Azure Data Factory vs Singer

Overview

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Singer

Stacks21

Followers34

Votes2

GitHub Stars573

Forks132

Azure Data Factory vs Singer: What are the differences?

Introduction

Azure Data Factory and Singer are both data integration tools that enable users to extract, transform, and load data from different sources into a destination. However, there are several key differences between these two platforms.

Integration Flexibility: Azure Data Factory provides a wide range of connectors and pre-built templates for integrating data from various sources, including on-premises systems, cloud services, and databases. In contrast, Singer is an open-source framework that requires users to write custom scripts called "taps" to extract data from sources and "targets" to load data into destinations. This gives Azure Data Factory an advantage in terms of integration flexibility and ease of use.
Scalability: Azure Data Factory is designed to handle large-scale data integration scenarios, offering scalability and parallel processing capabilities. It can efficiently process and transform large volumes of data using distributed computing resources. Singer, on the other hand, is a relatively lightweight framework that may not be as suitable for handling massive data sets or complex data transformations.
Monitoring and Management: Azure Data Factory provides a comprehensive monitoring and management interface that allows users to monitor data integration pipelines, view execution logs, and troubleshoot issues. It also offers built-in alerting and notification features for proactive monitoring. Singer, being a framework, does not provide a dedicated monitoring and management interface. Users need to implement their own logging and monitoring solutions using appropriate tools or platforms.
Data Transformation Capabilities: Azure Data Factory offers a wide range of built-in data transformation activities and functions, such as data mapping, aggregation, filtering, and enrichment. It also provides integration with Azure Databricks for advanced data transformation and analytics capabilities. Singer, on the other hand, focuses primarily on data extraction and loading, and does not provide built-in data transformation capabilities. Users can perform basic transformations using custom scripts, but more complex transformations may require additional tools or services.
Ease of Use: Azure Data Factory provides a visual interface and drag-and-drop functionality for designing and configuring data integration pipelines. It also offers a code-free development experience for users who are not familiar with coding. Singer, on the other hand, requires users to write custom scripts in Python or other supported programming languages, which may require a certain level of coding knowledge. This makes Azure Data Factory more accessible and easier to use for users with limited programming skills.
Pricing Model: Azure Data Factory follows a consumption-based pricing model, where users pay for the resources consumed during data integration and processing. The cost is based on the number of pipeline activities, data volume, and compute resources used. Singer, being an open-source framework, does not have a specific pricing model. Users need to consider the cost of infrastructure and any additional services or tools required for data integration and management.

In summary, Azure Data Factory offers a wider range of integration flexibility, scalability, monitoring and management capabilities, data transformation features, ease of use, and follows a consumption-based pricing model compared to Singer.

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Advice on Azure Data Factory, Singer

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

Azure Data Factory	Singer
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.	Singer powers data extraction and consolidation for all of your organization’s tools: advertising platforms, web analytics, payment processors, email service providers, marketing automation, databases, and more.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	-
Statistics
GitHub Stars 516	GitHub Stars 573
GitHub Forks 610	GitHub Forks 132
Stacks 253	Stacks 21
Followers 484	Followers 34
Votes 0	Votes 2
Pros & Cons
No community feedback yet	Pros 1 Open source 1 Multiple inputs "taps"
Integrations
Octotree Java .NET	GitLab FreshDesk Braintree HubSpot Marketo Shippo Close.io Harvest Urban Airship FullStory

What are some alternatives to Azure Data Factory, Singer?

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

Azure Data Factory vs Singer: What are the differences?

Introduction

Integration Flexibility: Azure Data Factory provides a wide range of connectors and pre-built templates for integrating data from various sources, including on-premises systems, cloud services, and databases. In contrast, Singer is an open-source framework that requires users to write custom scripts called "taps" to extract data from sources and "targets" to load data into destinations. This gives Azure Data Factory an advantage in terms of integration flexibility and ease of use.
Scalability: Azure Data Factory is designed to handle large-scale data integration scenarios, offering scalability and parallel processing capabilities. It can efficiently process and transform large volumes of data using distributed computing resources. Singer, on the other hand, is a relatively lightweight framework that may not be as suitable for handling massive data sets or complex data transformations.
Monitoring and Management: Azure Data Factory provides a comprehensive monitoring and management interface that allows users to monitor data integration pipelines, view execution logs, and troubleshoot issues. It also offers built-in alerting and notification features for proactive monitoring. Singer, being a framework, does not provide a dedicated monitoring and management interface. Users need to implement their own logging and monitoring solutions using appropriate tools or platforms.
Data Transformation Capabilities: Azure Data Factory offers a wide range of built-in data transformation activities and functions, such as data mapping, aggregation, filtering, and enrichment. It also provides integration with Azure Databricks for advanced data transformation and analytics capabilities. Singer, on the other hand, focuses primarily on data extraction and loading, and does not provide built-in data transformation capabilities. Users can perform basic transformations using custom scripts, but more complex transformations may require additional tools or services.
Ease of Use: Azure Data Factory provides a visual interface and drag-and-drop functionality for designing and configuring data integration pipelines. It also offers a code-free development experience for users who are not familiar with coding. Singer, on the other hand, requires users to write custom scripts in Python or other supported programming languages, which may require a certain level of coding knowledge. This makes Azure Data Factory more accessible and easier to use for users with limited programming skills.
Pricing Model: Azure Data Factory follows a consumption-based pricing model, where users pay for the resources consumed during data integration and processing. The cost is based on the number of pipeline activities, data volume, and compute resources used. Singer, being an open-source framework, does not have a specific pricing model. Users need to consider the cost of infrastructure and any additional services or tools required for data integration and management.

Azure Data Factory vs Singer

Overview

Azure Data Factory vs Singer: What are the differences?