Crunch Conference 2022
2022-10-03..2022-10-05
-- online --
[Crunch Conference 2019, Crunch Conference 2021, Crunch Conference 2022]
who it's for
data engineers, analytics engineers, data platform engineers, machine learning engineers, data scientists, privacy engineers
topics
multi-cloud data infrastructure; Kafka ingestion pipelines; Spark on Kubernetes; Flink low-latency processing; Snowflake multi-region analytics; BigQuery cost optimisation; dbt transformations and lineage; Apache Iceberg merge-on-read; Debezium change data capture; open source metric store (MetricFlow); MLOps model packaging and deployment (vetiver); GDPR privacy by design
| title | author | length |
|---|---|---|
| Data Infrastructure in a Multi-Cloud Environment | Wouter de Bie | 01h 01m |
Datadog runs separate cloud-region instances to avoid cross-cloud egress and meet legal constraints, using Kafka as the ingestion backbone and ephemeral… | ||
| Building Causal Measurement | Catherine Zhou | 39m |
Twitter builds an in-house incremental measurement system that layers incremental holdouts on top of standard A/B tests: each holdout runs 12 months split into… | ||
| FinOps Examples using Google Cloud BigQuery by Aliz.ai | Zoltán Guth, Gergely Schmidt | 40m |
Consultancy work on a 100M+ MAU e‑commerce stack analyzes BigQuery job history and INFORMATION_SCHEMA and uses ZetaSQL to group similar queries, finds 5,000… | ||
| Building an MLOps strategy from the ground up | Isabel Zimmerman | 50m |
Practical guide to getting models into production: use vetiver (R/Python) to package pipelines with an input prototype, pin versions via pins, and deploy a… | ||
| Setting a foundation for analytics as a business utility | Kathleen Maley | 41m |
Focus is on getting business context, not more reports: ask the right questions, run investigative analytics, then deliver a single-slide conclusion plus… | ||
| Understanding the GDPR and applying privacy principles in your designs | Julia Bodnar | 40m |
Explains that GDPR covers any EU-based business or any service targeting EU individuals, defines personal data as contextual and often pseudonymous, and lists… | ||
| Data Swamp to Data Swimming Pool: Cleaning up the Mess | Maggie Hays, John Joyce | 37m |
Acryl Data demonstrates a concrete governance pipeline: inventory metadata from Postgres, MongoDB, Kafka, Airflow, S3, Spark, Snowflake, dbt, Great… | ||
| Metrics and Semantics: An evolution in data consumption | Nick Handle | 40m |
MetricFlow is an open source metric store that compiles YAML metric definitions into rendered SQL for Snowflake, BigQuery, Redshift, Databricks, Postgres… | ||
| Use animated data stories in Jupyter to present | Peter Vidos | 40m |
Animated chart engine (C++ core, open JS library, Apache‑2.0) that morphs between chart types and runs inside Jupyter/Colab/Kaggle/Databricks via ipyvizzu and… | ||
| Data Modelling for Startups: Domain Models & the Modern Data Stack | Thomas in't Veld | 43m |
A fast path for startup analytics is to centralize raw sources into one warehouse, model using a domain-aligned main model (source → domain → presentation)… | ||
| Apache Iceberg Merge-On-Read: Streaming CDC | Victoria Bukta | 39m |
Built a streaming CDC pipeline that tails MySQL binlogs with Debezium, publishes events to Kafka, consumes them with Shopify’s Pepto and writes Parquet files… | ||
| Learning from pilots | Yizhar Toren | 42m |
Explains how small pilots (often ~100–200 merchants) give weak signals but can be strengthened using bootstrapping to show sensitivity, building digital twins… | ||
| Sustainable Machine Learning by Carl Zeiss AG | Vladimir Korzinov | 44m |
Carl Zeiss builds and runs ML systems as software: central Accelerator team with project managers, data scientists and MLE/Ops, aiming to grow to ~100 people… | ||
| nothing matches | ||