How R Developers Can Build and Share Data and AI Applications that Scale with Databricks and RStudio Connect

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This document discusses how R developers can build and share scalable data and AI applications using RStudio and Databricks. It outlines how RStudio and Databricks can be used together to overcome challenges of processing large amounts of data in R, including limited server memory and performance issues. Developers can use hosted RStudio servers on Databricks clusters, connect to Spark from RStudio using Databricks Connect, and share scalable Shiny apps deployed with RStudio Connect. The ODBC toolchain provides a performant way to connect R to Spark without issues encountered when using sparklyr directly.

Datos y análisis

How R Developers Can Build and
Share Data and AI Applications
that Scale with Databricks and
Rstudio Connect
James Blair, Solutions Engineer, RStudio PBC
Rafi Kurlansik, Sr. Solutions Architect, Databricks

Agenda
Rafi Kurlansik, Databricks
Building Scalable R and Shiny apps with RStudio
and Databricks
James Blair, RStudio PBC
Deploying Scalable Shiny apps with RStudio
Connect and Databricks
Benchmarking performance of Shiny connections
to Spark

How to scale R and Shiny
with RStudio and Databricks

How can we open up the data lake to R users?
▪ Typical development patterns
▪ Local
▪ Cloud / On Prem VM
▪ Challenges with big data
▪ Server memory - can only process so much data in the app itself before crashing R
▪ Performance - even on a powerful VM, eventually see our app get less responsive as we reach 100+ GBs
▪ Managing big data infrastructure - app value must be higher to justify the energy investment
If only there was a technology with a familiar API in R that let our app scale to process 100s of GBs...
Imagine trying to do so with traditional R development...

Scale R Apps with Databricks and RStudio
▪ Development Patterns
▪ Hosted RStudio Server (Pro) on Databricks Cluster
▪ RStudio with remote Spark access using Databricks Connect
▪ Overcoming challenges with big data
▪ Auto-scaling Databricks Spark Clusters - dynamically respond to accommodate larger data processing tasks
▪ Consistently fast performance with Delta Lake and Databricks Runtime
▪ Managed service allows data teams to focus on building data products, not maintaining infrastructure
Databricks Spark, RStudio IDE

RStudio with Databricks Connect
Local RStudio, Remote Spark

ODBC to the Rescue
- The R + ODBC toolchain is robust and stable
- As performant as a native Spark connection
- Easy to migrate code from sparklyr to ODBC
- Spark still does all of the computation
- Databricks provides an optimized Spark ODBC driver

ODBC Performance
Comparing sparklyr against two versions of the Databricks ODBC/JDBC Driver
Collecting Joins

▪ Interactive data analysis with SparkSQL
▪ sparklyr
▪ ODBC
▪ Other Spark APIs
▪ sparklyr
▪ Interactive data analysis with SparkSQL
▪ Shiny with ODBC
▪ Other Spark APIs
▪ ¯_(ツ)_/¯
▪ Deploy models with MLflow?
▪ Submit individual commands with Databricks
REST API 1.2?
Run sparklyr jobs from RStudio on Databricks
with bricksteR?
Stay tuned….
Deploy at scaleDevelop at scale
Conclusion

Additional Resources
▪ Hosted RStudio on Databricks
▪ Databricks Connect
▪ ODBC
▪ ODBC Configuration
▪ RStudio Connect
▪ Sparklyr
▪ blairj09-talks/spark-summit-2020
▪ RafiKurlansik/bricksteR
▪ delta-io/delta
▪ sparklyr/sparklyr
Related ReposDocumentation

Feedback
Your feedback is important to us.
Don’t forget to rate and
review the sessions.

How R Developers Can Build and Share Data and AI Applications that Scale with Databricks and RStudio Connect

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Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models. In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.

Machine Learning CI/CD for Email Attack Detection

Databricks

Más de Databricks (20)

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Machine Learning CI/CD for Email Attack Detection

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How R Developers Can Build and Share Data and AI Applications that Scale with Databricks and RStudio Connect

2. How R Developers Can Build and Share Data and AI Applications that Scale with Databricks and Rstudio Connect James Blair, Solutions Engineer, RStudio PBC Rafi Kurlansik, Sr. Solutions Architect, Databricks

3. Agenda Rafi Kurlansik, Databricks Building Scalable R and Shiny apps with RStudio and Databricks James Blair, RStudio PBC Deploying Scalable Shiny apps with RStudio Connect and Databricks Benchmarking performance of Shiny connections to Spark

4. How to scale R and Shiny with RStudio and Databricks

5. How can we open up the data lake to R users? ▪ Typical development patterns ▪ Local ▪ Cloud / On Prem VM ▪ Challenges with big data ▪ Server memory - can only process so much data in the app itself before crashing R ▪ Performance - even on a powerful VM, eventually see our app get less responsive as we reach 100+ GBs ▪ Managing big data infrastructure - app value must be higher to justify the energy investment If only there was a technology with a familiar API in R that let our app scale to process 100s of GBs... Imagine trying to do so with traditional R development...

6. Scale R Apps with Databricks and RStudio ▪ Development Patterns ▪ Hosted RStudio Server (Pro) on Databricks Cluster ▪ RStudio with remote Spark access using Databricks Connect ▪ Overcoming challenges with big data ▪ Auto-scaling Databricks Spark Clusters - dynamically respond to accommodate larger data processing tasks ▪ Consistently fast performance with Delta Lake and Databricks Runtime ▪ Managed service allows data teams to focus on building data products, not maintaining infrastructure Databricks Spark, RStudio IDE

7. Hosted RStudio Server Pro on Databricks

8. RStudio with Databricks Connect Local RStudio, Remote Spark

9. Sharing Scalable Shiny Apps

10. The Data Science Process

11. Shiny and Spark: A cautionary tale

12. ODBC to the Rescue - The R + ODBC toolchain is robust and stable - As performant as a native Spark connection - Easy to migrate code from sparklyr to ODBC - Spark still does all of the computation - Databricks provides an optimized Spark ODBC driver

13. ODBC with RStudio Connect

14. ODBC Performance Comparing sparklyr against two versions of the Databricks ODBC/JDBC Driver Collecting Joins

15. Sparklyr to ODBC

16. ▪ Interactive data analysis with SparkSQL ▪ sparklyr ▪ ODBC ▪ Other Spark APIs ▪ sparklyr ▪ Interactive data analysis with SparkSQL ▪ Shiny with ODBC ▪ Other Spark APIs ▪ ¯_(ツ)_/¯ ▪ Deploy models with MLflow? ▪ Submit individual commands with Databricks REST API 1.2? Run sparklyr jobs from RStudio on Databricks with bricksteR? Stay tuned…. Deploy at scaleDevelop at scale Conclusion

17. Additional Resources ▪ Hosted RStudio on Databricks ▪ Databricks Connect ▪ ODBC ▪ ODBC Configuration ▪ RStudio Connect ▪ Sparklyr ▪ blairj09-talks/spark-summit-2020 ▪ RafiKurlansik/bricksteR ▪ delta-io/delta ▪ sparklyr/sparklyr Related ReposDocumentation

18. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

How R Developers Can Build and Share Data and AI Applications that Scale with Databricks and RStudio Connect

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