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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: Python
Ingest Azure Event Hub Telemetry Data with Apache PySpark Structured Streaming on Databricks.
Overview. Ingesting, storing and processing millions of telemetry data from a plethora of remote IoT devices and Sensors has become common place. One of the primary Cloud services used to process streaming telemetry events at scale is Azure Event Hub. … 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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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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Automate Azure Databricks Job Execution using Custom Python Functions.
Introduction Thanks to a recent Azure Databricks project, I’ve gained insight into some of the configuration components, issues and key elements of the platform. Let’s take a look at this project to give you some insight into successfully developing, testing, … Continue reading
Chinny Chukwudozie, Cloud Solutions. 