AWS Data Pipeline vs Azure Data Factory

Overview

AWS Data Pipeline

Stacks94

Followers398

Votes1

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

AWS Data Pipeline vs Azure Data Factory: What are the differences?

Introduction

AWS Data Pipeline and Azure Data Factory are both cloud-based data integration services that are used to orchestrate and automate the movement and transformation of data between different sources and destinations. While they serve similar purposes, there are some key differences between these two services that sets them apart. Let's explore these differences in more detail below.

Supported Cloud Platforms: AWS Data Pipeline is a service provided by Amazon Web Services (AWS) and is designed to work specifically with AWS services and resources. It provides seamless integration with services like Amazon S3, Amazon RDS, and Amazon Redshift. On the other hand, Azure Data Factory is a service provided by Microsoft Azure and is designed to work with Azure services and resources. It provides integration with services like Azure Blob Storage, Azure Data Lake, and Azure SQL Database. The key difference here is that AWS Data Pipeline is focused on AWS services, while Azure Data Factory is focused on Azure services.
Data Movement Capabilities: Both AWS Data Pipeline and Azure Data Factory support moving data between different sources and destinations. However, there are some differences in the data movement capabilities offered by these services. AWS Data Pipeline provides a wide range of pre-built connectors and templates to extract, transform, and load data. It supports data movement from on-premises sources to AWS services, as well as between different AWS services. On the other hand, Azure Data Factory offers a similar set of data movement capabilities, but with a focus on Azure services. It supports data movement from on-premises sources to Azure services, as well as between different Azure services. The key difference here is that the data movement capabilities of these services are tailored to their respective cloud platforms.
Workflow Orchestration: Both AWS Data Pipeline and Azure Data Factory provide facilities for orchestrating and scheduling workflows. AWS Data Pipeline uses a visual editor to define and schedule complex data-driven workflows. It supports dependency management, error handling, and retry mechanisms for different phases of the workflow. Azure Data Factory also provides a visual designer for defining and scheduling workflows. It supports complex dependency management, error handling, and retry mechanisms using built-in activities and pipelines. The key difference here is that the workflow orchestration capabilities of these services are designed to work with their respective cloud platforms.
Pricing and Billing: AWS Data Pipeline and Azure Data Factory have different pricing models and billing structures. AWS Data Pipeline offers a pay-as-you-go pricing model, where you are billed for the resources used and the number of pipeline executions. It provides a free tier with limited features and capacity. Azure Data Factory also offers a pay-as-you-go pricing model, where you are billed for the resources used and the number of pipeline activities executed. It also provides a free tier with limited features and capacity. The key difference here is in the specific pricing and billing details for each service, which can vary depending on the cloud platform and the specific usage patterns.
Integration with Ecosystem: Both AWS Data Pipeline and Azure Data Factory integrate with the broader ecosystem of their respective cloud platforms. AWS Data Pipeline integrates well with other AWS services such as AWS Lambda, Amazon EMR, and AWS Glue for advanced data processing and analytics. Azure Data Factory integrates well with other Azure services such as Azure Functions, Azure Databricks, and Azure Synapse Analytics for data processing and analytics. The key difference here is the integration options and capabilities offered by these services within their respective cloud ecosystems.
Developer Community and Support: AWS Data Pipeline and Azure Data Factory are backed by strong developer communities and have extensive documentation and support resources available. Both services have active forums, documentation, and support channels to help users troubleshoot issues and find solutions. The key difference here is in the specific developer community and support resources provided by each service, which can vary based on the user base and ecosystem.

In summary, AWS Data Pipeline and Azure Data Factory are both powerful cloud-based data integration services, but they have key differences in terms of supported cloud platforms, data movement capabilities, workflow orchestration, pricing and billing structures, integration with ecosystem, and developer community and support.

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Advice on AWS Data Pipeline, Azure Data Factory

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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Detailed Comparison

AWS Data Pipeline	Azure Data Factory
AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.	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.
You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.;Hourly analysis of Amazon S3‐based log data;Daily replication of AmazonDynamoDB data to Amazon S3;Periodic replication of on-premise JDBC database tables into RDS	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 94	Stacks 253
Followers 398	Followers 484
Votes 1	Votes 0
Pros & Cons
Pros 1 Easy to create DAG and execute it	No community feedback yet
Integrations
No integrations available	Octotree Java .NET

What are some alternatives to AWS Data Pipeline, Azure Data Factory?

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

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.

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

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 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™ 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

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

Splunk

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

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.

Related Comparisons

AWS Data Pipeline vs Azure Data Factory: What are the differences?

Introduction

Supported Cloud Platforms: AWS Data Pipeline is a service provided by Amazon Web Services (AWS) and is designed to work specifically with AWS services and resources. It provides seamless integration with services like Amazon S3, Amazon RDS, and Amazon Redshift. On the other hand, Azure Data Factory is a service provided by Microsoft Azure and is designed to work with Azure services and resources. It provides integration with services like Azure Blob Storage, Azure Data Lake, and Azure SQL Database. The key difference here is that AWS Data Pipeline is focused on AWS services, while Azure Data Factory is focused on Azure services.
Data Movement Capabilities: Both AWS Data Pipeline and Azure Data Factory support moving data between different sources and destinations. However, there are some differences in the data movement capabilities offered by these services. AWS Data Pipeline provides a wide range of pre-built connectors and templates to extract, transform, and load data. It supports data movement from on-premises sources to AWS services, as well as between different AWS services. On the other hand, Azure Data Factory offers a similar set of data movement capabilities, but with a focus on Azure services. It supports data movement from on-premises sources to Azure services, as well as between different Azure services. The key difference here is that the data movement capabilities of these services are tailored to their respective cloud platforms.
Workflow Orchestration: Both AWS Data Pipeline and Azure Data Factory provide facilities for orchestrating and scheduling workflows. AWS Data Pipeline uses a visual editor to define and schedule complex data-driven workflows. It supports dependency management, error handling, and retry mechanisms for different phases of the workflow. Azure Data Factory also provides a visual designer for defining and scheduling workflows. It supports complex dependency management, error handling, and retry mechanisms using built-in activities and pipelines. The key difference here is that the workflow orchestration capabilities of these services are designed to work with their respective cloud platforms.
Pricing and Billing: AWS Data Pipeline and Azure Data Factory have different pricing models and billing structures. AWS Data Pipeline offers a pay-as-you-go pricing model, where you are billed for the resources used and the number of pipeline executions. It provides a free tier with limited features and capacity. Azure Data Factory also offers a pay-as-you-go pricing model, where you are billed for the resources used and the number of pipeline activities executed. It also provides a free tier with limited features and capacity. The key difference here is in the specific pricing and billing details for each service, which can vary depending on the cloud platform and the specific usage patterns.
Integration with Ecosystem: Both AWS Data Pipeline and Azure Data Factory integrate with the broader ecosystem of their respective cloud platforms. AWS Data Pipeline integrates well with other AWS services such as AWS Lambda, Amazon EMR, and AWS Glue for advanced data processing and analytics. Azure Data Factory integrates well with other Azure services such as Azure Functions, Azure Databricks, and Azure Synapse Analytics for data processing and analytics. The key difference here is the integration options and capabilities offered by these services within their respective cloud ecosystems.
Developer Community and Support: AWS Data Pipeline and Azure Data Factory are backed by strong developer communities and have extensive documentation and support resources available. Both services have active forums, documentation, and support channels to help users troubleshoot issues and find solutions. The key difference here is in the specific developer community and support resources provided by each service, which can vary based on the user base and ecosystem.

AWS Data Pipeline vs Azure Data Factory

Overview