Azure Data Factory vs Azure Synapse

Overview

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Azure Synapse

Stacks104

Followers230

Votes10

Azure Data Factory vs Azure Synapse: What are the differences?

Azure Data Factory and Azure Synapse are both powerful platforms provided by Microsoft for data integration and analytics. Let's explore the key differences between them:

Architecture and Use Cases: Azure Data Factory is primarily designed for data integration, transformation, and orchestration workflows. It enables the extraction, transformation, and loading (ETL) of data from various sources into data lakes or warehouses. In contrast, Azure Synapse is an end-to-end analytics service that combines big data, data warehousing, and data integration capabilities. It allows organizations to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs.
Ease of Use and User Interface: Azure Data Factory offers a user-friendly drag-and-drop interface that allows users to easily create data pipelines using pre-built connectors and activities. It simplifies the process of defining and executing complex workflows. On the other hand, Azure Synapse provides a unified workspace that integrates with various tools such as Power BI and Azure Machine Learning. It offers a familiar SQL-based environment for data professionals to perform data analytics and machine learning tasks.
Scalability and Performance: Azure Synapse is built on a massively parallel processing (MPP) architecture, which allows it to handle large volumes of data and complex analytical queries with high performance. It offers features like distributed caching and data replication for improved scalability and availability. Azure Data Factory, on the other hand, focuses on data movement and transformation workflows, with scalability options that can be configured based on the specific requirements of the data pipelines.
Built-in Integration: Azure Synapse provides native integration with a wide range of Azure services and tools, including Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Machine Learning. It offers built-in connectors for seamless data ingestion and integration, making it easier to leverage the power of other Azure services. Azure Data Factory also provides integration capabilities, but its focus is more on orchestrating data workflows across different data sources, both on-premises and in the cloud.
Analytics and ML Capabilities: While both platforms support analytics and machine learning tasks, Azure Synapse offers more advanced capabilities in this regard. It provides integrated notebooks, data wrangling capabilities, and support for Apache Spark, enabling users to perform exploratory data analysis, data engineering, and advanced analytics within the same unified environment. Azure Data Factory, on the other hand, primarily focuses on data movement and transformation, with limited native support for analytics and machine learning.
Pricing and Billing: Azure Synapse follows a consumption-based pricing model, where users are billed for the resources they consume, such as data storage and computing power. It offers different pricing tiers based on the performance and storage requirements. Azure Data Factory also follows a consumption-based pricing model, but it offers separate pricing for data movement and data transformation activities, allowing users to optimize costs based on their specific usage patterns.

In summary, Azure Data Factory is primarily focused on data integration and workflow orchestration, while Azure Synapse provides a unified platform for end-to-end analytics and data management. Azure Synapse offers advanced analytics and ML capabilities, a unified workspace, and a scalable MPP architecture, whereas Azure Data Factory excels in data movement, transformation workflows, and cost optimization.

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Advice on Azure Data Factory, Azure Synapse

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	Azure Synapse
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.	It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
GitHub Stars 516	GitHub Stars -
GitHub Forks 610	GitHub Forks -
Stacks 253	Stacks 104
Followers 484	Followers 230
Votes 0	Votes 10
Pros & Cons
No community feedback yet	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Concurrency 1 Dictionary Size Limitation - CCI
Integrations
Octotree Java .NET	No integrations available

What are some alternatives to Azure Data Factory, Azure Synapse?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

Related Comparisons

Azure Data Factory vs Azure Synapse: What are the differences?

Azure Data Factory and Azure Synapse are both powerful platforms provided by Microsoft for data integration and analytics. Let's explore the key differences between them:

Architecture and Use Cases: Azure Data Factory is primarily designed for data integration, transformation, and orchestration workflows. It enables the extraction, transformation, and loading (ETL) of data from various sources into data lakes or warehouses. In contrast, Azure Synapse is an end-to-end analytics service that combines big data, data warehousing, and data integration capabilities. It allows organizations to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs.
Ease of Use and User Interface: Azure Data Factory offers a user-friendly drag-and-drop interface that allows users to easily create data pipelines using pre-built connectors and activities. It simplifies the process of defining and executing complex workflows. On the other hand, Azure Synapse provides a unified workspace that integrates with various tools such as Power BI and Azure Machine Learning. It offers a familiar SQL-based environment for data professionals to perform data analytics and machine learning tasks.
Scalability and Performance: Azure Synapse is built on a massively parallel processing (MPP) architecture, which allows it to handle large volumes of data and complex analytical queries with high performance. It offers features like distributed caching and data replication for improved scalability and availability. Azure Data Factory, on the other hand, focuses on data movement and transformation workflows, with scalability options that can be configured based on the specific requirements of the data pipelines.
Built-in Integration: Azure Synapse provides native integration with a wide range of Azure services and tools, including Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Machine Learning. It offers built-in connectors for seamless data ingestion and integration, making it easier to leverage the power of other Azure services. Azure Data Factory also provides integration capabilities, but its focus is more on orchestrating data workflows across different data sources, both on-premises and in the cloud.
Analytics and ML Capabilities: While both platforms support analytics and machine learning tasks, Azure Synapse offers more advanced capabilities in this regard. It provides integrated notebooks, data wrangling capabilities, and support for Apache Spark, enabling users to perform exploratory data analysis, data engineering, and advanced analytics within the same unified environment. Azure Data Factory, on the other hand, primarily focuses on data movement and transformation, with limited native support for analytics and machine learning.
Pricing and Billing: Azure Synapse follows a consumption-based pricing model, where users are billed for the resources they consume, such as data storage and computing power. It offers different pricing tiers based on the performance and storage requirements. Azure Data Factory also follows a consumption-based pricing model, but it offers separate pricing for data movement and data transformation activities, allowing users to optimize costs based on their specific usage patterns.

Azure Data Factory vs Azure Synapse

Overview