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Category: Spark

PHI De-Identification in Databricks with NLP

Published 2022-06-24 by Kevin Feasel

Amir Kermany, et al, share a set of notebooks:

John Snow Labs, the leader in Healthcare natural language processing (NLP), and Databricks are working together to help organizations process and analyze their text data at scale with a series of Solution Accelerator notebook templates for common NLP use cases. You can learn more about our partnership in our previous blog, Applying Natural Language Processing to Health Text at Scale.

To help organizations automate the removal of sensitive patient information, we built a joint Solution Accelerator for PHI removal that builds on top of the Databricks Lakehouse for Healthcare and Life Sciences. John Snow Labs provides two commercial extensions on top of the open-source Spark NLP library — both of which are useful for de-identification and anonymization tasks — that are used in this Accelerator:

This is a really interesting scenario.

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Git Native Support for Databricks Workflows

Published 2022-06-23 by Kevin Feasel

Vaibhav Sethi and Roland Faeustlin make an announcement:

We are happy to announce native support for Git in Databricks Workflows, which enables our customers to build reliable production data and ML workflows using modern software engineering best practices. Customers can now use a remote Git reference as the source for tasks that make up a Databricks Workflow, for example, a notebook from the main branch of a repository on GitHub can be used in a notebook task. By using Git as the source of truth, customers eliminate the risk of accidental edits to production code. They also remove the overhead of maintaining a production copy of the code in Databricks and keeping it updated, and improve reproducibility as each job run is tied to a commit hash. Git support for Workflows is available in Public Preview and works with a wide range of Databricks supported Git providers including GitHub, Gitlab, Bitbucket, Azure Devops and AWS CodeCommit.

Read on to see how it works.

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Improving Join Performance with Skewed Datasets in Spark

Published 2022-06-22 by Kevin Feasel

Ajay Gupta gets into the topic of join performance:

Performing Joins on Skewed Datasets: A Dataset is considered to be skewed for a Join operation when the distribution of join keys across the records in the dataset is skewed towards a small subset of keys. For example when 80% of records in the datasets contribute to only 20% of Join keys.

Implications of Skewed Datasets for Join: Skewed Datasets, if not handled appropriately, can lead to stragglers in the Join stage (Read this linked story to know more about Stragglers). This brings down the overall execution efficiency of the Spark job. Also, skewed datasets can cause memory overruns on certain executors leading to the failure of the Spark job. Therefore, it is important to identify and address Join-based stages where large skewed datasets are involved.

Read on for five techniques which may help you out.

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Delta Live Tables and Power BI Data Modeling

Published 2022-06-21 by Kevin Feasel

Tahir Fayyaz goes from Delta Lake to Power BI:

To get the optimal performance from Power BI it is recommended to use a star schema data model and to make use of user-defined aggregated tables. However, as you build out your facts, dimensions, and aggregation tables and views in Delta Lake, ready to be used by the Power BI data model, it can become complicated to manage all the pipelines, dependencies, and data quality as you need to consider the following:

– How to easily develop and manage the data model’s transformation code.

– How to run and scale data pipelines for the model as data volumes grow.

– How to keep all the Delta Lake tables updated as new data arrives.

– How to view the lineage for all tables as the model gets more complex.

– How to actively stop data quality issues that result in incorrect reports.

Read on for recommendations, a couple architectural diagrams, and some sample code.

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Unity Catalog in Azure Databricks

Published 2022-06-10 by Kevin Feasel

Paul Roome, et al, announce Unity Catalog:

We are excited to announce that data lineage for Unity Catalog, the unified governance solution for all data and AI assets on lakehouse, is now available in preview.

This blog will discuss the importance of data lineage, some of the common use cases, our vision for better data transparency and data understanding with data lineage, and a sneak peek into some of the data provenance and governance features we’re building.

Click through to see what it currently supports. My curious question is around whether this and Microsoft Purview will play nice in an Azure Databricks setup.

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Ingesting Event Hub Telemetry Data with PySpark Streaming

Published 2022-06-06 by Kevin Feasel

Charles Chukwudozie shows how to read from Event Hubs in Databricks with Python:

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.

Most documented implementations of Azure Databricks Ingestion from Azure Event Hub Data are based on Scala.

So, in this post, I outline how to use PySpark on Azure Databricks to ingest and process telemetry data from an Azure Event Hub instance configured without Event Capture.

Click through for the process.

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Processing Security Logs in Databricks with Delta Live Tables

Published 2022-06-06 by Kevin Feasel

Silvio Fiortio ingests some data:

Databricks recently introduced Workflows to enable data engineers, data scientists, and analysts to build reliable data, analytics, and ML workflows on any cloud without needing to manage complex infrastructure. Workflows allows users to build ETL pipelines that are automatically managed, including ingestion, and lineage, using Delta Live Tables. The benefits of Workflows and Delta Live Tables easily apply to security data sources, allowing us to scale to any volume or latency required for our operational needs.

In this article we’ll demonstrate some of the key benefits of Delta Live Tables for ingesting and processing security logs, with a few examples of common data sources we’ve seen our customers load into their cyber Lakehouse.

Click through to learn more.

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Data Modeling with Spark–Breaking Data into Multiple Tables

Published 2022-06-02 by Kevin Feasel

Landon Robinson tokenizes data:

The result of joining the 2 DataFrames – pets and colorsdisplays the nickname, color and age of the pets. We went from a normalized dataset where common & recurring values weresubstituted for numeric representation s— to a slightly more denormalized dataset. Let’s keep going!

This is an interesting example of a useful technique but I strongly disagree with Landon about whether this is normalization. Translating a natural key to a surrogate key is not normalizing the data and translating a surrogate key to a natural key (which is what the example does) is not denormalizing the data. A really simplified explanation of the process is that normalization is ensuring that like things are grouped together, not that we build key-value lookup tables for everything. That’s why Landon’s “denormalized” example is just as normalized as the original: each of those attributes describes a unique thing about the pet identified by its (unique) nickname. This would be different if we included things like owner’s name (which could still be on that table), owner’s age, owner’s height, a list of visits to the vet for each pet, when the veterinarians received their licenses, etc.

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Monitoring Streaming Queries in PySpark

Published 2022-05-31 by Kevin Feasel

Hyukjin Kwon, et al, lay out some monitoring advice:

Streaming is one of the most important data processing techniques for ingestion and analysis. It provides users and developers with low latency and real-time data processing capabilities for analytics and triggering actions. However, monitoring streaming data workloads is challenging because the data is continuously processed as it arrives. Because of this always-on nature of stream processing, it is harder to troubleshoot problems during development and production without real-time metrics, alerting and dashboarding.

Read on to see how you can use the Observable API for alerting in PySpark—previously, it had been a Scala-only API.

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