Real-time data processing is becoming more common in companies of all sizes. The use cases range from simple stream ingestion to complex machine learning pipelines. If you need to get started with streaming in Azure, Stream Analytics gives you a simple way to get up and running. Most of my streaming projects involve Apache Kafka and Spark which can take a lot of setup (or at least involving additional vendors to simplify the experience). Those technologies are great especially for challenging streaming pipelines, but if your data platform is within Azure you should consider if Stream Analytics will meet your needs.
Spark .NET is the C# API for Apache Spark - a popular platform for big data processing. This demo is for you if you are curious to see a sample Spark .NET program in action or are interested in seeing Azure Synapse serverless Apache Spark notebooks. This demo includes guidance of how you can follow along to build a Spark .NET data load that reads linked sample data, transforms data, joins to a lookup table, and saves as a Delta Lake file to your Azure Data Lake Storage Gen2 account.
As a data engineer, you should not be trying to convince your colleagues that everything can be a scheduled batch job. It's time to learn how to building streaming data pipelines. For many data engineers, Apache Kafka is the go to platform for enabling real-time data pipelines. Let's quickly cover why and how to get started.
Data engineer roles vary but some core traits stand out for any data engineer. If you missed it, check out my first posts in this series on What is a Data Engineer? and Data Engineer Skills for Success. Let's finish off this series with the traits I see as most critical for success as a data engineer.
Data engineers job descriptions vary significantly as they are asked to work on many different projects. Yet, there are categories of skills that are consistently desired in a data engineer and serve as a foundation for learning new technologies. Here are the skills I see as most critical for success as a data engineer.
Hearing a lot of mention of Data Lakes but still not sure what that means or why anyone cares? This video will cover a brief introduction to what a Data Lake is and why so many organizations are adding them to their analytics ecosystem. To show what interacting with a data lake may look like for a typical data analyst, I included a demo of how you would use Spark SQL to query the data lake from Azure Databricks.
Managing big data is critical for many organizations. Analytics can improve products and inform critical business decisions. Using data can provide distinct advantages, and it’s likely that an organization’s competitors are already leveraging their data.
Dustin Vannoy is a consultant in data analytics and engineering. His specialties are modern data pipelines, data lakes, and data warehouses. He loves to share knowledge with the data science community.
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