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- Designing and Implementing a Modern Data Architecture on Azure Cloud.
- Ingest Azure Event Hub Telemetry Data with Apache PySpark Structured Streaming on Databricks.
- Publish PySpark Streaming Query Metrics to Azure Log Analytics using the Data Collector REST API.
- Write Data from Azure Databricks to Azure Dedicated SQL Pool(formerly SQL DW) using ADLS Gen 2.
- Incrementally Process Data Lake Files Using Azure Databricks Autoloader and Spark Structured Streaming API.
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Tag Archives: Structured Streaming API
Incrementally Process Data Lake Files Using Azure Databricks Autoloader and Spark Structured Streaming API.
Use Case. In this post, I will share my experience evaluating an Azure Databricks feature that hugely simplified a batch-based Data ingestion and processing ETL pipeline. Implementing an ETL pipeline to incrementally process only new files as they land in … Continue reading
Posted in Azure Databricks
Tagged Analytics, Apache Spark, Apache Spark Connector, Apache Spark JDBC Connector, Autoloader, Azure Data Factory, Azure Data Lake Gen 2, Azure Databricks, Azure Event Grid, Azure SQL DB, Big Data, cloudFiles, CSV, Data, ETL, Ingestion, JSON, Pipeline, PySpark, Python, Queue Service, schema, StructType, Structured Streaming API, udf, Unified Analytics
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Chinny Chukwudozie, Cloud Solutions. 