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- 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: Apache Spark
Write Data from Azure Databricks to Azure Dedicated SQL Pool(formerly SQL DW) using ADLS Gen 2.
In this post, I will attempt to capture the steps taken to load data from Azure Databricks deployed with VNET Injection (Network Isolation) into an instance of Azure Synapse DataWarehouse deployed within a custom VNET and configured with a private … Continue reading
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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Build a Jar file for the Apache Spark SQL and Azure SQL Server Connector Using SBT.
The Apache Spark Azure SQL Connector is a huge upgrade to the built-in JDBC Spark connector. It is more than 15x faster than generic JDBC connector for writing to SQL Server. In this short post, I articulate the steps required … Continue reading
Posted in Unified Analytics
Tagged Apache Spark, Azure Databricks, Azure Databricks Cluster, Microsoft, sbt, Spark, sql-spark-connector, Unified Analytics
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Configure a Databricks Cluster-scoped Init Script in Visual Studio Code.
Databricks is a distributed data analytics and processing platform designed to run in the Cloud. This platform is built on Apache Spark which is currently at version 2.4.4. In this post, I will demonstrate the deployment and installation of custom … Continue reading
Data Preparation of PySpark Dataframes in Azure Databricks Cluster using Databricks Connect.
In my limited experience with processing big data workloads on the Azure Databricks platform powered by Apache Spark, it has become obvious that a significant part of the tasks are targeted towards Data Quality. Data quality in this context mostly … Continue reading
Posted in Azure Databricks
Tagged Apache Spark, APIs, CSV, Databricks-Connect, DataCompy, Dataframes, Jupyter Notebook, PySpark, Python, Python Virtual Environmet, Venv
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Chinny Chukwudozie, Cloud Solutions. 