Synapse Data Engineering: A New Way to Work with Data in Microsoft Fabric

Table of Contents

Data is the lifeblood of any organization, but managing and analyzing it can be a daunting task. Data engineers face many challenges such as data fragmentation, data security, data democratization and data scalability. To address these challenges, Microsoft Fabric introduces Synapse Data Engineering, a core experience that enables data engineers to leverage the power of Apache Spark to transform their data at scale and build out a robust lakehouse architecture.

What is a lakehouse?

A lakehouse is a new paradigm that combines the best of both worlds: the data lake and the data warehouse. A data lake is a repository of raw data in various formats, such as tabular, unstructured, images, IoT sensors and more. A data warehouse is a structured database that stores processed and aggregated data for analytics and reporting purposes.

A lakehouse allows data engineers to ingest, process and share data in an open format, such as Delta lake, without having to move or copy data across different systems. This reduces complexity, cost and latency, while improving performance, reliability and consistency.

How does Synapse Data Engineering enable the lakehouse?

Synapse Data Engineering is one of the core experiences of Microsoft Fabric, a platform that empowers teams of data professionals to seamlessly collaborate on their analytics projects, ranging from data integration to data warehousing, data science and business intelligence.

With Synapse Data Engineering, data engineers can easily create and work with the lakehouse as a first-class item in the workspace. They can choose from various ways of bringing data into the lakehouse, such as dataflow & pipelines, or use shortcuts to create virtual folders and tables without the data ever leaving their storage accounts. They can also use Apache Spark notebooks or SQL scripts to transform their data at scale using familiar languages and frameworks.

The lakehouse also streamlines the process of collaborating on top of the same data with other Fabric workloads, such as Synapse Data Warehouse ¹ and Synapse Data Science ². Data engineers can share their processed data with other teams in a secure and governed way, without having to worry about data duplication or synchronization. They can also query and cross-join data from different sources using SQL or Spark SQL.

What are the benefits of Synapse Data Engineering?

Synapse Data Engineering offers many benefits for data engineers and their organizations, such as:

Simplified data management: no need to deal with multiple products or systems to ingest, process and share data.
Open data standards: no vendor lock-ins or proprietary formats; data is stored in Delta lake format in Microsoft OneLake, providing interoperability with other Fabric workloads and the Spark ecosystem.
Scalable performance: no need to provision or manage clusters; resources are allocated on-demand based on the workload requirements.
Cost efficiency: pay only for what you use; resources are automatically scaled up or down based on the demand.
Security and governance: inherit the same security policies and governance rules from Microsoft OneLake; control access to your data at granular levels.