 76	Followers 692
Votes 0	Votes 65
Pros & Cons
No community feedback yet	Pros 17 Visual Data Flows using Directed Acyclic Graphs (DAGs) 8 Free (Open Source) 7 Simple-to-use 5 Reactive with back-pressure 5 Scalable horizontally as well as vertically Cons 2 Memory-intensive 2 HA support is not full fledge 1 Kkk
Integrations
No integrations available	MongoDB Amazon SNS Amazon S3 Linux Amazon SQS Kafka Apache Hive macOS

What are some alternatives to Apache Oozie, Apache NiFi?

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.

Airflow

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

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.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

Related Comparisons

Apache NiFi vs Apache Oozie: What are the differences?

Data Flow vs Workflow Management: Apache NiFi is a data flow management tool that focuses on the automation of data movement between systems. It is designed to handle real-time data streaming and allows the creation of complex data flows using a graphical user interface. On the other hand, Apache Oozie is a workflow scheduler system that is used to manage Hadoop jobs. It provides a way to define dependencies between jobs and schedule their execution accordingly.
Real-Time vs Batch Processing: Apache NiFi is more suitable for real-time data processing scenarios where data needs to be ingested, processed, and delivered in near real-time. It supports streaming data and can handle data ingestion from various sources. In contrast, Apache Oozie is typically used for batch processing jobs that require a predefined workflow with dependencies between tasks.
User Interface: Apache NiFi provides a user-friendly graphical interface that allows users to design, monitor, and manage data flows visually. It simplifies the process of creating complex data pipelines without the need for extensive coding. Apache Oozie, on the other hand, relies on XML-based configuration files to define workflows, which may require more technical expertise.
Extensibility: Apache NiFi has a modular architecture that allows users to extend its functionality by adding custom processors, controllers, and reporting tasks. It supports a wide range of plugins and extensions that can be easily integrated into data flows. In comparison, Apache Oozie's functionality is more limited and focused primarily on job scheduling within the Hadoop ecosystem.
Scalability: Apache NiFi is designed to be highly scalable and can handle large volumes of data across distributed systems. It supports clustering and provides mechanisms for fault tolerance and high availability. Apache Oozie can also scale to some extent by deploying multiple instances for workload distribution but may not be as flexible in handling dynamic data flows.
Use Cases: Apache NiFi is commonly used for data ingestion, ETL (extract, transform, load) processes, IoT (Internet of Things) data management, and real-time analytics. It is well-suited for scenarios that require handling streaming data and building data pipelines. In contrast, Apache Oozie is preferred for batch processing tasks, such as running MapReduce jobs, Spark jobs, Hive queries, and other Hadoop ecosystem jobs that have dependencies and workflow scheduling requirements.

Apache NiFi vs Apache Oozie

Overview