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Category: Spark

Data Sharing and Secure Cleanrooms in Databricks

Published 2022-07-20 by Kevin Feasel

Craig Porteous reviews a couple of announcements from Data + AI Summit:

Having worked with many organisations across different industries and sectors, the sharing of data with partners and vendors is always a pain point and one that all too often results in both parties not quite getting what they want or need. This isn’t restricted to my experience however which is why Databricks announced Delta Sharing back at DATA + AI Summit 2021.

Coming to this year’s conference, Delta Sharing has been established as the foundation for many new features with the announcement Databricks Marketplace and Cleanrooms for example, both built upon the Delta Sharing protocol. We’ll explore Cleanrooms below and I’ll look at the Databricks Marketplace in it’s own post.

Read on for Craig’s thoughts on two of the bigger announcements at this year’s summit.

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Mounting Data Lake Storage from a Spark Pool

Published 2022-07-19 by Kevin Feasel

Kamil Nowinski runs into some trouble:

Last weekend, I played a bit with Azure Synapse from a way of mounting Azure Data Lake Storage (ADLS) Gen2 in Synapse notebook within API in the Microsoft Spark Utilities (MSSparkUtils) package. I wanted to just do a simple test, hence I followed the documentation from Microsoft: How to use file mount/unmount API in Synapse.
Having an ADLS Account already created in a subscription – should be easy peasy, right?

Read on to understand when things might be a little more complicated than they seem. And more frustrating, once you see the cause of the problem.

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Summarizing Data & AI Summit Announcements

Published 2022-07-05 by Kevin Feasel

Zach Stagers hits the high notes:

One of the biggest cheers of the keynote was that Delta is being fully open sourced! Databricks continue to share their incredible work to help drive our industry forward. Delta already has wide adoption, but with the open sourced version now being levelled up to the same standard as the ‘proprietary’ one, this should help cement it as the default choice for lake-based storage.

There were some announcements of things to come with Delta too, such as a optimised deletes and updates by removing single rows instead of having to completely rewrite the file. It’ll be really interesting to see how this works, and just how much it boosts performance.

Read on for more notes on several big announcements.

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Data Lakehouse Cleanrooms in Databricks

Published 2022-06-30 by Kevin Feasel

Matei Zaharia, et al, announce an interesting idea:

We are excited to announce data cleanrooms for the Lakehouse, allowing businesses to easily collaborate with their customers and partners on any cloud in a privacy-safe way. Participants in the data cleanrooms can share and join their existing data, and run complex workloads in any language – Python, R, SQL, Java, and Scala – on the data while maintaining data privacy.

With the demand for external data greater than ever, organizations are looking for ways to securely exchange their data and consume external data to foster data-driven innovations. Historically, organizations have leveraged data sharing solutions to share data with their partners and relied on mutual trust to preserve data privacy. But the organizations relinquish control over the data once it is shared and have little to no visibility into how data is consumed by their partners across various platforms. This exposes potential data misuse and data privacy breaches. With stringent data privacy regulations, it is imperative for organizations to have control and visibility into how their sensitive data is consumed. As a result, organizations need a secure, controlled and private way to collaborate on data, and this is where data cleanrooms come into the picture.

Read on to learn more about how this all works. It’s definitely a lot better than sending off a bunch of CSVs…

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Data Governance in Databricks with Unity Catalog

Published 2022-06-29 by Kevin Feasel

Paul Roome, et al, announce the upcoming GA for Databricks Unity Catalog:

Today we are excited to announce that Unity Catalog, a unified governance solution for all data assets on the Lakehouse, will be generally available on AWS and Azure in the upcoming weeks. Currently, you can apply for a public preview or reach out to a member of your Databricks account team.

In a previous blog, we set out our vision for a governed lakehouse and how Unity Catalog can help customers simplify governance at scale. This blog will explore the most recent updates to Unity Catalog and our growing partner ecosystem.

Click through for those updates and to sign up for the public preview if so inclined.

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Customer Segmentation via Databricks Solution Accelerator

Published 2022-06-29 by Kevin Feasel

Gavita Regunath discovers customer segments in a dataset:

We will be using the German Credit dataset, a publicly available dataset provided by Dr. Hans Hofmann of the University of Hamburg. The German Credit dataset contains features describing 1000 loan applicants who have taken credit from the bank. Using this dataset, our aim will be to understand the following “How should the bank personalise its products for its customers?”.

Click through to see an example of clustering to generate customer segments.

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Software Engineering Practices for Notebooks

Published 2022-06-27 by Kevin Feasel

Rafi Kurlansik and Austin Ford explain how to get the most out of notebooks, using Databricks as an example:

Notebooks are a popular way to start working with data quickly without configuring a complicated environment. Notebook authors can quickly go from interactive analysis to sharing a collaborative workflow, mixing explanatory text with code. Often, notebooks that begin as exploration evolve into production artifacts. For example,

1. A report that runs regularly based on newer data and evolving business logic.

2. An ETL pipeline that needs to run on a regular schedule, or continuously.

3. A machine learning model that must be re-trained when new data arrives.

Perhaps surprisingly, many Databricks customers find that with small adjustments, notebooks can be packaged into production assets, and integrated with best practices such as code review, testing, modularity, continuous integration, and versioned deployment.

Read on for several tips and recommendations.

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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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