![]() ![]() Run a Snowflake query and extract the results into Azure Storage container or S3 bucket (AKA Data Unloading). ![]() Copy data from an Azure Storage container or S3 bucket into a Snowflake table (AKA Data Loading).Specifically, the connector will be able to: We will create a connector that can be used by Azure Data Factory to move data into Snowflake as well as being able to run Snowflake queries and export the results. In this blog post I will walk you through the process of doing just that. ![]() While we wait for an official connector from Microsoft we have no alternative but to roll our own. If, like me, you are a fan of Azure Data Factory and love Snowflake then you are probably disappointed that there isn't a native Data Factory connector for Snowflake. You may still find this post useful if you are doing anything out-of-the-box, but otherwise I would recommend using the official connector. Update, June 2020: Since writing this post Microsoft has announced an official Snowflake connector. The notebook can be accessed from here.By Jess Panni Principal I 25th April 2019 The predicted data is stored into Snowflake. The python libraries are imported into Snowflake using the stage and the model is deployed into Snowflake as a UDF to run batch inference on a random set of records. The model is trained using Random Forest Classifier from skikit-learn and evaluated using the confusion matrix. The transformed features are written back to a Snowflake table, so they can be utilized by the model. EDA is then applied to get a sense of the data and feature engineering is used to transform the data into features which can be processed by the model. The Data Source is Snowflake tables accessed using the python Snowflake connector. The notebook uses a classification model that applies the data science process lifecycle. The notebook uses a simple banking dataset and leverages Azure ML notebook and compute to connect to Snowflake and use the data to train the model and deploy it using Snowpark UDF to run batch inference. We have created a simple Azure ML notebook to demonstrate the various integration capabilities. The Snowpark function can be triggered by Azure ML for processing. The model can be prepared and trained in Azure ML to deploy into Snowflake using Snowpark. Snowpark can be leveraged by Azure ML to deploy ML models into Snowflake. It supports pushdowns for all operations, including Snowflake UDFs and all the computations are done within Snowflake. Snowpark provides the ability to execute ML workloads in Snowflake. The Python connector can be used in Notebook as well as Pipelines for real-time and batch endpoints. The Snowflake Python connector supports push down to execute queries directly in Snowflake utilizing Snowflake’s compute. Snowflake Python connector can be used to connect to Snowflake directly and consume the data in Azure ML. Azure Data Factory can also be used to push the processed data back into Snowflake.Ĭonnecting via Snowflake Python Connector Azure Data Factory would orchestrate the flow and can trigger Azure ML Notebooks and Pipelines. This can then be registered as a Data set and utilized in Azure ML. Notebooks can be used for Ad-hoc analytics and Pipelines can be used for ML Pipelines.Īzure Data Factory can be leveraged to connect to Snowflake, pull in the data from Snowflake and staged into Azure ML Datastore (Data Lake). Azure ML can interface with Snowflake in both Notebooks as well as Pipelines. By using the best of both technologies Customers can now develop and deploy ML models in a secure enterprise ready collaborative environment. Snowflake Data Cloud supports advanced workloads like Artificial intelligence and Machine learning enabling enterprises to have a single place to instantly access all the relevant data by having a single point of global network of trust data and providing native support for structured, semi-structured (JSON, Avro, ORC, Parquet, or XML), and unstructured data.Ĭustomers can leverage the power of Azure Machine learning with Snowflake utilizing Snowpark to support various ml-driven data science use-cases like Forecasting, Prediction, etc. Azure Machine learning can be used to create advanced models using open-source technologies and the MLOps tools help with monitor, retrain, and deploy models. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models and manage MLOps. Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. ![]()
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