Data Love 2021
| title | author | length |
|---|---|---|
| The Future of Data Science/ML at Scale | Jules S. Damji | 02m |
Argues that the legacy platform stack fails on increasing data volume, timeliness, and quality and that brittle hand-built ETL is the bottleneck; examines the… | ||
| A Change-Data-Capture use-case: designing an evergreen cache | Nikolas Frankel | 01m |
Concrete design for an evergreen cache built on Change-Data-Capture: the talk outlines a CDC pipeline connecting the source DB to a message bus and a cache… | ||
| Data Love 2021, Apr 16th | 01m | |
https://datalove.konfy.care
https://datalove.eventbrite.com
This conference is not only for engineering professionals but also for people of science. No… | ||
| Back to the Future | Michał Kowalczykiewicz | 01m |
Revisits old academic ideas like Prolog and logical programming and shows an application framework that composes logic blocks into pipelines. Demonstrates that… | ||
| Don't Drop ACID - Transactions in Distributed NoSQL | Matthew D Groves | 01m |
Explains why early NoSQL dropped full ACID, then demonstrates a working distributed multi-document ACID transaction on a document database and the cost… | ||
| Getting Reactive with Relational Databases and R2DBC | Rob Hedgpeth | 01m |
Overview of the R2DBC connectivity specification for nonblocking access to relational databases and how it maps reactive streams to SQL operations, with… | ||
| E2E ML Platform on Kubernetes with just a few clicks | Mofi Rahman | 01m |
Shows how to build an end-to-end machine learning platform on Kubernetes using the open source qflow, demonstrating setup and orchestration that the uploader… | ||
| Cloud-Native Apache Spark - Spark on Kubernetes Is Now Generally Available | Jean-Yves Stephan | 01m |
Spark on Kubernetes reaches GA with Spark 3.1 (March) and is declared production ready; demonstrates running driver and executors as Kubernetes pods, native… | ||
| DataLove + Recorem | 01m | |
Big data. Cloud data. ML, AI training data, and personally-identifying data. Data is all around you. The world is data-centric.
Data Love is the conference… | ||
| MLOps: Applying DevOps to ML | Damian Brady | 01m |
Describes applying DevOps practices to machine learning by mapping planning, code management, CI/CD and deployment to model workflows; introduces concrete… | ||
| Kotlin for Apache Spark | Pasha Finkelshtein | 01m |
Introduces a new Kotlin API called continuity for Apache Spark that reimplements core Spark primitives, shows concrete code translating DataFrame/SQL and RDD… | ||
| Small Big Data: using NumPy and Pandas | Itamar Turner Trauring | 01m |
Shows how to process datasets larger than RAM on a single machine using concrete NumPy and Pandas techniques, not cluster handwaving. Explains that running out… | ||
| Windowing Functions for Database Analytics | David Stokes | 01m |
Demos windowing functions available in mysql and other relational databases that make per-group analytics feasible inside SQL, including partition by… | ||
| SOFTWARE 2.0 NEEDS DATA 2.0 | Davit Buniatyan | 01m |
Founder of activo (a Y Combinator startup) draws on a Princeton PhD and work with petabyte-scale neuroscience data to argue for Data 2.0: a concrete… | ||
| Windowing Functions for Database Analytics | David Stokes | 32m |
Introduction to SQL window functions with concrete MySQL 8.0 examples showing how to get running totals, moving averages, quartiles and percent ranks from… | ||
| The Future of Data Science/ML at Scale: A Look at MLflow, Delta Lake + Lakehouse | Jules S. Damji | 54m |
Argument: the lakehouse unifies cheap object storage with a metadata layer and new query engines so one system serves BI, streaming, and ML without ETL… | ||
| Getting Reactive with Relational Databases and R2DBC | Rob Hedgpeth | 48m |
R2DBC claims full non‑blocking SQL by building on Reactive Streams and the demo proves it: a MariaDB R2DBC driver + Spring Data R2DBC + Project Reactor stack… | ||
| Back to the Future | Michał Kowalczykiewicz | 42m |
Logic programming returns to industry with LogiQL/LogicBlox and Google’s new Logica, claiming real production use and parallel computation out of the box… | ||
| A Change-Data-Capture use-case: designing an evergreen cache | Nicolas Fränkel | 41m |
Claims that Change-Data-Capture keeps a cache evergreen by wiring database binlog events directly into the cache rather than using TTLs or fragile triggers… | ||
| Cloud-Native Apache Spark - Spark on Kubernetes Is Now Generally Available | Jean-Yves Stephan | 29m |
Spark on Kubernetes reaches Spark 3.1 GA with concrete features: dynamic allocation with shuffle tracking and graceful node decommissioning that moves shuffle… | ||
| The Heat is On: Architecting for Hot Analytics | Gian Merlino | 28m |
Apache Druid is presented as a high‑performance serving database that ingests from Kafka or batch, stores indexed partitions locally and pushes backups to… | ||
| Keeping identity graphs in sync with Apache Spark | Ruben Berenguel | 40m |
Large-scale graph computation is not a very common topic. In this talk I will explain how we handle a significantly large graph at Hybrid Theory, using current… | ||
| E2E ML Platform on Kubernetes with just a few clicks | Mofi Rahman | 32m |
Claims an end-to-end ML platform on Kubernetes can be provisioned with a few clicks by installing Kubeflow via the manifests or the Kubeflow Operator, then… | ||
| Kotlin for Apache Spark: Why the world needs one more API for Spark | Pasha Finkelshtein | 31m |
JetBrains’ Kotlin for Apache Spark library provides a concrete API that removes encoder boilerplate, implements typed Dataset support and uses Kotlin extension… | ||
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