It makes it easier to manage, track, and update machine learning models deployed from the cloud to the edge. ![]() With the built-in Azure Machine Learning, Databricks gives you access to advanced automated machine learning capabilities that enable you to find suitable algorithms and hyperparameters with great speed. Additionally, you can integrate and transform data using the traditional Data Factory interface through Azure Synapse Pipelines. Due to Azure's global scalability and availability, you can easily create clusters and build in a fully managed Apache Spark environment.ĭata Factory powers Azure Synapse Analytics, which enables you to ingest data from on-premises, hybrid, and multi-cloud sources and transform it using robust data flows. With Azure Databricks, you can seamlessly integrate with open source libraries and access the most recent versions of Apache Spark. You can select over 90 pre-built connectors to get data from enterprise data warehouses like Oracle Exadata and Teradata SaaS applications like Salesforce, Marketo, and ServiceNow Big Data sources like Amazon Redshift, Google BigQuery, and HDFS, etc. With a single pay-as-you-go service, Azure Data Factory enables you to ingest data from various sources. Since Databricks supports Spark clusters, it can handle Big Data more efficiently than Data Factory and connect to various data sources available on-premise. While its initial Copy Activity employs integration run-times instead of Spark clusters and provides connectivity to the on-site SQL Servers, ADF's Mapping Data Flows do not support connecting to the currently available databases on-premise. It includes Graphical User Interface (GUI) capabilities that enable faster program delivery.ĭatabricks carries out various Data Engineering and Data Science tasks employing notebooks using Python, Spark, R, Java, or SQL.Īlthough Azure Data Factory uses GUI tools to streamline the ETL pipeline process, developers have less freedom because they cannot change the backend code and, thus, take longer to finish a task that requires any code modification.ĭevelopers in Databricks have the freedom to tweak their code and activities using a variety of performance optimization techniques to enhance data processing capabilities. ![]() ADF offers a drag-and-drop option for visually creating and maintaining data pipelines.
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