Building Data Quality pipelines with Apache Spark and Delta Lake

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Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports. With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data. Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.

Data & Analytics

Building Data Quality
pipelines with Apache
Spark and Delta Lake
Sandy May & Darren Fuller
Lead Data Engineers
Elastacloud

Speaker Bio
Sandy May - @spark-spartan
Databricks Champion
Data Science London Co-Organizer
Tech speaker across the UK
Passionate about Apache Spark,
Databricks, AI, Data Security and
Reporting platforms in Microsoft
Azure

Speaker Bio
Darren Fuller - @dazfuller
Databricks Champion
Tech speaker across the UK
Passions include Apache Spark,
Microsoft Azure, Raspberry Pi

Agenda
Sandy May
What is the problem? What do we
need? How can we make it easy to
use?
Darren Fuller
How can we investigate? Where
do we go from here? What have
we learnt

What is the problem?
• Harvard Business review suggested Dirty Data cost US companies $3
trillion in 2017
• Business data is hard to clean generically, it often requires domain
knowledge
• Dirty Data can be frustrating for Data Scientists and BI Engineers
• In the worst case, Dirty Data can provide incorrect reports and predictions
leading to potential significant losses
@dazfuller @spark_spartan

Should we Build or Buy?
§ Own the IP
§ Prioritise the features you want
§ Built for your use case
§ No licence fees
§ Use your core technology
§ May have track record
§ Bugs fixed by vendor
§ Features not thought about by
business
§ Service Level Agreements
Buy
Build
@dazfuller @spark_spartan

Key Design Decisions
▪ Support to Run Cross Cloud
▪ Use Native tools in Azure and AWS
▪ Easy for SQL Devs to write rules
▪ Single Reporting Platform
▪ Capability to reuse custom business rules
▪ Run as part of our Data Ingestion Pipelines with Delta Lake
@dazfuller @spark_spartan

Enterprise Data Warehouse
@dazfuller @spark_spartan

Let’s Build it!
@dazfuller @spark_spartan

Conclusions
▪ Building can be quick and effective
▪ Prioritise your own business needs, you know your data best
▪ Can be used as a stop gap while you create a service for an off the shelf
product
▪ Easy to run as part of ingestion pipelines
▪ Business value in reports and reuse of rules
▪ Use Delta Lake for Schema Evolution
@dazfuller @spark_spartan

Thanks for listening!
Questions?
@dazfuller @spark_spartan

Feedback
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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

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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

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Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

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

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Building Data Quality pipelines with Apache Spark and Delta Lake

1. Building Data Quality pipelines with Apache Spark and Delta Lake Sandy May & Darren Fuller Lead Data Engineers Elastacloud

2. Speaker Bio Sandy May - @spark-spartan Databricks Champion Data Science London Co-Organizer Tech speaker across the UK Passionate about Apache Spark, Databricks, AI, Data Security and Reporting platforms in Microsoft Azure

3. Speaker Bio Darren Fuller - @dazfuller Databricks Champion Tech speaker across the UK Passions include Apache Spark, Microsoft Azure, Raspberry Pi

4. Agenda Sandy May What is the problem? What do we need? How can we make it easy to use? Darren Fuller How can we investigate? Where do we go from here? What have we learnt

5. Data Quality Overview

6. What is the problem? • Harvard Business review suggested Dirty Data cost US companies $3 trillion in 2017 • Business data is hard to clean generically, it often requires domain knowledge • Dirty Data can be frustrating for Data Scientists and BI Engineers • In the worst case, Dirty Data can provide incorrect reports and predictions leading to potential significant losses @dazfuller @spark_spartan

7. Should we Build or Buy? § Own the IP § Prioritise the features you want § Built for your use case § No licence fees § Use your core technology § May have track record § Bugs fixed by vendor § Features not thought about by business § Service Level Agreements Buy Build @dazfuller @spark_spartan

8. Key Design Decisions ▪ Support to Run Cross Cloud ▪ Use Native tools in Azure and AWS ▪ Easy for SQL Devs to write rules ▪ Single Reporting Platform ▪ Capability to reuse custom business rules ▪ Run as part of our Data Ingestion Pipelines with Delta Lake @dazfuller @spark_spartan

9. Enterprise Data Warehouse @dazfuller @spark_spartan

10. Let’s Build it! @dazfuller @spark_spartan

11. Summing Up @dazfuller @spark_spartan

12. Conclusions ▪ Building can be quick and effective ▪ Prioritise your own business needs, you know your data best ▪ Can be used as a stop gap while you create a service for an off the shelf product ▪ Easy to run as part of ingestion pipelines ▪ Business value in reports and reuse of rules ▪ Use Delta Lake for Schema Evolution @dazfuller @spark_spartan

13. Thanks for listening! Questions? @dazfuller @spark_spartan

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

Building Data Quality pipelines with Apache Spark and Delta Lake

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