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Synapse Analytics and the Differences for Data Engineers and Data Scientists

Updated: May 26

In the ever-evolving world of data, professionals need powerful tools to manage and analyse vast amounts of information efficiently. 


Azure Synapse Analytics is a powerful cloud-based data warehousing and analytics tool designed to meet the needs of data engineers, data scientists, and other data professionals who work with big data. It integrates seamlessly with the broader Azure ecosystem, providing a unified environment for data integration, exploration, and analysis.


One of its core strengths is its exceptional scalability, which allows organisations to dynamically allocate and manage computing resources based on their current workload requirements. This elastic scaling capability ensures that organisations can access immense computational power during peak times, such as during complex data transformations, large-scale queries, or machine learning model training, and then scale down during periods of lower demand to optimise cost efficiency.


With more and more companies understanding the necessity of having cloud-based data platforms, it’s natural that more data professionals will be needed in the future, and so it’s fundamental to know the key differences between its usage for Data Engineers and Data Scientists (the main roles that Synapse was designed for).

 

Data Engineer

A Data Engineer will design a company's data infrastructure and will also be responsible for the development and upkeep of all necessary processes to extract, transform, and load that data. And for them, Synapse Analytics fits very well as a daily instrument to perform all those functions.


Pipelines are designed to build ETL or ELT workloads easily and are naturally the main feature used by a Data Engineer. It can ingest data from a variety of sources, including on-premises databases, cloud storage (e.g., Azure Data Lake Storage, Azure Blob Storage), and external data sources.


The drag-and-drop interface is made for easy building of complex transformations, and the native integration with Azure Data Lake Storage Gen2 makes the load stage very fast and reliable.


SQL Scripts and Notebooks, which support multiple languages like Python, Scala, Java, R and .NET, are also frequently used for programming other tasks that will complement the pipeline workflow.


All the management of Triggers and Schedules as well as the Data Governance and Security are usually made by Data Engineers and Synapse Analytics has a variety of configurations that can help data be continuously processed with security.

 

Data Scientists

Usually, Data Scientists will work with data prepared by Data Engineers. Their goal is to explore the data, utilising their expertise in statistics and programming, to extract meaningful insights from it. And because Synapse Analytics is also designed for them, it’s easy to have an integrated ecosystem where all the data teams can work together, reducing the costs of having multiple tools. While Data Scientists sometimes need to build ETL or ELT processes, their skills will appear when using other features.


Notebooks are something that Data Scientists will use frequently, but different from Data Engineers, they will use them for a deep analysis of the data. The Serverless SQL Pools can execute queries with large data throughput without requiring complex provisioning. This is a key advantage for Data Scientists as they can work with different databases, so they don’t need to have a powerful computational capacity on each one of those servers. Notebooks can show results as graphs, which helps them to have a good overview of all the data to find patterns, for example.


Synapse Analytics has a native integration with Azure Machine Learning. Hence, its usage is very easy to configure. Still, with Apache Spark Pools, Data Scientists can use Synapse to train and score using various algorithms and libraries, that can help solve most classical machine learning problems.

  


In conclusion, Synapse Analytics offers powerful tools for both Data Engineers and Data Scientists, streamlining data management and enhancing analytical capabilities. For Data Engineers, Synapse Analytics simplifies the integration and processing of large data sets, making data pipelines more efficient. For Data Scientists, it provides robust platforms for advanced analytics and scalable machine learning. Understanding these main uses not only clarifies the differences between the roles but also highlights how Synapse Analytics can optimise your data strategy. 

 

We hope this guide helps you better leverage Synapse Analytics to unlock the full potential of your data.


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by Alessandro Melo

@ Passio Consulting

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