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  5. Azure Data Factory vs Mule

Azure Data Factory vs Mule

OverviewDecisionsComparisonAlternatives

Overview

Mule runtime engine
Mule runtime engine
Stacks127
Followers129
Votes8
Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610

Azure Data Factory vs Mule: What are the differences?

Introduction

Azure Data Factory and Mule are both integration platforms used to build data integration workflows. However, there are key differences between the two that set them apart.

  1. Deployment and scalability: Azure Data Factory is a cloud-based service provided by Microsoft Azure, allowing users to easily deploy and scale their data integration workflows. On the other hand, Mule is an on-premises integration platform that requires infrastructure setup and management for deployment and scalability.

  2. Connectivity and adaptability: Azure Data Factory offers a wide range of connectors and built-in transformations, allowing users to connect to various data sources and adapt to different data formats seamlessly. Mule, on the other hand, provides extensive connectivity options through its Anypoint Platform, allowing users to integrate with disparate systems and leverage multiple protocols.

  3. Data movement and transformation: Azure Data Factory focuses primarily on data movement and transformation capabilities, enabling users to efficiently orchestrate and transform data at scale. In contrast, Mule provides a comprehensive set of integration capabilities beyond just data movement, including process orchestration, API management, and real-time integration.

  4. Monitoring and management: Azure Data Factory offers built-in monitoring and management features, allowing users to track and manage their data integration workflows efficiently. Mule provides robust monitoring and management capabilities through its Anypoint Platform, including real-time alerts, dashboards, and centralized control.

  5. Automation and scheduling: Azure Data Factory provides native scheduling and automation capabilities, allowing users to define and schedule their data integration workflows easily. Mule provides similar automation features through its Anypoint Platform, allowing users to automate integration processes and workflows.

  6. Pricing and cost: Azure Data Factory offers a pay-as-you-go pricing model, allowing users to pay only for the resources they consume. Mule follows a subscription-based pricing model, where users are charged based on the number of connections, processing power, and features used.

In summary, Azure Data Factory and Mule are both powerful integration platforms with different strengths. Azure Data Factory provides a cloud-native solution with focus on data movement and scalability, while Mule offers a comprehensive integration platform with extensive connectivity options and capabilities beyond data integration.

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Advice on Mule runtime engine, Azure Data Factory

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

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

Mule runtime engine
Mule runtime engine
Azure Data Factory
Azure Data Factory

Its mission is to connect the world’s applications, data and devices. It makes connecting anything easy with Anypoint Platform™, the only complete integration platform for SaaS, SOA and APIs. Thousands of organizations in 60 countries, from emerging brands to Global 500 enterprises, use it to innovate faster and gain competitive advantage.

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.

Connects data;Connects applications;Integration platform;Fast
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Statistics
GitHub Stars
-
GitHub Stars
516
GitHub Forks
-
GitHub Forks
610
Stacks
127
Stacks
253
Followers
129
Followers
484
Votes
8
Votes
0
Pros & Cons
Pros
  • 4
    Open Source
  • 2
    Microservices
  • 2
    Integration
No community feedback yet
Integrations
CloudApp
CloudApp
API Umbrella
API Umbrella
Zapier
Zapier
Octotree
Octotree
Java
Java
.NET
.NET

What are some alternatives to Mule runtime engine, Azure Data Factory?

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Apache Camel

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

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