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Slice & DAIS - Summit Highlights from Data and AI Summit 2021 (former Apache Spark Summit)

Slice & DAIS - Summit Highlights from Data and AI Summit 2021 (former Apache Spark Summit)

Slides for our beginner-friendly, level 250 community session with Data and AI Summit highlights and announcements.

ML updates by Matt Thomson, Databricks
Lakehouse updates by Frank Munz, Databricks
You Might be Suffering From the
Small Files Syndrome, by Adi Polak, Microsoft

643cd45dcfa73b072018046e39ed36d1?s=128

Frank Munz

June 18, 2021
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Transcript

  1. Slice & DAIS 2021 Community Hightlights Data and AI Summit

    2021 Adi Polak Matt Thomson Frank Munz
  2. None
  3. Agenda for Today ▪ Beginner friendly DAIS highlights ▪ Agenda

    ▪ This intro :-) ▪ ML updates, Matt Thomson, Databricks ▪ Lakehouse updates, Frank Munz, Databricks ▪ You Might be Suffering From the Small Files Syndrome, Adi Polak, Microsoft
  4. Databricks ML May 2021 announcements

  5. Key ML Announcements 1. Databricks Machine Learning 2. Feature Store

    3. AutoML 4. MLflow developments
  6. Lakehouse One platform to unify all your data, analytics, and

    AI workloads BI & SQL Open Data Lake Data Management & Governance Real-time Data Applications Data Science & ML
  7. Open Data Lakehouse Foundation with Announcing: Databricks Machine Learning A

    data-native and collaborative solution for the full ML lifecycle MLOps / Governance Data Prep Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving Data Science Workspace
  8. Persona-based Navigation Purpose-built surfaces for data teams

  9. ML Dashboard All ML related assets and resources in one

    place
  10. Feature Store

  11. Open Data Lakehouse Foundation with MLOps / Governance Data Prep

    Data Versioning Monitoring Batch Scoring Online Serving AutoML Data Science Workspace Model Training Model Tuning Runtime and Environments Feature Store Batch (high throughput) Real time (low latency) Feature Store The first Feature Store codesigned with a Data and MLOps Platform
  12. A day (or 6 months) in the life of an

    ML model Raw Data Featurization Training Joins, Aggregates, Transforms, etc. csv csv Serving Client No reuse of Features Online / Offline Skew
  13. Solving the Feature Store Problem Raw Data Featurization Training Joins,

    Aggregates, Transforms, etc. Serving Client Feature Store Feature Registry Feature Provider Batch (high throughput) Online (low latency)
  14. None
  15. None
  16. None
  17. AutoML

  18. Open Data Lakehouse Foundation with MLOps / Governance Data Prep

    Data Versioning Monitoring Batch Scoring Online Serving AutoML Data Science Workspace Model Training Model Tuning Runtime and Environments Feature Store Batch (high throughput) Real time (low latency) Databricks AutoML A glassbox approach to AutoML that empowers data teams without taking away control
  19. What is AutoML? Automated machine learning (AutoML) is a fully-automated

    model development solution seeking to “democratize” machine learning. While the scope of the automation varies, AutoML technologies usually automate the ML process from data to model selection. select a dataset automated data prep automated feature engineering and selection automated training and model selection automated hyperparameter tuning
  20. AutoML solves two key pain points for data scientists Quickly

    Verify the Predictive Power of a Dataset “Can this dataset be used to predict customer churn?” Marketing Team Data Science Team Dataset Data Science Team Baseline Model Dataset “What direction should I go in for this ML project and what benchmark should I aim to beat?” Get a Baseline Model to Guide Project Direction
  21. Problems with Existing AutoML Solutions Opaque-Box and Production Cliff Problems

    in AutoML Problem Result / Pain Points 1. A “production cliff” exists where data scientists need to modify the returned “best” model using their domain expertise before deployment 2. Data scientists need to be able to explain how they trained a model for regulatory purposes (e.g., FDA, GDPR, etc.) and most AutoML solutions have “opaque box” models • The “best” model returned is often not good enough to deploy • Data scientists must spend time and energy reverse engineering these “opaque-box” returned models so that they can modify them and/or explain them AutoML Configuration Returned Best Model AutoML Training “Opaque Box” ? Deployed Model Production Cliff ?
  22. Databricks AutoML A glass-box solution that empowers data teams without

    taking away control UI and API to start AutoML training Data exploration notebook Generated notebook with feature summary statistics and distributions Reproducible trial notebooks Generated notebooks with source code for every model MLflow experiment Auto-created MLflow Experiment to track models and metrics Easily deploy to Model Registry Understand and debug data quality and preprocessing Iterate further on models from AutoML, adding your expertise
  23. None
  24. None
  25. Notebook source databricks.automl.classify(df, target_col='label', timeout_minutes=60) “Glass-Box” AutoML with an API

  26. MLflow Developments

  27. : An Open Source ML Platform Monitoring Model management Reproducible

    runs Packaging & serving TRACKING PROJECTS MODEL REGISTRY MODELS Training Deployment Raw Data Data Prep ML Engineer Application Developer Data Engineer Any Language Any ML Library
  28. What’s New in MLflow AUTO LOGGING TRACKING mlflow.spark.autolog() mlflow.pyspark.ml.autolog() mlflow.sklearn.autolog()

    mlflow.tensorflow.autolog() mlflow.pytorch.autolog() mlflow.gluon.autolog() mlflow.keras.autolog() mlflow.lightgbm.autolog() mlflow.xgboost.autolog() mlflow.fastai.autolog() mlflow.autolog() mlflow.shap.log_explainer() mlflow.shap.log_explanation() mlflow.log_figure() mlflow.log_image() mlflow.log_dict() mlflow.log_text() mlflow.catboost.autolog() mlflow-thin-client
  29. What’s New in MLflow Google Cloud AI Platform DEPLOYMENT BACKENDS

  30. PyCaret + MLflow

  31. Lakehouse Intro

  32. Streaming Analytics BI Data Science Machine Learning Structured, Semi-Structured and

    Unstructured Data Data Lake for all your data One platform for every use case Structured transactional layer Lakehouse RELIABILITY & QUALITY PERFORMANCE & LATENCY GOVERNANCE ACID transactions Advanced indexing, caching, compaction Fine-grained access control
  33. Lakehouse adoption across industries

  34. Single node Data Science meets Big Data

  35. What is Koalas? Implementation of pandas APIs over Spark Easily

    port existing data science code, making it execute at scale import databricks.koalas as ks df = ks.read_csv(file) df[‘x’] = df.y * df.z df.describe() df.plot.line(...) import pandas as pd df = pd.read_csv(file) df[‘x’] = df.y * df.z df.describe() df.plot.line(...) Now ~ 3 million PyPI downloads per month.
  36. Single Node Performance Comparison - 31 GB Lower is better

  37. Performance Comparison - 95 GB pandas Lower is better pyspark.pandas

  38. Scala DataFrame Python DataFrame pandas APIs Different APIs, Powered by

    the Same Engine SQL Language
  39. ANSI SQL Compliance Python Performance More Streaming Decorrelation Framework Timestamp

    w/o Time Zone Adaptive Optimization Scala 2.13 Beta Error Code Implicit Type Cast Interval Type Complex Type Support in Lateral Join Compile Latency Reduction JAVA 17 Push-based Shuffle Session Window Visualization and Plotting RocksDB State Store Queryable State Store Pythonic Error Handling Richer Input/Output Koalas (pandas APIs) Parquet 1.12 (Column Index) State Store APIs DML Metrics ANSI Mode GA Apache Spark Development (Link 3.1.1 RelNotes) Low Latency Scheduler
  40. OSS Delta Lake 1.0

  41. • Project Zen: more Pythonic, better usability • Faster performance

    including predicate pushdown and pruning • ANSI SQL compliance for DDL/DML commands including INSERT, MERGE, and EXPLAIN • Spark 3.1 comes with Databricks Runtime 8.0 https://spark.apache.org/releases/spark-release-3-1-1.html https://databricks.com/blog/2021/03/02/introducing-apache-spark-3-1.html delta.io blog: Delta Lake 1.0.0 Released
  42. Generated Columns Problem: Partition by date Better solution: generated columns

    CREATE TABLE events( id bigint, eventTime timestamp, eventDate GENERATED ALWAYS AS ( CAST(eventTime AS DATE) ) ) USING delta PARTITIONED BY (eventDate) id eventTime eventDate 1 2021-05-24 09:00:00.000 2021-05-24 .. ... ...
  43. Delta Everywhere Standalon e JVM

  44. pip install delta-spark Python APIs for using Delta Lake with

    Apache Spark, e.g. for unit testing pip install deltalake # delta lake without spark from deltalake import DeltaTable dt = DeltaTable("reviews") dt.version() 3 dt.files() ['part-00000-...-ff32ddab96d2-c000.snappy.parquet', 'part-00000-...-d46c948aa415-c000.snappy.parquet', 'part-00001-...-7eb62007a15c-c000.snappy.parquet'] PyPI Install Delta Rust client
  45. Delta Live Tables

  46. Filtered, Cleaned, Augmented Business-level Aggregates Raw Ingestion and History Building

    the foundation of a Lakehouse with ETL Data Lake CSV, JSON, TXT… Kinesis BI & Reporting Streaming Analytics Data Science & ML BRONZE SILVER GOLD QUALITY
  47. Delta Live Tables: Easily build data pipelines Declaratively build data

    pipelines with business logic and chain table dependencies Run in batch or streaming with structured or unstructured data Reuse ETL pipelines across environments https://docs.databricks.com/data-engineering/delta-live-tables/index.html
  48. Treat your data as code A single source of truth

    for more than just transformation logic. CREATE LIVE TABLE clean_data( CONSTRAINT valid_timestamp EXPECT (timestamp > "…") ) COMMENT "Customer data with timestamps cleaned up" TBLPROPERTIES ( "has_pii" = "true", ) as SELECT to_timestamp(ts) as ts, * FROM LIVE.raw_data Declarative Quality Expectations Just say what makes bad data bad and what to do with it. Documentation with Transformation Helps ensure discovery information is recent. Governance Built-In All information about processing is captured into a table for analysis / auditing.
  49. Databricks Unity Catalog Simplified governance for data and AI

  50. Data Lake Governance Today is Complex Data (files on S3/ADLS/GCS)

    /dataset/pages/part-001 /dataset/pages/part-002 /dataset/users/uk/part-001 /dataset/users/uk/part-002 /dataset/users/us/part-001 Users File-based permissions: • user1 can read /pages/ • user2 can read /users/ • user3 can read /users/us/ What if we only want users to see some columns/rows within a table? What if we want to change data layout? What if governance rules change? Metadata (e.g. Hive Metastore) Tables & views ML Models SQL Databases Could be out of sync with the data! Different governance model Different governance model
  51. Databricks Unity Catalog Data (files on S3/ADLS/GCS) /dataset/pages/part-001 /dataset/pages/part-002 /dataset/users/uk/part-001

    /dataset/users/uk/part-002 /dataset/users/us/part-001 Users table 1 ML Models SQL Databases Delta Shares Unity Catalog table 2 view1 view2 models view3 • Fine-grained permissions on tables, fields, views • ANSI SQL grants • Uniform permission model for all data assets • Across workspaces • ODBC/JDBC / Delta Sharing Audit Log
  52. Using the Unity Catalog CREATE TABLE iot_events GRANT SELECT ON

    iot_events TO engineers GRANT SELECT(date, country) ON iot_events TO marketing
  53. Attribute-Based Access Control (ABAC) CREATE ATTRIBUTE pii ALTER TABLE iot_events

    ADD ATTRIBUTE pii ON email ALTER TABLE users ADD ATTRIBUTE pii ON phone ... GRANT SELECT ON DATABASE iot_data HAVING ATTRIBUTE NOT IN (pii) TO product_managers Set permission on all columns tagged pii together
  54. Delta Sharing An Open Protocol for Secure Data Sharing

  55. Delta Sharing: delta.io/sharing Delta Lake Table Delta Sharing Server Delta

    Sharing Protocol (REST) Data Provider Data Recipient Commercial or open source Python connector for pandas or Apache Spark Access permissions pip install delta-sharing
  56. Delta Sharing Recipient: Pandas + Jupyter Notebook

  57. Delta Sharing on Databricks Secure Delta Sharing server integrated in

    our service easily manages shares with CREATE SHARE commands in SQL or REST APIs. Delta Sharing Protocol Data Recipients Data Provider sales CREATE SHARE retail ALTER SHARE retail ADD TABLE sales GRANT SELECT ON SHARE retail TO supplier1 Audit log Unity Catalog
  58. How to engage? delta.io delta-users Slack delta-users Google Group Delta

    Lake YouTube channel Delta Lake GitHub Issues Delta Lake RS Bi-weekly meetings
  59. Databricks SQL

  60. First-Class SQL Development Experience Develop Query tabs Drafts & “pick

    up where you left off” Command history Contextual auto-complete Troubleshoot Query progress Error highlighting Execution time breakdown Collaborate Scheduled email delivery Edit permissions Enabling simple, quick ad-hoc exploratory analysis on the Lake with SQL
  61. Large Query Performance Price / Performance Benchmark with Barcelona Supercomputing

    Center (Nov 2020) 30TB TPC-DS Price/Performance Lower is better
  62. Beyond large query performance Many small files Small queries BI

    Results Retrieval Providing fast and predictable performance for all workloads Mixed small/large
  63. What about many concurrent users on small data? 10 GB

    TPC-DS @ 32 Concurrent Streams (Queries/Hr) Higher is better
  64. What about many concurrent users on small data? 10 GB

    TPC-DS @ 32 Concurrent Streams (Queries/Hr) Higher is better
  65. What about badly laid out tables? Small files ~12x rows

    scanned within the same duration Async & parallel IO: Cold S3/ADLS remote reads fully saturate S3/ADLS/GCS bandwidth with increased parallelism for better cold reads “Too Many Small Files” Scenario Benchmark (# rows scanned/sec) Higher is better
  66. Summary: Advancing the Lakehouse Reliable ETL made easy with Delta

    Lake The first open protocol for data sharing The first multi-cloud data catalog for the lakehouse The first high performance query engine for the lakehouse Delta Sharing Delta Live Tables Unity Catalog Photon Available Today Coming Soon Coming Soon Public Preview
  67. Data and AI Summit: https://dataaisummit.com/ Databricks Youtube channel: https://www.youtube.com/ Databricks

    Databricks blog: https://databricks.com/blog @frankmunz