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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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Author Archives: jbernec
Designing and Implementing a Modern Data Architecture on Azure Cloud.
I just completed work on the digital transformation, design, development, and delivery of a cloud native data solution for one of the biggest professional sports organizations in north America. In this post, I want to share some thoughts on the … Continue reading
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
Publish PySpark Streaming Query Metrics to Azure Log Analytics using the Data Collector REST API.
Overview. At the time of this writing, there doesn’t seem to be built-in support for writing PySpark Structured Streaming query metrics from Azure Databricks to Azure Log Analytics. After some research, I found a work around that enables capturing the … Continue reading
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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Setting Up Jupyter Notebook to Run in a Python Virtual Environment.
1) Install Jupyter on the local machine outside of any existing Python Virtual environment: pip install jupyter –no-cach-dir 2) Create a Python Virtual environment. mkdir virtualenv cd virtualenv python.exe -m venv dbconnect 3) Change directory into the virtual environment and … Continue reading
Posted in Python
Tagged Jupyter Kernel, Jupyter Notebook, Python Virtual Environmet, Venv
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Programmatically Provision an Azure Databricks Workspace and Cluster using Python Functions.
Azure Databricks is a data analytics and machine learning platform based on Apache Spark. The first set of tasks to be performed before using Azure Databricks for any kind of Data exploration and machine learning execution is to create a … Continue reading
Chinny Chukwudozie, Cloud Solutions. 