Apache NiFi vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache NiFi

Stacks393

Followers692

Votes65

Apache NiFi vs Apache Spark: What are the differences?

Introduction

Apache NiFi and Apache Spark are both open-source data processing frameworks used for big data analysis and processing. While they both offer powerful features, there are several key differences between the two.

Data Flow vs Data Processing: One of the major differences between Apache NiFi and Apache Spark is their primary focus. NiFi focuses on data flow management, enabling users to easily design and manage data pipelines with a visually intuitive interface. On the other hand, Spark focuses on data processing and analytics, providing a distributed computing framework that can handle complex computations efficiently.
Real-Time vs Batch Processing: Apache NiFi is designed for real-time data processing, allowing users to collect, transform, and route data in real-time. It provides capabilities for handling streaming data and supports continuous data ingestion. In contrast, Apache Spark is primarily designed for batch processing, although it also offers support for real-time processing using its Structured Streaming API.
Ease of Use: Apache NiFi aims to provide a user-friendly interface for data engineers and non-technical users to easily design and manage data flows. It offers a drag-and-drop GUI and a powerful visual representation of data flows, making it easy to understand and maintain complex data pipelines. Apache Spark, on the other hand, has a steeper learning curve and requires knowledge of programming languages like Scala, Java, or Python.
Processing Speed: Apache Spark is known for its high processing speed and in-memory computing capabilities. It utilizes distributed computing across a cluster of machines, allowing it to process large datasets in parallel. NiFi, on the other hand, may not offer the same level of speed and efficiency for large-scale data processing, as it focuses more on data flow management and real-time ingestion.
Data Transformation and Integration: NiFi provides extensive support for data transformation and integration, with a wide range of processors and connectors for various data sources and systems. It allows users to perform tasks such as enrichment, filtering, and joining of data streams. Apache Spark also offers data transformation capabilities but primarily focuses on processing and analytics, with a rich library of functions for data manipulation.
Scalability and Fault Tolerance: Both Apache NiFi and Apache Spark are designed to scale horizontally and handle large volumes of data. However, Spark's underlying architecture and execution model make it more suitable for big data processing and analytics at scale. Spark also provides built-in fault tolerance mechanisms, ensuring that computations continue even in the event of failures.

In summary, Apache NiFi is a data flow management tool that excels in real-time data processing and provides an easy-to-use interface, while Apache Spark is a distributed computing framework primarily focused on data processing and analytics, offering high performance and scalability.

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Advice on Apache Spark, Apache NiFi

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	Apache NiFi
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	Web-based user interface; Highly configurable; Data Provenance; Designed for extension; Secure
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 393
Followers 3.5K	Followers 692
Votes 140	Votes 65
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	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 Spark, 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.

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

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

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.

Related Comparisons

Apache NiFi vs Apache Spark: What are the differences?

Introduction

Data Flow vs Data Processing: One of the major differences between Apache NiFi and Apache Spark is their primary focus. NiFi focuses on data flow management, enabling users to easily design and manage data pipelines with a visually intuitive interface. On the other hand, Spark focuses on data processing and analytics, providing a distributed computing framework that can handle complex computations efficiently.
Real-Time vs Batch Processing: Apache NiFi is designed for real-time data processing, allowing users to collect, transform, and route data in real-time. It provides capabilities for handling streaming data and supports continuous data ingestion. In contrast, Apache Spark is primarily designed for batch processing, although it also offers support for real-time processing using its Structured Streaming API.
Ease of Use: Apache NiFi aims to provide a user-friendly interface for data engineers and non-technical users to easily design and manage data flows. It offers a drag-and-drop GUI and a powerful visual representation of data flows, making it easy to understand and maintain complex data pipelines. Apache Spark, on the other hand, has a steeper learning curve and requires knowledge of programming languages like Scala, Java, or Python.
Processing Speed: Apache Spark is known for its high processing speed and in-memory computing capabilities. It utilizes distributed computing across a cluster of machines, allowing it to process large datasets in parallel. NiFi, on the other hand, may not offer the same level of speed and efficiency for large-scale data processing, as it focuses more on data flow management and real-time ingestion.
Data Transformation and Integration: NiFi provides extensive support for data transformation and integration, with a wide range of processors and connectors for various data sources and systems. It allows users to perform tasks such as enrichment, filtering, and joining of data streams. Apache Spark also offers data transformation capabilities but primarily focuses on processing and analytics, with a rich library of functions for data manipulation.
Scalability and Fault Tolerance: Both Apache NiFi and Apache Spark are designed to scale horizontally and handle large volumes of data. However, Spark's underlying architecture and execution model make it more suitable for big data processing and analytics at scale. Spark also provides built-in fault tolerance mechanisms, ensuring that computations continue even in the event of failures.

Apache NiFi vs Apache Spark

Overview

Apache NiFi vs Apache Spark: What are the differences?