Learn to Use Databricks for Data Science

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Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Dados e análise

Learn to Use Databricks
for Data Science
Sean Owen, Principal Solutions Architect
Austin Ford, Sr. Product Manager

Data Science is a tough job
▪ Today, companies are becoming
more and more data-driven, and
the ones getting the most out of
their data will be the ones to
succeed
▪ As a result, Data Science is now a
core capability of many
businesses
▪ Unfortunately, it comes with a
challenging, complex workﬂow at
scale

What does a data science workﬂow look like?
I need the correctly sized compute
resource for my task
I need to be able to ﬁnd and access
the right data sources to fuel my
analysis
I need to be sure my toolbox is ready
with the packages and libraries
required for my work
1. Setup
I’ve been given a business
question to answer with data.
Before I can even get started on
the data science, I need to set
up my development
environment.

What does a data science workﬂow look like?
I uncover insights through
statistical inference, modeling, or
other methods
I start with exploratory data
analysis to familiarize myself with
the data and form hypotheses
I synthesize the results of my work
and the answers to the original
business question
2. Data Science
Once the initial overhead of
setup is complete, the real work
begins.
At any point, I could be sent
back to the Setup phase to add
another data source, change
the size of my compute
resource, or pull in another
library.

What does a data science workﬂow look like?
I share the results with my business
stakeholders via email or Slack
I formulate the results into a report
or dashboard so they can be
consumed
I get feedback about my work from
my stakeholders and iterate with
them to have the biggest impact
3. Sharing Results
The most important step comes
once I ﬁnish the analysis:
sharing the results with my
stakeholders.

Our answer: The Databricks Lakehouse Platform
We want to remove the overhead so
you can focus on the most important
part of your work — data science

Structured Semi-structured Unstructured Streaming
BI &
SQL Analytics
Machine Learning
Real-time Data
Applications
Data Management & Governance
Open Data Storage
Data Science &
Engineering
Lakehouse Platform
Simple | Open | Collaborative
Reliable | Scalable | Secure

Databricks makes setup easy
1. Setup
The Lakehouse brings all
your company’s data
together into a single place
so you don’t have to go
digging through a variety of
data sources
Easily choose the right
compute resource for your
task and switch as needed
single-machine VMs
GPUs Spark clusters
Databricks’ runtimes come
prepackaged with the most
common data science tools,
and customization is easy
Add Python libraries on top of a
runtime with a single line of code

Databricks has the tools to enable you to focus on
your work
2. Data Science
Multi-language,
collaborative notebooks
with co-presence,
commenting, and co-editing
Built-in visualizations that
take you from raw data to
insights in two clicks
Auto-logged revision
history and a git integration
to ensure reproducibility and
enable version control

Databricks lets your share results and iterate
quickly
3. Sharing Results
Easily share your notebooks
with stakeholders, who can
view them as reports
Create a dashboard directly
from your notebook’s results
Iterate with your
stakeholders directly in the
notebook through comments
and co-presence

Getting practical: hands-on with an expert
Sean Owen
Principal Solutions Architect

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Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Databricks

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

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

Databricks

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

Sarah: CEO-Finance-Report pipeline seems to be slow today. Why Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days. Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours. Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert. We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example: Tom: I am not seeing any data for today in my Campaign Metrics Dashboard. Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021. This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Databricks

Hyperparameter tuning is critical in model development. And its general form: parameter tuning with an objective function is also widely used in industry. On the other hand, Apache Spark can handle massive parallelism, and Apache Spark ML is a solid machine learning solution. But we have not seen a general and intuitive distributed parameter tuning solution based on Apache Spark, why? Not every tuning problem is on Apache Spark ML models. How can Apache Spark handle general models? Not every tuning problem is a parallelizable grid or random search. Bayesian optimization is sequential, how can Apache Spark help in this case? Not every tuning problem is single epoch, deep learning is not. How to fit algos such as hyperband and ASHA into Apache Spark? Not every tuning problem is a machine learning problem, for example simulation + tuning is also common. How to generalize? In this talk, we are going to show how using Fugue-Tune and Apache Spark together can eliminate these painpoints Fugue-Tune like Fugue, is a “super framework” – an absraction layer unifying existing solutions such as Hyperopt and Optuna It firstly models the general tuning problems, independent from machine learning It is designed for both small and large scale problems. It can always fully parallelize the distributable part of a tuning problem It works for both classical and deep learning models. With Fugue, running hyperband and ASHA becomes possible on Apache Spark. In the demo, you will see how to do any type of tuning in a consistent, intuitive, scalable and minimal way. And you will see a live demo of the amazing performance.

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

Databricks

When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed. As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations. In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment

Infrastructure Agnostic Machine Learning Workload Deployment

Databricks

This presentation will explore the new work in Spark 3.1 adding the concept of graceful decommissioning and how we can use this to improve Spark’s performance in both dynamic allocation and spot/preemptable instances. Together we’ll explore how Spark’s dynamic allocation has evolved over time, and why the different changes have been needed. We’ll also look at the multi-company collaboration that resulted in being able to deliver this feature and I’ll end with encouraging pointers on how to get more involved in Spark’s development.

Improving Apache Spark for Dynamic Allocation and Spot Instances

Databricks

With data as a valuable currency and the architecture of reliable, scalable Data Lakes and Lakehouses continuing to mature, it is crucial that machine learning training and deployment techniques keep up to realize value. Reproducibility, efficiency, and governance in training and production environments rest on the shoulders of both point in time snapshots of the data and a governing mechanism to regulate, track, and make best use of associated metadata. This talk will outline the challenges and importance of building and maintaining reproducible, efficient, and governed machine learning solutions as well as posing solutions built on open source technologies – namely Delta Lake for data versioning and MLflow for efficiency and governance.

Importance of ML Reproducibility & Applications with MLfLow

Databricks

Mais de Databricks (20)

Data Lakehouse Symposium | Day 2

Democratizing Data Quality Through a Centralized Platform

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

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

Infrastructure Agnostic Machine Learning Workload Deployment

Improving Apache Spark for Dynamic Allocation and Spot Instances

Importance of ML Reproducibility & Applications with MLfLow

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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Learn to Use Databricks for Data Science

1. Learn to Use Databricks for Data Science Sean Owen, Principal Solutions Architect Austin Ford, Sr. Product Manager

2. Data Science is a tough job ▪ Today, companies are becoming more and more data-driven, and the ones getting the most out of their data will be the ones to succeed ▪ As a result, Data Science is now a core capability of many businesses ▪ Unfortunately, it comes with a challenging, complex workﬂow at scale

3. What does a data science workﬂow look like? I need the correctly sized compute resource for my task I need to be able to ﬁnd and access the right data sources to fuel my analysis I need to be sure my toolbox is ready with the packages and libraries required for my work 1. Setup I’ve been given a business question to answer with data. Before I can even get started on the data science, I need to set up my development environment.

4. What does a data science workﬂow look like? I uncover insights through statistical inference, modeling, or other methods I start with exploratory data analysis to familiarize myself with the data and form hypotheses I synthesize the results of my work and the answers to the original business question 2. Data Science Once the initial overhead of setup is complete, the real work begins. At any point, I could be sent back to the Setup phase to add another data source, change the size of my compute resource, or pull in another library.

5. What does a data science workﬂow look like? I share the results with my business stakeholders via email or Slack I formulate the results into a report or dashboard so they can be consumed I get feedback about my work from my stakeholders and iterate with them to have the biggest impact 3. Sharing Results The most important step comes once I ﬁnish the analysis: sharing the results with my stakeholders.

6. Our answer: The Databricks Lakehouse Platform We want to remove the overhead so you can focus on the most important part of your work — data science

7. Structured Semi-structured Unstructured Streaming BI & SQL Analytics Machine Learning Real-time Data Applications Data Management & Governance Open Data Storage Data Science & Engineering Lakehouse Platform Simple | Open | Collaborative Reliable | Scalable | Secure

8. Structured Semi-structured Unstructured Streaming BI & SQL Analytics Machine Learning Real-time Data Applications Data Management & Governance Open Data Storage Data Science & Engineering Lakehouse Platform Simple | Open | Collaborative Reliable | Scalable | Secure Our focus today

9. Databricks makes setup easy 1. Setup The Lakehouse brings all your company’s data together into a single place so you don’t have to go digging through a variety of data sources Easily choose the right compute resource for your task and switch as needed single-machine VMs GPUs Spark clusters Databricks’ runtimes come prepackaged with the most common data science tools, and customization is easy Add Python libraries on top of a runtime with a single line of code

10. Databricks has the tools to enable you to focus on your work 2. Data Science Multi-language, collaborative notebooks with co-presence, commenting, and co-editing Built-in visualizations that take you from raw data to insights in two clicks Auto-logged revision history and a git integration to ensure reproducibility and enable version control

11. Databricks lets your share results and iterate quickly 3. Sharing Results Easily share your notebooks with stakeholders, who can view them as reports Create a dashboard directly from your notebook’s results Iterate with your stakeholders directly in the notebook through comments and co-presence

12. Getting practical: hands-on with an expert Sean Owen Principal Solutions Architect

Learn to Use Databricks for Data Science

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