Scale By the Bay 2017
2017-11-16..2017-11-18
San Francisco, United States
[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 |
|---|---|---|
| scale.bythebay.io: Stephan Ewen, Apache Flink and the Next Wave of Stream Processing Applications | Stephan Ewen | 49m |
Apache Flink is presented as a runtime for stateful stream computations that combines event-time watermarks, large in-process state and exactly-once guarantees… | ||
| The Futures are Calling and I Must Choose | Chris Phelps | 41m |
Compares concrete async primitives: Scala Futures use an ExecutionContext (global fork/join pool) and submit each map/flatMap as runnables, Twitter Futures… | ||
| Building a Tagless Final DSL for WebGL in Scala | Luka Jacobowitz | 35m |
Claims that a tagless final DSL built in Scala.js provides composable layers from a low-level WebGL algebra up to a render-engine, interpreting into any effect… | ||
| Playing with Shapeless | Thomas Kim, Charles Ruhland | 23m |
Play-Formless generates Play Framework Form mappings from case classes using Shapeless (LabelledGeneric, HLists and singleton-typed symbols) so a full form can… | ||
| Index your State for Safer Functional APIs | Vincent Marquez | 20m |
Claims that using IndexedState in Scala (via Scalaz/StateT) enforces API call order at compile time rather than relying on runtime checks. Demonstrates… | ||
| Functional Patterns and Gems in Scala | Ryan Delucchi | 33m |
Defines a Planet monad and implements its Monad/Functor/Traverse laws in Scala, using Cats/Scalaz concepts, for-comprehensions and flatMap examples; shows a… | ||
| Declarative concurrent programming with Chymyst | Sergei Winitzki | 41m |
Chymyst is an open‑source Scala implementation of the chemical machine (Join Calculus) that claims to make actors type‑safe, stateless and automatically… | ||
| Fixing the rusty core of the internet with functional prog. | Colt Frederickson | 20m |
Claims OpenSSL shows under 50% test coverage and dozens of high/critical vulnerabilities over the last decade, so types plus functional programming can encode… | ||
| Error Handling Without Throwing Exceptions | Haeley Yao | 18m |
Payment service for a shopping bot at eBay uses EitherT stacking Future+Either so asynchronous calls to external services short‑circuit on the first failure… | ||
| Analyzing Functional Programs | David Cleaver | 33m |
Shows how Tagless Final and the Free Monad let business logic stay effect-agnostic while enabling concrete interpreters: a synchronous in-memory test… | ||
| FS2 Internals: Performance | Michael Pilquist | 39m |
FS2 0.10 (milestone8) claims major speedups: common take implementations run about 4–5× faster than 0.9 and some microbenchmarks jump from ~11k to ~50k+… | ||
| Defusing the configuration time bomb with PureConfig and Refined | Leif Wickland | 34m |
Research shows 75% of high-severity failures trace to latent configuration errors; real incidents include App Engine’s 2010 outage after a manual config edit… | ||
| Scala 2.13 | Adriaan Moors | 40m |
Scala 2.13 focuses on concrete changes: a faster compiler whose perf work already lands in 2.12, Java 8 lambdas and traits compiling to interfaces to cut… | ||
| Functional Programming with Effects | Rob Norris | 43m |
Derives monads from function composition and implements the required operations — pure and flatMap-derived fish operator — then checks the identity and… | ||
| Auto code generation using AI & Scala Macros | Rahul Chitturi | 25m |
A prototype compiles natural language into Scala by parsing dependency trees, mapping phrases to AST fragments and using Scala Macros plus runtime compilation… | ||
| Adaptive Scrooge - Adaptive Thrift Decoding | Pankaj Gupta | 24m |
AdaptiveScrooge samples a few events to learn which Thrift fields are actually accessed and rewrites decoding and the in-memory representation to skip unneeded… | ||
| Adjunctions in Everyday Life | Rúnar Bjarnason | 35m |
Adjunctions claim to be the precise pattern behind monads, products, coproducts and folds and the talk shows concrete instances and constructions in Haskell… | ||
| Scala Meta Live Coding Session | Pathikrit Bhowmick | 40m |
Live coding builds a Scala Meta annotation that reads a case class annotated with resource and generates companion DTOs: a get case class with listed fields, a… | ||
| Inventory History as Pure Functions | Sky Yin | 20m |
Stitch Fix builds Tracer to answer exact inventory state at any timestamp by storing delta transitions (per-item quantity changes) plus periodic hourly… | ||
| Type Classes for the Rest of Us | Andrew Kuhnhausen | 20m |
Shows how Scala type classes replace ad-hoc serialization patterns by building a compiler-enforced deserialize[A] returning Option that avoids runtime… | ||
| Scala for the Rest of Us | Paul Cleary | 19m |
Argues that OO developers can adopt practical FP without diving into category theory, teaching a minimal checklist: immutability, case classes, pattern… | ||
| The Design of the Scalaz 8 Effect System | John A De Goes | 43m |
Scalaz 8 implements a new IO data type plus a lightweight runtime that uses fiber-based concurrency with an async constructor that supports immediate return… | ||
| How to Elm-ify Your ML | Oscar Boykin | 41m |
Stripe builds a small, principled feature system: a free applicative feature DSL that describes feature logic as an AST, compiles to batch… | ||
| Cloud Science | Marius Eriksen | 38m |
GRAIL builds and runs Reflow, a vertically integrated language+runtime that transparently parallelizes Dockerized bioinformatics across EC2 (including spot… | ||
| Disorder & Tolerance in Distributed Systems at Scale | Helena Edelson | 40m |
Argues that distributed systems can borrow biological principles — ensemble forecasting for multiple failure modes, entropy as a measurable snapshot, and… | ||
| Teaching Scala to the statically challenged | Rebecca Ely | 22m |
Most beginners arriving from Python, JavaScript or Ruby learn faster when introduced to concrete, practical things: start with types and type signatures, use… | ||
| Functional Linear Algebra in Scala | Vlad Patryshev | 28m |
Claims a type-safe, functional linear-algebra layer in Scala that prevents mixing dimensions at compile time, finds real bugs, and supports practical PCA and… | ||
| Just enough DevOps for data scientists. | Anya Bida | 23m |
Practical checklist for running Salesforce Einstein at scale: monitor p99 latency and request/error KPIs on a simple dashboard, collect host metrics with… | ||
| Managing Nation-Wide Traffic Cameras and Sensors | David Massart | 25m |
Belgium builds a nation-wide system aimed at 20,000 cameras (project scales up from 60) that sends plate, GPS and timestamp messages which cameras cache up to… | ||
| Building model testing infrastructure and scaling AI predictions | Sunil Mallya | 39m |
Describes a working serverless MapReduce using S3 and AWS Lambda with a coordinator Lambda that processes 4,200 files (424 GB, 3.54 billion lines) in about 2… | ||
| How to Pick Your Next Streaming Architecture | Vladimir Bacvanski | 26m |
Compares concrete systems and tradeoffs: Akka Streams + Scoobs is used at PayPal for a web crawler and yields about 10x speed versus a multithreaded Java… | ||
| Live coding server-less, single codebase iOS, Android and Web Apps | Irfan Ahmad | 45m |
Claims end-to-end Scala code can produce iOS, Android and web apps from one codebase using Scala.js + React-Native with GraphQL (Graphcool demo). Live demo… | ||
| Building a high-performance Future | Flavio Brasil | 27m |
An open-source reimplementation called trane.io replaces Twitter’s Future internals, trading many per-element closures for shared collector objects and Java… | ||
| Serverless Tensorflow 1.0.0 on AWS. Wait what? | Rustem Feyzkhanov | 15m |
Ports TensorFlow 1.0 / 1.4 and Keras models into AWS Lambda using Docker on Amazon Linux, building a deployment artifact in the lambda-packs repo and loading… | ||
| Intelligent system optimizations | Martin Zapletal | 24m |
Shows that tuning ~20 Spark and system config knobs in experiments changes job runtime from about 10 to 90 seconds, and demonstrates TensorFlow/Keras code for… | ||
| Druid Lookups for High Cardinality Dimensions | Pavan | 15m |
This talk video is a node in devreal.ai, the developer community graph.
It connects to:
Talk: Druid Lookups for High Cardinality Dimen... —… | ||
| Streamlio: Towards a Unified End-to-end Solution | Sanjeev Kulkarni, Sijie Guo | 36m |
A unified real‑time stack combines Apache Pulsar (multi‑tenant, low‑latency pub/sub) with Apache BookKeeper (durable, segment‑centric log storage) and Heron… | ||
| Immutable AWS Deployments with Packer and Jenkins | Manish Pandit | 43m |
Shows how to use Packer AMI builder plus shell provisioners to bake OpenJDK, Tomcat and a hello-world WAR into immutable AWS AMIs, then launch instances that… | ||
| Build a Modern, End-to-End, Big Data Scala Reference Application | Edgar Orendain | 43m |
A runnable Scala reference pipeline implements IoT truck sensors, joins truck and traffic streams, flags violations and computes average speed, then persists… | ||
| Apache SystemML: State of the Project and Future Plans | Frederick Reiss | 30m |
Project is now a top-level Apache offering and ships R and Python APIs plus an experimental Scala DSL that compiles matrix-based code into execution plans. A… | ||
| Fantastic ML apps and how to build them | Matthew Tovbin | 38m |
Salesforce builds an AutoML stack called Optimus Prime that uses Scala + Spark to automate data cleaning, vectorize feature generation, automatic feature… | ||
| VEGAS: The missing Matplotlib for Spark | Roger Menezes | 22m |
Vegas is a Scala plotting library that aims to be the “missing Matplotlib for Spark” and shows concrete features not promises it works with Spark DataFrame and… | ||
| Lawful AI | Adam Pingel | 21m |
Argues that Scala’s FP libraries rarely serve data scientists and that building lawful libraries using abstract algebra (Spire, HLearn patterns) yields… | ||
| A Tale of Two Graph Engines on Spark | Russell Alexander Spitzer, Artem Aliev | 38m |
Compares two Spark graph engines: Apache TinkerPop OLAP (Gremlin on Spark) and GraphFrames, showing concrete trade-offs. TinkerPop uses a pair RDD adjacency… | ||
| Deep distributed decision trees on Apache Spark | Feynman Liang | 20m |
Yggdrasil is a Scala package for Apache Spark that trains deep decision trees by column-partitioning instead of row partitioning, keeping features sorted and… | ||
| End-to-End Computation on the GPU with a GPU Data Frame | Todd Mostak | 24m |
MapD runs SQL, visualization and model prep entirely on GPUs and demonstrates end-to-end zero-copy IPC between processes using Apache Arrow as the on‑GPU… | ||
| Stream All The Things! | Dean Wampler | 36m |
Compares concrete streaming engines and trade-offs: Kafka as durable event log, Spark and Flink for cluster-parallel processing, Akka and Kafka Streams as… | ||
| Strato: Twitter’s Virtual Database Powered by Microservices | Mike Solomon | 24m |
Strato exposes a unified catalog of datasets and adapters to existing services, so a feature that previously required a new microservice and deploy becomes a… | ||
| Magnolia: Generic Derivation 2.0 | Jon Pretty | 25m |
Magnolia is a ~400-line Apache-2 macro that generates typeclass instances for case classes, sealed traits, case objects, value classes and tuples, with special… | ||
| Complex Machine Learning Pipelines Made Easy | Chris Rupley, Till Bergmann | 34m |
Salesforce describes a Scala library called Optimus Prime that sits on top of Spark and automates building thousands of multi-tenant, per-customer models by… | ||
| Scaling From Research to Production with Skymind DL4J and ScalNet | Ari Kamlani | 40m |
Shows how DL4J/ScalNet takes a Keras/TensorFlow prototype into a JVM production pipeline: ingest via DataVec with on-the-fly batch transforms, ND4J off‑heap… | ||
| Turning a Relational RDBMS Table into a Spark Datasource | Kuassi Mensah | 40m |
A Spark DataSource implementation for Oracle exposes relational tables to Spark with parallel direct access, generating logical splits (single, rows, block… | ||
| An introduction to Xtract | Thayne McCombs | 22m |
Xtract is a small Scala library that turns XML into Scala objects and is proven by importing IDML (InDesign) into Lucidpress JSON. It provides an XPath-like… | ||
| Scala DSL for ML Training Set Stratification | Shiva Chaitanya | 21m |
A Scala DSL for user training-set stratification runs on Apache Spark and expresses targets as counts or percentages, expands rules like country.each, and uses… | ||
| Building a High-Performance Database with Scala, Akka, and Spark | Evan Chan | 40m |
FiloDB is a production-ready columnar analytics DB built with Scala, Akka Cluster, Spark and Monix that ingests high-volume streams and serves low-latency… | ||
| Featran, Featran77 - Generic Feature Transformer for Data Pipelines | Neville Li | 38m |
Featran (F77) is a Scala library that builds reusable Map→Reduce→Map feature transformers, computes summaries (min/max, label sets) once and reuses them as… | ||
| Satellite data monitoring for analytical models | Alex Kudriashova | 14m |
Astro Digital streams calibrated multispectral imagery (10–30 m, 200×200 km tiles) from public archives plus a Landmapper constellation, claiming daily… | ||
| Real Time ML Pipelines in Multi-Tenant Environments | Karl Skucha, Yan Yang | 38m |
Built a real‑time ML pipeline that keeps training and serving feature logic identical by using a common feature definition, a precomputed feature store and… | ||
| Avoiding Spark Pitfalls at Scale | Long Cao | 21m |
Production Spark on the Scala API focused on concrete fixes: data skew causes executors to OOM so split hot/cold keys and run a skew join by broadcasting the… | ||
| Democratizing data with an internal data pipeline platform | Manoj Mahalingam | 44m |
This talk video is a node in devreal.ai, the developer community graph.
It connects to:
Talk: Democratizing data with an internal data... —… | ||
| Reasonable Scala Compiler | Eugene Burmako | 21m |
An experimental Scala compiler optimized solely for compilation speed claims dramatic wins: a prototype type checker handles an 11k‑line nontrivial corpus in… | ||
| Scaling to Billions of Real-time Recommendations Daily | Ajeet Grewal | 42m |
Twitter runs an in-house multi-tenant graph partition system that holds both forward and transpose follow graphs on nodes, powering a low-latency fan-out… | ||
| Spark Datasets: why they aren't great and what could be done | Flavio Brasil | 23m |
Argues Spark Datasets trade type safety for optimization visibility and shows a working alternative: a Quill-based quoted DSL that converts Scala code into a… | ||
| Return of the Transaction King | Ryan Knight | 40m |
Claims that FaunaDB gives ACID transactions at global scale without GPS atomic clocks and shows Scala examples using its functional query language to create… | ||
| Introduction To Deep Learning | Alexander Tsyplikhin | 45m |
Deep learning here means artificial neural networks with more than one hidden layer that consistently outperform classic ML on large datasets for images, text… | ||
| IoT, Wavelets and Machine Learning in the Smart Home | Keita Broadwater | 21m |
Floor-mounted accelerometers stream at about 1 kHz sampling through a TCP socket into a Kafka producer and Spark Structured Streaming pipeline, where… | ||
| Multi Runtime Serving Pipelines | Stepan Pushkarev | 21m |
An open source runtime treats models as serverless functions with an Envoy sidecar handling API, discovery and routing; pipelines chain heterogeneous runtimes… | ||
| Tame Your Data with Akka Streams | Jacek Kunicki | 46m |
Live code shows Akka Streams parsing gzipped CSV files into ByteString lines using framing (delimiter '\n', max 128, allowTruncation true), mapping lines to… | ||
| Bringing Deep Learning into the Big Data Analytics | Yuhao Yang | 22m |
A distributed deep learning library on Apache Spark implemented in Scala with a Python API and native C bundled as a single jar uses Intel MKL to accelerate… | ||
| Deep Dive: Continuous Delivery for AI Applications with ECS | Asif Khan | 34m |
Shows a working CI/CD pipeline that packages an MXNet inference app into Docker, pushes to Amazon ECR, and deploys via Amazon ECS with CloudFormation and… | ||
| Fireworks - lighting up the sky with millions of Sparks | Thomas Gerber | 48m |
Salesforce Einstein runs Spark pipelines for each customer and scales by turning per-app Azkaban clusters into a template-driven multi-tenant workflow service… | ||
| Marius Eriksen Interview | Marius Eriksen, Alexy Khrabrov | 04m |
GRAIL detects cancers early and pipelines generate up to a terabyte per sample, forcing investment in storage, network and cost-aware compute. Analysis runs on… | ||
| Manish Pandit Interview | Manish Pandit, Alexy Khrabrov | 05m |
A fintech payments platform moves most infrastructure to Amazon and relies on Lambda functions and auto scaling groups to shrink footprint while handling high… | ||
| Kuassi Mensah Interview | Kuassi Mensah, Alexy Khrabrov | 04m |
Oracle product management discusses scaling Oracle + Spark access by partitioning a single relational table so 100 Spark nodes can read in parallel, using… | ||
| Keita Broadwater Interview | Keita Broadwater, Alexy Khrabrov | 05m |
Describes building a pipeline that glues sensors, messaging systems and Spark to get usable inputs and repeatedly hits roadblocks around cleaning… | ||
| John A. De Goes Interview | John A De Goes, Alexy Khrabrov | 06m |
SlamData builds an advanced analytics compiler that translates workflows to MongoDB, MarkLogic, Couchbase, Spark or Postgres and insists on 100% correct… | ||
| Jacek Kunicki Interview | Jacek Kunicki, Alexy Khrabrov | 05m |
Works in Scala and confronts real concurrent-execution bugs: thread pools and execution contexts were causing the app to block until the team separated… | ||
| Andrew Kuhnhausen Interview | Andrew Kuhnhausen, Alexy Khrabrov | 08m |
Describes concrete engineering choices at Domino Data Lab: balancing rapid feature work with enterprise-grade stability, supporting small-to-medium data… | ||
| Adam Pingel Interview | Adam Pingel, Alexy Khrabrov | 03m |
Builds end-to-end products from data science through front-end visualizations and struggles to integrate those layers quickly, aiming for well under 2s… | ||
| Sijie Guo Interview | Sijie Guo, Alexy Khrabrov | 03m |
Co-founder of Streamlio who co-created Apache DistributedLog and chairs Apache BookKeeper focuses on making a messaging+storage stack that scales hundreds or… | ||
| Sanjeev Kulkarni Interview | Sanjeev Kulkarni, Alexy Khrabrov | 04m |
Streamlio claims a messaging architecture that scales to millions of topics in a single seamless subsystem instead of dozens of clusters, and it handles… | ||
| Rustem Feyzkhanov Interview | Rustem Feyzkhanov, Alexy Khrabrov | 03m |
Describes porting real ML stacks — TensorFlow/Keras/Sklearn on AWS Lambda — using the lambda-packs repo, and why packaging native binaries and cold-start… | ||
| Rebecca Ely Interview | Rebecca Ely, Alexy Khrabrov | 03m |
Works on the Spark and NoSQL platform at Bloomberg, which claims to own and process more financial data than any other organization, so scale is a concrete… | ||
| Pathikrit Bhowmick Interview | Pathikrit Bhowmick, Alexy Khrabrov | 03m |
Coatue Management runs Scala and Spark pipelines on multi‑billion‑row datasets and faces severe skew where some nodes overload and others sit idle, breaking… | ||
| Neville Li Interview | Neville Li, Alexy Khrabrov | 04m |
Spotify runs 200 to 300 developers on Scala data pipelines and faces concrete scaling pain points: immutable collections create lots of tiny object allocation… | ||
| Fred Reiss Interview | Frederick Reiss, Alexy Khrabrov | 03m |
Work on Apache Spark focuses on scaling transformations for very wide tables, for example recoding string columns when there are millions of distinct values… | ||
| Evan Chan Interview | Evan Chan, Alexy Khrabrov | 04m |
Built FiloDB and Spark Job Server, and describes a columnar real‑time distributed query engine using Storm, Spark, Kafka, Cassandra and Scala/Akka. Scaling… | ||
| Devin Loftis Interview | Devin Loftis, Alexy Khrabrov | 06m |
Company authenticates email traffic and reports 3 billion messages validated over three years, requiring sub‑second lookups to avoid phishing delivery… | ||
| Alex Kudriashova Interview | Aleksandra Kudriashova, Alexy Khrabrov | 05m |
Platform handles 5–6 petabytes of satellite imagery growing ~500 TB/year, so processing targets only requested windows to avoid moving whole scenes. First-day… | ||
| Adelbert Chang Interview | Adelbert Chang, Alexy Khrabrov | 04m |
Lead engineer at Target describes building automated deployment infrastructure that scales services and batch jobs, using Kubernetes Nomad envoy to move… | ||
| Ben Edwards Interview | Ben Edwards, Alexy Khrabrov | 04m |
A team at Starbucks migrates many developers from a Microsoft stack onto Scala and learns that small config choices break production: aggressive connection… | ||
| Ryan Knight Interview | Ryan Knight, Alexy Khrabrov | 05m |
Argues that strong consistency is more than read-after-write and must include compare-and-swap, so consistency belongs in the database layer not the… | ||
| Thayne McCombs Interview | Thayne McCombs, Alexy Khrabrov | 04m |
Company moves to continuous delivery and faces test coverage, backward-compatibility and release-safety gaps while shipping more often. Relational data is… | ||
| Yuhao Yang Interview | Yuhao Yang, Alexy Khrabrov | 04m |
A library called BigDL runs deep learning on Spark clusters using Scala to give JVM shops model training and inference without moving to Python frameworks. It… | ||
| Paul Cleary Interview | Paul Cleary, Alexy Khrabrov | 04m |
Comcast describes migrating from on‑prem virtualized stacks to hybrid clouds while juggling OpenStack workloads, Docker/OpenShift containerization, and… | ||
| Thomas Gerber Interview | Thomas Gerber, Alexy Khrabrov | 07m |
Salesforce Einstein builds a platform to democratize AI, ingesting CRM data to predict, recommend and automate while wrestling with data processing, automated… | ||
| Vladimir Kostyukov Interview | Vladimir Kostyukov, Alexy Khrabrov | 04m |
Senior engineer at Twitter describes concrete operational work: building and using Finagle to handle massive request loads, running internal trainings to teach… | ||
| Stepan Pushkarev Interview | Stepan Pushkarev, Alexy Khrabrov | 08m |
Hydrosphere.io focuses on shipping ML pipelines into production and reports concrete engineering choices: using a functional paradigm so models are delivered… | ||
| Long Cao Interview | Long Cao, Alexy Khrabrov | 04m |
Builds scalable data pipelines in Scala and Spark to consume alternative data for market signals, describes production failures that only appear at large scale… | ||
| Leif Wickland Interview | Leif Wickland, Alexy Khrabrov | 04m |
Performance is the limiter: Python prototypes are orders of magnitude too slow for real-time ad tech, so the stack relies on Scala and a Spark shell pointed at… | ||
| Haeley Yao Interview | Haeley Yao, Alexy Khrabrov | 04m |
Builds a microservice backend for a Chinese-targeted shopping bot and app using Scala, Cats, microservice architecture. Describes switching from Java to Scala… | ||
| Chris Rupley Interview | Chris Rupley, Alexy Khrabrov | 04m |
Salesforce faces integration headaches from many acquisitions, so the work focuses on unifying disparate systems and handling surprises when scaling beyond… | ||
| Feynman Liang Interview | Feynman Liang, Alexy Khrabrov | 04m |
Gigster aggregates telemetry from Slack, Trello, GitHub, Gmail across hundreds of concurrent projects to produce real‑time status and outcome predictions, and… | ||
| David Cleaver Interview | David Cleaver, Alexy Khrabrov | 03m |
Discusses two years of building services in Scala and ongoing work creating self-service infrastructure that keeps adding clients and multiplying request… | ||
| David Massart Interview | David Massart, Alexy Khrabrov | 03m |
Company builds a lightweight stream routing layer to connect multiple live feeds into distinct processing pipelines, because existing tools lack an obvious way… | ||
| Anya Bida Interview | Anya Bida, Alexy Khrabrov | 03m |
Works as an SRE supporting the Einstein platform and says regular social demos every Friday break down silos by forcing cross-training and visibility — they… | ||
| Alexandre Bergeron Interview | Alexandre Bergeron, Alexy Khrabrov | 07m |
Builds Domino Data Lab’s terminal platform to let data scientists build, train and productionize arbitrary Python and R models while keeping services isolated… | ||
| Chris Phelps Interview | Chris Phelps, Alexy Khrabrov | 03m |
Platform teams at Tendril wrestle with analytics at scale by balancing heavy batch jobs that spike load against live, low-latency user queries, and describe… | ||
| Adriaan Moors Interview | Adriaan Moors, Alexy Khrabrov | 04m |
Discussion centers on keeping Scala useful by prioritizing balance between features and simplicity and sometimes removing complex additions rather than… | ||
| Vladimir Bacvanski Interview | Vladimir Bacvanski, Alexy Khrabrov | 05m |
Distributed processing at scale improves throughput but networked microservices introduce latency, so the next optimization is to group services onto the same… | ||
| Ryan Delucchi Interview | Ryan Delucchi, Alexy Khrabrov | 06m |
Verizon Labs uses free monads but finds trampoline overhead makes them too slow on hot paths, so teams replace them with finally tagless in… | ||
| Rob Norris Interview | Rob Norris, Alexy Khrabrov | 02m |
Gemini Observatory software team reports a gradual migrated from Java to Scala over six years and now relies on functional programming to tame a large legacy… | ||
| Rajesh Muppalla Interview | Rajesh Muppalla, Alexy Khrabrov | 03m |
At Indix built a catalog and machine learning facility and address the last mile problem in machine learning: reproducibility and self-serve deployment remain… | ||
| Petr Zapletal Interview | Petr Zapletal, Alexy Khrabrov | 06m |
Focuses on cost and state at scale: uses the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka), targets systems with millions of concurrent users, and… | ||
| Nikola Kasev Interview | Nikola Kasev, Alexy Khrabrov | 04m |
Software engineer at ING describes tackling long lived application complexity by designing for change and reasoning about composability, drawing on Scala and… | ||
| Michael Pilquist Interview | Michael Pilquist, Alexy Khrabrov | 03m |
Works on control systems at Comcast that manage tens of millions of cable devices and are intended to last 10–20 years. Describes using Scala libraries like… | ||
| Martin Zapletal Interview | Martin Zapletal, Alexy Khrabrov | 04m |
Builds globally distributed, elastic systems that tolerate catastrophic data center failures and common node losses while keeping latency and infrastructure… | ||
| Does Your Privacy Scale? | Devin Loftis | 22m |
Regulations shift privacy work from product to engineering and demand concrete changes: Privacy by Design means store only necessary fields, isolate services… | ||
| Vlad Patryshev Interview | Vlad Patryshev, Alexy Khrabrov | 05m |
A production system on AWS processes incoming data as streams and requires layered aggregations per minute, hour, week and year, forcing a choice between store… | ||
| Vincent Marquez Interview | Vincent Marquez, Alexy Khrabrov | 04m |
Work centers on designing safe APIs and choosing back pressure trade-offs versus fully asynchronous behavior, with no one-size answer and decisions driven by… | ||
| Interview | Todd Mostak | 07m |
MapD runs a SQL database and visual analytics entirely on GPUs, compiling SQL into GPU machine code with an LLVM compiler and cutting workloads that once… | ||
| Interview | Tim Perrett | 04m |
Infrastructure engineering focuses on enabling internal developers at massive scale and treats organization shape as a technical constraint, invoking Conway's… | ||
| Interview | Thomas Kim | 05m |
Works on data infrastructure for Iterable, using Elasticsearch to support analytics and transaction workloads and Scala and functional programming for clearer… | ||
| Interview | Shiva Chaitanya | 04m |
Netflix runs depersonalization infrastructure that collects behavior from 100 million members and funnels it into both open-source and proprietary ML… | ||
| Interview | Sergei Winitzki | 05m |
Describes backend ML and data pipelines at Workday and concrete steps taken to keep data correct, secure and elastic as volumes grow. Explains moving a slow… | ||
| Interview | Till Bergmann | 03m |
An interview with Till Bergmann, a Senior Data Scientist at Salesforce, focused on his work and connections within the developer community around Scale By the… | ||
| Futures: Twitter vs Scala | Vladimir Kostyukov | 21m |
Compares Twitter Futures and Scala Futures by naming concrete features and tradeoffs: Twitter Futures use local scheduling via a tiny thread‑local scaler to… | ||
| Functional Programming for Machine Learning Panel | Malcolm Greaves, Vitaly Gordon | 59m |
Panel debates whether functional programming will dominate ML, citing concrete tools: Scala and Haskell, Rust, TensorFlow (now offering imperative mode)… | ||
| Composable Parallel Processing in Apache Spark and Weld | Matei Zaharia | 42m |
Explains how Apache Spark’s DataFrames and Datasets expose a schema-aware DSL and runtime (Spark SQL/Tungsten) that enables filter pushdown, runtime codegen… | ||
| Storage, Then and Now | Evan Weaver, Boaz Avital | 35m |
History of Twitter storage turns into two concrete recipes: a custom internal system called Manhattan built ~6 years ago to be a multi-tenant self-service… | ||
| The Fun of Functional Programming Panel | Bill Venners, Julie Pitt, Dick Wall, John A De Goes, Michael Pilquist, Stew O'Connor | 57m |
Panel argues that functional programming makes code understandable by turning components into pure functions as equations and using types to encode meaning… | ||
| The Legends of Twitter and Beyond: Real-World Architectures at Scale Panel | Ben Hindman, Evan Weaver, Helena Edelson, Marius Eriksen, Mark McBride, William Morgan | 01h 06m |
Panel traces concrete systems born at Twitter and spun into companies: finagle as the RPC stack powering services, linkerd repackaged and renamed to create the… | ||
| Mesos, Then and Now | Ben Hindman, Ian Downes | 45m |
Apache Mesos begins as Berkeley’s Nexus in 2010 and becomes Twitter’s cluster fabric by 2012, paired with Aurora scheduler to run JVM services and improve… | ||
| Nelson: Rigorous Deployment for a Functional World | Tim Perrett | 43m |
Nelson is an open-source Verizon deployment system built from free algebras and coproducts that turns application topology into a graph-based dependency model… | ||
| Building systems on top of Kubernetes | Alexandre Bergeron | 15m |
A small Scala reverse proxy is built to act as a Kubernetes Ingress Controller using Kubernetes event streams, ingresses, services and kube‑dns; it uses the… | ||
| Declare, verify and execute microservices-based process flows | Nikola Kasev | 39m |
ING builds and runs an open-source Scala library called Baker that declares orchestration as an internal DSL (recipes of interactions, ingredients and sensor… | ||
| Introduction to Machine Learning | James Ward | 40m |
Covers building a product recommender using ALS alternating least squares implemented with Spark ML + Scala, training on user-item “likes” and producing… | ||
| Interview | Bill Venners | 04m |
Artima moves a legacy website to AWS and integrates old and new systems while the company scales from one to seven people, forcing delegation and hiring… | ||
| Interview | Remy DeCausemaker | 09m |
Twitter maintains 150+ repos and focuses less on infra and more on automating community workflows — comm ops — to speed onboarding, reduce bad actors, and… | ||
| Interview | Artem Aliev | 04m |
Works at DataStax and describes concrete engineering pain: integrating TinkerPop/Cassandra distributions and subtle distribution differences that break… | ||
| The Functor, Applicative, Monad talk | Adelbert Chang | 35m |
Defines Functor, Applicative, Monad with concrete Scala types and code patterns: Option, Either, State as examples; shows map for Functor, zip and pure (or… | ||
| Continuous Delivery Principles for Machine Learning | Rajesh Muppalla | 33m |
This talk video is a node in devreal.ai, the developer community graph.
It connects to:
Talk: Continuous Delivery Principles for Machi... —… | ||
| nothing matches | ||