Apache NiFi vs Azure Data Factory

Overview

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Apache NiFi

Stacks393

Followers692

Votes65

Apache NiFi vs Azure Data Factory: What are the differences?

Introduction

Apache NiFi and Azure Data Factory are two popular data integration platforms that are widely used in the industry. While both of them provide similar functionalities such as data ingestion, transformation, and integration, there are several key differences that set them apart. In this article, we will explore these differences and understand when and why to choose one over the other.

Architecture: Apache NiFi is built on a flow-based programming model, where data flows through a series of interconnected processors and controllers. It provides a visual UI for designing and managing these data flows. On the other hand, Azure Data Factory follows a more pipeline-based approach, where data is moved through a series of activities and transformation steps in a directed fashion. The pipelines can be defined using JSON or a visual designer in Azure Portal.
Deployment Flexibility: Apache NiFi offers more deployment flexibility as it can be run on-premises, in the cloud, or in a hybrid environment. It provides support for clustering and high availability, allowing scalability and fault tolerance. In contrast, Azure Data Factory is a cloud-native service and is tightly integrated with other Azure services. It is primarily designed for running in the Azure cloud environment, although it can also connect with on-premises data sources.
Ecosystem Integration: Apache NiFi has a large and vibrant open-source community, which results in a wide range of processors, extensions, and integrations available. It supports integration with various data sources, databases, messaging systems, and cloud services. Azure Data Factory, on the other hand, is tightly integrated with the Azure ecosystem, making it seamless to work with other Azure services such as Azure Blob Storage, Azure Data Lake, Azure SQL Database, and more.
Ease of Use: Apache NiFi provides a user-friendly web-based UI for designing and monitoring data flows. It offers a drag-and-drop interface along with visual feedback, making it easy for users to connect processors and set up data routing. Azure Data Factory also provides a visual designer, but the overall user experience may vary depending on the user's familiarity with the Azure Portal.
Data Transformation Capabilities: Both Apache NiFi and Azure Data Factory offer a range of built-in data transformation capabilities. However, Apache NiFi provides a wider range of processors and transformation functions out of the box. It also supports custom scripting using languages like Groovy, Python, and JavaScript. Azure Data Factory, on the other hand, relies more on external services such as Azure Databricks or Azure Function for complex data transformations.
Pricing Model: Apache NiFi is an open-source project and is available free of cost. However, if you choose to use a commercial distribution or enterprise support, there may be associated costs. On the other hand, Azure Data Factory follows a pay-as-you-go pricing model, where you are billed based on the number of pipeline activities executed and the amount of data processed.

In summary, Apache NiFi and Azure Data Factory differ in terms of architecture, deployment flexibility, ecosystem integration, ease of use, data transformation capabilities, and pricing model. The choice between them depends on specific requirements such as deployment environment, data sources, integration needs, and budget considerations.

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Advice on Azure Data Factory, Apache NiFi

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Comments

Detailed Comparison

Azure Data Factory	Apache NiFi
It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.	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.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	Web-based user interface; Highly configurable; Data Provenance; Designed for extension; Secure
Statistics
GitHub Stars 516	GitHub Stars -
GitHub Forks 610	GitHub Forks -
Stacks 253	Stacks 393
Followers 484	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
Octotree Java .NET	MongoDB Amazon SNS Amazon S3 Linux Amazon SQS Kafka Apache Hive macOS

What are some alternatives to Azure Data Factory, 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.

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

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.

Related Comparisons

Apache NiFi vs Azure Data Factory: What are the differences?

Introduction

Architecture: Apache NiFi is built on a flow-based programming model, where data flows through a series of interconnected processors and controllers. It provides a visual UI for designing and managing these data flows. On the other hand, Azure Data Factory follows a more pipeline-based approach, where data is moved through a series of activities and transformation steps in a directed fashion. The pipelines can be defined using JSON or a visual designer in Azure Portal.
Deployment Flexibility: Apache NiFi offers more deployment flexibility as it can be run on-premises, in the cloud, or in a hybrid environment. It provides support for clustering and high availability, allowing scalability and fault tolerance. In contrast, Azure Data Factory is a cloud-native service and is tightly integrated with other Azure services. It is primarily designed for running in the Azure cloud environment, although it can also connect with on-premises data sources.
Ecosystem Integration: Apache NiFi has a large and vibrant open-source community, which results in a wide range of processors, extensions, and integrations available. It supports integration with various data sources, databases, messaging systems, and cloud services. Azure Data Factory, on the other hand, is tightly integrated with the Azure ecosystem, making it seamless to work with other Azure services such as Azure Blob Storage, Azure Data Lake, Azure SQL Database, and more.
Ease of Use: Apache NiFi provides a user-friendly web-based UI for designing and monitoring data flows. It offers a drag-and-drop interface along with visual feedback, making it easy for users to connect processors and set up data routing. Azure Data Factory also provides a visual designer, but the overall user experience may vary depending on the user's familiarity with the Azure Portal.
Data Transformation Capabilities: Both Apache NiFi and Azure Data Factory offer a range of built-in data transformation capabilities. However, Apache NiFi provides a wider range of processors and transformation functions out of the box. It also supports custom scripting using languages like Groovy, Python, and JavaScript. Azure Data Factory, on the other hand, relies more on external services such as Azure Databricks or Azure Function for complex data transformations.
Pricing Model: Apache NiFi is an open-source project and is available free of cost. However, if you choose to use a commercial distribution or enterprise support, there may be associated costs. On the other hand, Azure Data Factory follows a pay-as-you-go pricing model, where you are billed based on the number of pipeline activities executed and the amount of data processed.

Apache NiFi vs Azure Data Factory

Overview