Scalable Python and SQL Data Engineering without Migraines

Thursday 10:55 in Europium2

Data loading processes are complex and require effort to organize, often different tools are used and seamless processing is not ensured. Learn how to create pipelines efficiently and easily with Python in Snowflake Notebooks. Create and monitor tasks to continuously load data. Use third-party data directly to extend the data model without copying it. Harness the power of Python to quickly calculate values and write efficient stored procedures.

In this session you will see how to

Load Parquet data to Snowflake using schema inference
Setup access to Snowflake Marketplace data
Create a Python UDF to convert temperature
Create a data engineering pipeline with Python stored procedures to incrementally process data
Orchestrate the pipelines with tasks
Monitor the pipelines with Snowsight

Dirk Jung

Dirk Jung has more than 20 years of experience in the IT industry. In his position as Senior Solution Engineer at Snowflake Computing, he supports companies in building modern data and analysis platforms in the cloud. In his professional career, he has held various positions at SAS Institute, Blue Yonder and Datameer, among others. He specializes in business intelligence, predictive analytics and data warehousing.