Modern Data Engineering & Architectures

The online world we interact with today is increasingly powered by data and by insights extracted from that data. Our ever-growing thirst for data insights and data-driven behavior (e.g. ML-based systems) is driving our industry to collect data more often from an increasingly varied set of sources. With increased amounts of data, scale becomes a challenge. To complicate matters further, customers want reliable access to high-quality data and insights. This adds availability and data quality to our list of requirements. More often than not, customers require low-latency as well, often referring to the time it takes raw data to be converted into usable insights or production-grade models. Last but not least, access patterns and use-cases dictate the form data will take when being served!

Depending on how the data will be used, the medium used to store and serve it will vary widely. OLTP/OLAP DBs, caches, object stores, search engines, graph DBs, data streams, vector DBs, and the like represent the many forms data takes to be suitable to its many uses. Come to this track to learn about new technologies, practices, and trends shaping the way you will work with data.

From this track

Session Graph Databases

LIquid: A Large-Scale Relational Graph Database

Monday Oct 2 / 10:35AM PDT

We describe LIquid(1 2), the graph database built to host LinkedIn.

Speaker image - Scott Meyer
Scott Meyer

Distinguished Software Engineer @LinkedIn, Creator of the Graph Database, LIquid, Metaweb/freebase Alum

Session Data

PRQL: A Simple, Powerful, Pipelined SQL Replacement

Monday Oct 2 / 11:45AM PDT

Most databases use SQL as the interface to access relational data. Because of that, we associate SQL to be the language of relational algebra. But its affinity with the English language and unclear and inconsistent semantics leave a lot of space for improvements.

Speaker image - Aljaž Mur Eržen
Aljaž Mur Eržen

Compiler Developer @EdgeDB & PRQL Maintainer

Session Stream Processing

Streaming Databases: Embracing the Convergence of Stream Processing and Databases

Monday Oct 2 / 01:35PM PDT

Streaming databases have gained significant attention in recent years. From its name, it is evident that a streaming database combines the power of stream processing and databases.

Speaker image - Yingjun Wu
Yingjun Wu

Founder and CEO @RisingWave Labs, Previously Engineer @AWS Redshift & Researcher @IBM Research Almaden

Session Distributed Systems

Redesigning OLTP for a New Order of Magnitude

Monday Oct 2 / 02:45PM PDT

The world is becoming more transactional. From colocation and server rental to serverless and usage-based billing. From coal to clean energy and smart meters that arbitrage solar prices 1440 times a month instead of monthly. Not to mention FedNow or the tsunami of instant payments.

Speaker image - Joran Greef
Joran Greef

Founder and CEO @TigerBeetle

Session Data Lakes

Incremental Data Processing with Apache Hudi

Monday Oct 2 / 03:55PM PDT

Incremental Data Processing is an emerging style of data processing gathering attention recently that has the potential to deliver orders of magnitude speed and efficiency over traditional batch processing on data lakes and data warehouses.

Speaker image - Saketh Chintapalli
Saketh Chintapalli

Software Engineer @Uber, Bringing Incremental Data Processing to Data Warehouse Models

Speaker image - Bhavani Sudha Saktheeswaran
Bhavani Sudha Saktheeswaran

Distributed Systems Engineer @Onehouse, Apache Hudi PMC, Ex-Moveworks, Ex-Uber, Ex-Linkedin

Session Architecture

Sleeping at Scale - Delivering 10k Timers per Second per Node with Rust, Tokio, Kafka, and Scylla

Monday Oct 2 / 05:05PM PDT

As a part of OneSignal’s no-code Journeys system, we knew that we would need a way to store billions of timers.

Speaker image - Lily Mara
Lily Mara

Engineering Manager @OneSignal, Author of "Refactoring to Rust"

Speaker image - Hunter Laine
Hunter Laine

Software Engineer @OneSignal

Date

Monday Oct 2 / 10:35AM PDT

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Track Host

Sid Anand

Chief Architect @Datazoom, Committer/PMC Apache Airflow, Previously: Netflix, LinkedIn, eBay, Etsy, & PayPal

Sid currently serves as the Chief Architect and Head of Engineering for Datazoom, where he and his team build high-fidelity, low-latency data streaming systems. Prior to joining Datazoom, Sid served as PayPal's Chief Data Engineer, where he helped build systems, platforms, teams, and processes, all with the aim of building access to the hundreds of petabytes of data under PayPal's management. Prior to joining PayPal, Sid held senior technical positions at Netflix, LinkedIn, eBay, & Etsy to name a few. He earned my BS and MS degrees in CS from Cornell University, focusing on Distributed Systems.

Outside of work, Sid advises early-stage companies and several conferences. Once an active committer on Apache Airflow, he is now mostly a fan.

Sid's body of work includes but is not limited to :

  • The world's first cloud-based streaming video service -- I was the first engineer to work on the cloud at Netflix
  • LinkedIn's Federated Search Typeahead (a.k.a. auto-complete)
  • LinkedIn's (Big Data) Self-service Marketing Analytics tool
  • PayPal's DBaaS - an internal self-service system to provision & manage heterogenous databases
  • PayPal's CDC - an internal self-service CDC system to stream DB updates to nearline applications
  • eBay-over-Skype : Following the Skype-acquisition, I built a P2P version of eBay offers
  • eBay's Best Match Search Ranking Engine powered by an In-Memory Database
  • eBay's Fuzzy-match name/email Search
  • Agari's Data Platform : Batch & Streaming Predictive Data Platform as a Service
  • Datazoom's Platform : High-fidelity, Low-latency Streaming Data Platform as a Service
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