Scale By the Bay 2020
2020-11-12..2020-11-13
-- online --
[Scale By the Bay 2017, Scale By The Bay 2018, Scale By The Bay 2019, Scale By the Bay 2020, Scale By the Bay 2021, Scale By the Bay 2023]
| title | author | length |
|---|---|---|
| Scaling Databricks to Run Data & AI Workloads on Millions of VMs | Matei Zaharia | 57m |
Databricks runs millions of VMs across ~7,000 customers processing exabytes per day and builds a Scala control plane on Kubernetes with Envoy and Prometheus… | ||
| Countdown to 3 | Martin Odersky | 01h 06m |
Scala 3 reaches milestone M1 and plans RC1 for December with a final release around Jan–Feb; the new compiler and a Metals-based language server, Scala 3 doc… | ||
| Preview: Keynotes | Martin Odersky | 01m |
ai.bythebay.io Nov 2025, Oakland, full-stack AI conference SBTB 2020 is keynoted by Martin Odersky, creator of Scala, Anima Anandkumar, Director of AI at… | ||
| Panel Discussion: Will AI Kill Programming? | Vitaly Gordon, Helena Edelson, Omar Alonso, Bryan Cantrill, Gene Linetsky | 55m |
The panel debates whether AI becomes as essential to software work as source control and CI, arguing specific claims and demos: proponents point to GPT-3… | ||
| Acting Lessons for Scala Engineers with Akka and ZIO | Salar Rahmanian | 22m |
Compares Akka and ZIO Actors by building a contrived mailing‑list app that actually spawns actors, validates emails, logs actions and sends subscribe replies… | ||
| Monocle 3: a peek into the future | Julien Truffaut | 33m |
Explains concrete Monocle 3 changes: a focus macro that generates lenses/prisms/indexes from a dot-path (IDE-completion friendly) and runs only on Scala 3, a… | ||
| Panel: Were Microservices a Huge Mistake? | Alexy Khrabrov, William Morgan, Pete Hunt, Kikia Carter, Chris Richardson, Nick Schrock | 01h 05m |
Panel argues specific tradeoffs: one claim predicts a multi‑billion dollar cleanup industry from misapplied microservices; another warns of distributed… | ||
| Project Loom? Better Futures? What’s next for JVM concurrent programming | Adam Warski | 32m |
Project Loom introduces virtual threads and continuations to the JVM, making threads cheap to create and block so thousands can run while the JVM schedules… | ||
| Query Planning in GraphQL | Greg Kesler | 40m |
Implements a reusable query planner for GraphQL Java that statically builds merged-field vertices, expands abstract types into concrete variants, and… | ||
| Materialize Typeclasses with Magnolia | Shameera Rathnayaka | 34m |
Shows how Magnolia derives typeclasses at compile time to auto-generate Json/Avro/BigQuery/Protobuf converters for case classes and sealed traits, replacing… | ||
| C-chain: the Integration of 5G and real time Blockchain | Rudolf Bayer | 23m |
C-chain replaces proof-of-work with signature-based proof of correctness and a single chain with many per-agent chains, achieving transaction booking in 20… | ||
| Reproducible Data Pipelines Using Controlled Effects | Yves Parès | 32m |
Presents a Haskell architecture (kernmantle) that encodes side effects as binary abstract effects with two type parameters, composes tasks via arrows, and… | ||
| TypeScript - You'll Like It | Jason Swartz | 24m |
TypeScript is TypeScript = JavaScript + types (Microsoft, 2012) and compiles with the tsc compiler into ES6 JavaScript; examples show ts-node + yarn/npm… | ||
| Apache Spark meets FIPS standard | Prashant Sharma | 23m |
Explains how to run Apache Spark on an OpenShift/Kubernetes cluster with Cloud HSM and bring your own key using envelope encryption so cloud providers cannot… | ||
| When the Only Way to Scale up, is to Scale Down | Jennifer Prendki | 36m |
Claims that less data often beats more data and shows tooling to prove it: Alectio uses active learning and an auto active learning optimizer to pick training… | ||
| Applicative: The Origin Story | Justin Heyes-Jones | 32m |
Shows that applicative lets pure multi‑argument functions be applied to effects by lifting the function and using ap / map2 instead of flatMap. Translates the… | ||
| Getting Things Done in the Scala REPL | Li Haoyi | 30m |
Live demo shows using Ammonite REPL plus JVM library jsoup to scrape Wikipedia and MDN, parse headlines and first paragraphs, and write JSON files to disk… | ||
| Enhancing Spark's Power with ZIO | Leo Benkel | 29m |
Combines Apache Spark with ZIO via an open source library zpark-io that converts Futures to ZIO, wires Spark into ZIO layers, and removes boilerplate Spark… | ||
| Packaging & Deployment Options for Scala Applications / Services | James Ward | 28m |
Compares four concrete packaging paths for Scala apps: Buildpacks, Jib, sbt-native-packager (Docker), GraalVM native-image. Demonstrates Buildpacks producing a… | ||
| Keynote: Next-generation frameworks for Large-scale AI | Anima Anandkumar | 24m |
Claims that embedding structure beats scale: Tensorly layers and CP/tensor-train factorizations produce models an order of magnitude smaller with better… | ||
| Panel: Programming Languages in the Era of the Cloud | Evan Chan, Wiem Zine Elabidine, Alexy Khrabrov, Bill Venners, Baruch Sadogursky, Rúnar Bjarnason, Jon Pretty, Bryan Cantrill, Jaana Dogan | 01h 00m |
Panel argues cloud choices hinge on concrete tradeoffs: Go wins for fast startup and tiny static binaries and cross-compilation; Rust wins for memory control… | ||
| A tour of String diagrams and Monoidal categories | Juan Pablo Romero | 31m |
Describes a Scala declarative DSL that encodes string diagrams / monoidal structure, composes processes by tensor (parallel) and sequential operations, and… | ||
| Owned By Statistics: How Kubeflow & MLOps Can Help Secure... | David Aronchick | 29m |
Shows concrete attacks and defenses: builds a toy circle-vs-square failure and links it to real failures like a snow detector for huskies, stickers that flip… | ||
| Data Science in Scala with ScalaPy | Shadaj Laddad | 32m |
ScalaPy embeds a CPython interpreter and exposes Python libraries to Scala on JVM and Scala Native via JNA or native FFI, with heap synchronization, GIL… | ||
| Functional implementation of Finger Trees in JavaScript | Vlad Patryshev | 32m |
A complete, purely functional Finger Tree implemented in a narrow subset of JavaScript and demonstrated inside an ObservableHQ workbook, including cons and… | ||
| Programming machine learning algorithms in hardware, sanely, using Haskell and Rust! | Ryan Orendorff, Daniel Hensley | 29m |
Clash (a Haskell-like language) compiles a dependently-typed neural network (GADTs, type-level naturals) into FPGA logic and a Cyclone V runs a five-layer… | ||
| Conquering All Stores with Gimel – A Unified Data Processing Platform | Deepak Chandramouli, Anisha Nainani, Vladimir Bacvanski | 35m |
Gimel is a Spark-and-Scala based platform that exposes a single dataset.read/dataset.write and SQL surface over Hive, HBase, Cassandra, Druid, Teradata… | ||
| Put Your Machine Learning on Autopilot | Antje Barth | 31m |
Shows a concrete AutoML pipeline that takes the Amazon Customer Reviews CSV (130M records available, demo uses camera category), loads a pre-trained bird model… | ||
| Keynote: Apache Pulsar | Karthik Ramasy | 40m |
Splunk replaces Kinesis/SQS/RabbitMQ/Kafka with Apache Pulsar inside Splunk DSP, running billions of messages and supporting cloud and on‑prem deployments… | ||
| Cloudflow: Spark, Flink, and Akka Working together on Kubernetes | Nolan Grace | 30m |
Cloudflow wires Spark, Flink, Akka and Kafka into Kubernetes with a Scala/Java API and an operator that compiles streamlet definitions into Docker images… | ||
| 8 Lessons Learned from using Kafka with 1000 microservices | Natan Silnitsky | 30m |
Wix runs ~1,500 microservices and 1.5 billion Kafka messages per day and builds a shared Kafka layer called Greyhound rewritten on ZIO. Greyhound adds parallel… | ||
| Smokey and the Multi-Armed Bandit featuring BERT Reynolds | Chris Fregly | 36m |
Fine-tuned two BERT variants (TensorFlow and PyTorch, using the Transformers library from Hugging Face) on the Amazon Customer Reviews dataset and deploys them… | ||
| A Reinforcement Learning Framework in Scala 3 | Robert J. Neal | 28m |
A small Scala 3 library implements tabular reinforcement learning for experimenting and comparing algorithms, with a working tic‑tac‑toe agent that learns via… | ||
| Rebuilding Twitter’s public API | Steve Cosenza | 22m |
Twitter builds a GraphQL-based multi-tenant platform (Scala, OpenAPI Spec, thrift) to host v2 endpoints, consolidating fragment-driven field configs and… | ||
| Goku Flow: A Self-Service Data Pipeline Builder | Lei Gao | 30m |
Workday runs about 20 petabytes of operational logs and ingests roughly 2 trillion messages per month; Goku Flow is an in-house, patent-backed self-service… | ||
| Event Streaming with Kafka Streams | Viktor Gamov | 31m |
Kafka Streams is a Java library that runs inside applications and implements scalable, stateful event processing by handling partition assignment when multiple… | ||
| The art of being resilient. How to handle failures gracefully in Distributed Systems. | Muktesh Mishra | 29m |
Shows how structured logging (JSON metadata + common schema) and propagated headers for Jaeger traces let teams pinpoint the failing microservice and cut MTTR… | ||
| NLP text recommender system journey to automated training pipeline with Spark and Sagemaker | Aditya Sakhuja | 31m |
Built an enterprise NLP recommender that reduces agent time‑to‑resolution by surfacing top‑k articles, using Scala/Python, Apache Spark, Docker and SageMaker +… | ||
| Deploying a Modern Serverless Reactive Container to the Cloud | Mary Grygleski | 35m |
A live demo builds a polyglot Vert.x application (Java, Kotlin, JavaScript), dockerizes the image, pushes it to Docker Hub and deploys a Knative Service to an… | ||
| How Micro-Service Patterns Change When the Database Scalable | Brecht De Rooms | 27m |
Argues that a globally consistent, scalable database reshapes microservice choices by restoring single-database options and shifting complexity from engineers… | ||
| Ray: A System for High-performance, Distributed ML Applications | Dean Wampler | 24m |
Ray is a lightweight distributed runtime that scales Python from laptop to clusters (users run up to 10,000 nodes), exposes a tiny API — ray.remote for tasks… | ||
| Automate the boring ML stuff with pipelines | Catherine Nelson, Hannes Hapke | 18m |
A reproducible ML pipeline that validates incoming data against a generated schema and stops training on mismatched batches, runs feature engineering with… | ||
| Multi-language Runtime Environments for Smart Contract Execution | James Aman | 30m |
Implements a multi-language smart contract runtime inside a Scala blockchain client using GraalVM polyglot and Truffle, running JavaScript contracts on a… | ||
| Cloud-Native Apache Spark: why & how to migrate your Spark | Jean-Ives Stephan | 28m |
Spark runs natively on Kubernetes since 2.3 and is declared production ready in Spark 3.1; practical migration uses the spark-on-kubernetes operator, cluster… | ||
| Spreading the JAMstack | Scott Spence | 26m |
JAMstack means pre-render pages and serve them from a CDN so pages require no server work at request time; big claims: sites aim for ~2s load (slower loses… | ||
| Optimizing Latency-Sensitive Queries for Presto at Facebook | Rohit Jain, Bin Fan | 28m |
Facebook embeds Alluxio local on-SSD cache into Presto workers and measures results on a 600-node shadow cluster with 460 GB cache per node and 17,000 real… | ||
| AIoT: Why now? And How To? | Dirk Slama | 22m |
Claims that industrial AIoT is practical now and lays out concrete tooling and organization: examples include autonomous parking already rolling into public… | ||
| State of the art natural language understanding at scale | David Talby | 33m |
Open-source Spark NLP provides production-ready, enterprise-grade NLP with about 90k downloads/week and ~1M downloads per quarter, 26 releases in 2020, and… | ||
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