There are two great data tools within the Microsoft Fabric ecosystem that were independent prior to the launch of Fabric. Both tools have similar capabilities in terms of extract, transform, and load (ETL) and extract, load, and transform (ELT), however, they are conceived differently.
Azure Data Factory (ADF) and Azure Synapse are both cloud-based data integration and analytics services offered by Microsoft. However, they have different features, capabilities, and use cases.
In this post, we will review the main differences between the two tools, explore how they work, and how they integrate with Microsoft Fabric.
What are the Differences Between Azure Data Factory and Azure Synapse?
Data Integration
Azure Data Factory (ADF) is a data integration service that allows you to create data-driven workflows (pipelines) that can ingest data from many different sources, transform data with code-free or code-based tools, and load data into various destinations. Its +90 built-in connectors facilitate data ingestion with ease.
Azure Synapse provides the same data integration engine and experience as ADF by creating data pipelines. However, Synapse Link is a feature that allows near-real-time ingestion of data from internal (Azure) or external )third-party provider) operational databases without ETL.
Data Warehousing
ADF does not have its own data warehouse service, but it can load data into other data warehouse services such as Azure SQL Data Warehouse (now Azure Synapse Analytics) or Azure SQL Database.
Azure Synapse, on the other hand, is an enterprise data warehouse service that integrates SQL technologies for relational and non-relational data, offering both serverless and dedicated resource models.
Big Data Analytics
ADF does not have its own big data analytics service, but it can orchestrate and monitor big data processing jobs using computation services such as Azure HDInsight, Azure Databricks, or Azure Synapse Spark. Azure Synapse Spark is a fully managed Apache Spark service that integrates with Synapse SQL and Synapse Studio.
Synapse Spark allows you to perform data engineering, data preparation, machine learning, and interactive data exploration using Scala, Python, SQL, or .NET languages.
What are the Purposes of Each Tool and Who’s the Target Audience?
Let’s understand now the different target audiences for ADF and Synapse. Here are some of the common scenarios and use cases for each tool:
Azure Data Factory
ADF is a tool for data engineers, data analysts, and data scientists who need to create, manage, and monitor data pipelines that move and transform data from various sources to various destinations.
ADF is suitable for scenarios such as data ingestion, data transformation, and data orchestration.
Azure Synapse
Azure Synapse is a tool for data engineers, data analysts, data scientists, and business users who need to perform data integration, data warehousing, big data analytics, and log and time series analytics on a single platform.
Azure Synapse is suitable for scenarios such as data warehousing, big data analytics, and log and time series analytics. It also integrates seamlessly with Power BI for visualization.
Conclusion
In my experience, Azure Synapse is a comprehensive solution for data integration, data warehousing, and data analytics. It supports both structured and unstructured data, and it allows to perform machine learning and artificial intelligence. It is possible to monitor Spark jobs. On the other hand, Azure Data Factory is a data integration tool capable of creating data pipelines and loading data into any destination, inside or outside Azure. It has cross-region integration runtime and runtime sharing, which is not available in Azure Synapse.
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