Building End-to-End Delta Pipelines on GCP

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Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years. Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines. In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.

Data & Analytics

Building End-to-End Delta Pipelines
on GCP
Himanish Kushary,
Practice Lead @Databricks
Molly Nagamuthu,
Sr Resident Solutions Architect@Databricks

Agenda
▪ Delta Lake Overview
▪ Delta Lake on GCP
▪ Reference Architecture
▪ Demo
▪ Questions

Who are we ?
Practice Lead @
Databricks
Sr Resident Solutions
Architect@ Databricks
Himanish Kushary Molly Nagamuthu

Reliability Challenges
Hard to append data
✗
Modification of
existing data diﬀicult
Jobs failing mid way
Real-time operations hard
Costly to keep historical
versions of the data

Difficult to handle large metadata
Performance Challenges
Hard to get great performance
✗
"Too many files” problems

Governance and Data Quality Challenges
Lack of transactional
traceability
Fine grained access control
diﬀicult
Data quality Issues

Open Source & Open Format
Adds Reliability & Performance
Best of data warehouse and data
lakes
Fully Compatible with Spark APIs
A New Standard for Building Data Lakes

Data Lake : Reality to the Ideal
● Efficient Upserts
● Data Versioning
● Time Travel
● ACID Transactions
● Schema Enforcement
● Data Quality Checks
● Scalable Metadata Handling
● Streaming/Batch Uniﬁcation
● Small ﬁle compaction
DATA LAKE DELTA LAKE

Data
Warehouse
Lakehouse
One platform to unify all of
your data, analytics, and AI workloads
Data
Lake

Structured Semi-structured Unstructured Streaming
Lakehouse Architecture
Data Engineering
BI & SQL
Analytics
Real-time Data
Applications
Data Science
& Machine Learning
Data Management & Governance
Open Data Lake

Build a Modern Lakehouse Architecture
Semi-Structured
Curated Data
Data
Lake
Raw Data
Unstructured Data Structured
Data Engineering BI and SQL based
Analytics
Dashboards
and reporting
Data Science and
Machine Learning
Streaming
Analytics

Open , reliable and performant foundation
Semi-Structured
Curated Data
Data
Lake
Raw Data
Unstructured Data Structured
Data Engineering BI and SQL based
Analytics
Dashboards
and reporting
Data Science and
Machine Learning
Streaming
Analytics

Select the Right Tool
Semi-Structured
Curated Data
Data
Lake
Raw Data
Unstructured Data Structured
Streaming
Analytics
BI and SQL based
Analytics
Dashboards and
Reporting
Data Science and
Machine Learning
Data Engineering

Simplify the Architecture
Curated Data
ETL/ELT, Streaming, SQL Analytics on
Data Lake, Data Science and ML
SQL Analytics and BI
Raw Data
Semi-Structured Unstructured Data
Structured
Reporting and
Dashboards
Data
Lake

Delta Lake Reference Architecture
Uniﬁed architecture that
facilitates:
● Reliability and
performance of batch and
streaming ETL and ML at
scale
● Interactive Data Science
and production machine
learning
● Streamline collaboration
across the whole data
ecosystem

Databricks on Google Cloud
A jointly developed service for data engineering, data science and analytics
Optimized Performance
Databricks on Google Kubernetes Engine is the ﬁrst Kubernetes-based Databricks runtime for
performant, scalable runtime
Streamlined Integrations
Faster, easier data access with built-in connectors to all Google Cloud
including integrations to Google Data Analytics (BigQuery, Pub/Sub,
Looker) as well Cloud Infrastructure and storage.
Integrated Security & Administration
1-click access to Databricks from the Google Cloud Console with SSO, Identity
Passthrough and SCIM support
Innovate Faster with a Uniﬁed, Fully Integrated Platform for all Analytics
Make data lakes more Reliable and Scalable
Collaborate & Simplify DS/ML at Scale

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

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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

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Data Lakehouse Symposium | Day 2

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

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

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