When getting started with Azure Databricks for data processing and analytics, you need to create at least one cluster to get started. Check out the video for a quick overview of how to do this from the Azure Portal. I include a quick description of the options you have and an overview of what cluster… Continue Reading
In the world of data science we often default to processing in nightly or hourly batches, but that pattern is not enough any more. Our customers and business leaders see information is being created all the time and realize it should be available much sooner. While the move to stream processing adds complexity, the tools we have available make it achievable for teams of any size.
This presentation covers why we need to shift some of our workloads from batch data jobs to streaming in real-time. We dive into how Spark Structured Streaming in Azure Databricks enables this along with streaming data systems such as Kafka and EventHub. We will discuss the concepts, how Azure Databricks enables stream processing, and review code examples on a sample data set.
With the shift to data lakes that use distributed file storage as the foundation, we have been missing the reliability that relational databases provides. Databricks Delta is a data management system focused on bringing more reliability and performance into our data lakes. It sits on top of existing storage and the API is very similar to reading and writing to files from Spark already. This session will present the overview of Delta Lake, why it may be a better option than standard data lake storage, and how you can use it from Azure Databricks.
A quick pre-conference post on my top 5 take-aways from PASS Summit 2019 in Seattle.
Slides from my PASS Summit presentation: https://www.slideshare.net/DustinVannoy/passsummit2019azurestorageoptionsforanalytics
I am pleased to share with you a new, improved way of developing for Azure Databricks from your IDE – Databricks Connect! Databricks Connect is a client library to run large scale Spark jobs on your Databricks cluster from anywhere you can import the library (Python, R, Scala, Java). It allows you to develop from your computer with your normal IDE features like auto complete, linting, and debugging. You can work in an IDE you are familiar with but have the Spark actions send out to the cluster, with no need to install Spark locally.
This video we will quickly cover Apache Spark. The goal is to cover why use Spark and where it fits in the data ecosystem. If you want to just get hands on with Spark, check out one of my next videos on Spark and Databricks. Watch the video to get my overview of Spark and… Continue Reading
I hear questions quite frequently about what options are best for data pipelines? Should we write code using Pandas or Spark? Should we use AWS Glue or Azure Data Factory? Or maybe SSIS? Where do Airflow and Luigi fit? I plan to dive into these technologies and provide more clarity into the options we have… Continue Reading
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.