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5 min read
TimeXtender Tuesday - Episode 34
Join our expert Solution Specialist, Frank Wagner, as he shows you how blazing fast it is to connect Apache Spark, Databricks, and TimeXtender. Follow along as he goes through getting the raw data to the data lake, and getting the curated data into the data warehouse to provide business insights to users so they can make confident, data-driven decisions. This process also gives Data Scientists the access and data they need to perform the advanced analyses they need with both curated and raw data, bringing together the best of both worlds.
Great news! Timextender is now FREE! If you are ready to get started modeling data and providing data-based insights to your business you can start your free plan today.
Visit now: https://www.timextender.com/freemium
If you'd like to review previous TimeXtender Tuesdays sessions, check out our TimeXtender Tuesday playlist on YouTube.
Other helpful links Frank mentioned:
•Create Azure Key Vault backed secret scope in Azure Databricks: https://learn.microsoft.com/en-us/azure/databricks/security/secrets/secret-scopes
•Connect Spark to a SQL Server: https://learn.microsoft.com/en-us/azure/databricks/external-data/sql-server
•Read from SQL Server with Spark: https://learn.microsoft.com/en-us/azure/databricks/external-data/sql-server
•Mount Data Lake in Spark/Databricks: https://learn.microsoft.com/en-us/azure/databricks/dbfs/mounts
•Read Parquet files with Azure Databricks: https://learn.microsoft.com/en-us/azure/databricks/external-data/parquet
•Apache PySpark DataFrame API: https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html
•Apache PySpark DataFrame join API: https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.join.html
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