Databricks + Snowflake: Catalyzing Data and AI Initiatives
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"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
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LINK_COMMANDE (Client ↔ Produit ↔ Date)
Structure avec clés techniques
Script SQL + logique métier
7. Création des SATELLITES
SAT_CLIENT_DETAILS, SAT_PRODUIT_DETAILS…
Historisation avec LOAD_DATE, END_DATE, HASH_DIFF
Script SQL (MERGE ou INSERT conditionnel)
8. Orchestration
Exemple de flux via dbt ou Airflow (ou simplement séquence SQL)
Screenshot modèle YAML dbt ou DAG Airflow
9. Création des vues métiers (DM)
Vue agrégée des ventes mensuelles
SELECT complexe sur HUB + LINK + SAT
Screenshot ou exemple de résultat
10. Visualisation
Connexion à Power BI / Tableau
Screenshot d’un graphique simple basé sur une vue DM
11. Conclusion et bénéfices
Fiabilité, auditabilité, versioning, historique
Adapté aux environnements de production
Lambda Architecture in the Cloud with Azure Databricks with Andrei Varanovich
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Democratizing Data Quality Through a Centralized Platform
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At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
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Dynamically generating pipelines that can be abstracted away from users
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Why APM Is Not the Same As ML Monitoring
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As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
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The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
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Simplify Data Conversion from Spark to TensorFlow and PyTorch
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Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
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· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
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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.
Raven: End-to-end Optimization of ML Prediction Queries
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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.
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(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.
Processing Large Datasets for ADAS Applications using Apache Spark
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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.
Massive Data Processing in Adobe Using Delta Lake
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Databricks + Snowflake: Catalyzing Data and AI Initiatives
- 1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics
- 2. Garren Staubli Solutions Architect Databricks + Snowflake: Catalyzing Data and AI #UnifiedAnalytics #SparkAISummit Slides & Resources: garrens.com/DataSnowCat [email protected] | @gstaubli
- 3. Agenda 3#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Introductions Scenario Challenges Solutions Demo
- 4. 4#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Introductions - Me 2011 2012 2013 2014 2015 2016 2017 2018 2019 MySQL Ruby AWS Pig & Hive Python Linux Scala, Python & Java Apache Spark & ML Hadoop NoSQL
- 5. Databricks Delta Databricks Workspace Collaborative Notebooks, Production Jobs Databricks Runtime Transactions Indexing ML FrameworksML Frameworks Introductions - Databricks Cloud Data & ML Lifecycle Data Engineering Data Science Accelerate innovation by unifying data science and engineering
- 6. 6#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Introductions - Snowflake
- 7. 7#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Forget Oil. Data is worth more than Gold
- 8. 8#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Scenario
- 9. 9#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Data Mining Data Science ML Engineering QADevOps Production Delivery* * not Digiorno Scenario - Annotated
- 10. 10#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Scenario - Reality
- 11. 11#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Challenges WAREHOUSES NOSQL LAKES STREAMS Sources APIs Apps BI Data-Driven Production
- 12. 12#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Challenges - Reality Partitions: 20 Rows per second: 10,000 Format: JSON Extract Transform ML Load Analysis Insights
- 13. Flat, RDBMS, Streams, etc Unified batch & stream APIs Autoscaling with usage Python, Scala, SQL, R & Java JVM w/ optimization Multilevel APIs (SQL + RDDs) 13#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Challenges & Solutions - ETL Partitions: 20 Rows per second: 10,000 Format: JSON Sources Syntax Scale Languages Performance Expressiveness
- 14. 14#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Challenges & Solutions - ETL Partitions: 20 Rows per second: 10,000 Format: JSON Malformed Records Errors Changing Fields Writes - Performance - Semantics - Reliability Ignore/infer + log records Handle + retry w/ checkpoint Schema Evolution Partitioned + optimized files Exactly once ACID transactions
- 15. 15#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Challenges & Solutions - ML Partitions: 20 Rows per second: 10,000 Format: JSON Data Access Syntax Collaboration Models - Iteration - Reproducibility - Deployment Apache Spark + Delta Koalas Databricks Notebooks - Tracking - Projects - Models
- 16. Interactive queries Instant Scaling SQL Tableau, PowerBI, etc Optimized DWaaS Decoupled storage + compute 16#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Challenges & Solutions - Analysis Partitions: 20 Rows per second: 10,000 Format: JSON Time to value Intermittent demand Language Common Tooling Ease of Use Cost control
- 17. 17#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Final Solution Architecture Partitions: 20 Rows per second: 10,000 Format: JSON Machine Learning BI Reporting Dashboards
- 18. 18#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Demo
- 19. Review 19#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Introductions Scenario Challenges Solutions Demo
- 20. 20#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat Solution WAREHOUSES NOSQL LAKES STREAMS Sources Processing WAREHOUSES NOSQL BI Persistence APIs Apps BI LAKES DELTA Integration DELTA ML
- 21. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT