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  5. Azure Data Factory vs FUSE for macOS

Azure Data Factory vs FUSE for macOS

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
FUSE for macOS
FUSE for macOS
Stacks6
Followers6
Votes0

FUSE for macOS vs Azure Data Factory: What are the differences?

Developers describe FUSE for macOS as "File system integration made easy". Allows you to extend macOS's native file handling capabilities via third-party file systems. On the other hand, Azure Data Factory is detailed as "Hybrid data integration service that simplifies ETL at scale". 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.

FUSE for macOS and Azure Data Factory can be categorized as "Integration" tools.

Some of the features offered by FUSE for macOS are:

  • SDK
  • AccessibilityFS support
  • exFAT support

On the other hand, Azure Data Factory provides the following key features:

  • Real-Time Integration
  • Parallel Processing
  • Data Chunker

FUSE for macOS and Azure Data Factory are both open source tools. FUSE for macOS with 5.43K GitHub stars and 366 forks on GitHub appears to be more popular than Azure Data Factory with 152 GitHub stars and 256 GitHub forks.

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Advice on Azure Data Factory, FUSE for macOS

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?

269k views269k
Comments

Detailed Comparison

Azure Data Factory
Azure Data Factory
FUSE for macOS
FUSE for macOS

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.

Allows you to extend macOS's native file handling capabilities via third-party file systems.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
SDK; AccessibilityFS support; exFAT support; Ext support; Mount options; NTFS 3G support; procfs support; SSHFS support; XFS support; YouTubeFs support
Statistics
GitHub Stars
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
253
Stacks
6
Followers
484
Followers
6
Votes
0
Votes
0
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
No integrations available

What are some alternatives to Azure Data Factory, FUSE for macOS?

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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