OSA Con 2022
who it's for
data engineers, database engineers, analytics engineers, streaming engineers, data platform architects, site reliability engineers, business intelligence engineers
topics
ClickHouse performance and architecture; ClickHouse materialized views and compression; ClickHouse as observability backend (Jaeger); real-time analytics with Redpanda and ksqlDB; Apache Pulsar to Apache Pinot pipelines; time series databases and Prometheus integration; Apache Iceberg and open table formats; HTAP database architectures; Presto for SQL lakehouse queries; building event collection SDKs and data models; scaling pandas with Modin; open source headless BI and cloud analytics
| title | author | length |
|---|---|---|
| Welcome to OSA CON, Version 2022 | Robert Hodges | 14m |
Kickoff overview for OSA Con 2022 outlines logistics and content with 26 talks, 31 presenters recorded on Zoom and slides promised within days, plus Q&A via… | ||
| ClickHouse® - What is Behind the Fastest Columnar Database? | Olena Kutsenko | 34m |
Explains why ClickHouse processes billions of rows and claims tens of GB/s by using columnar storage, per-column compression codecs, vectorized execution and a… | ||
| Tips & Tricks to Keep Your Queries under 100ms with ClickHouse® | Javi Santana | 42m |
Claims sustained p99 under 100ms at 100–500 qps on ClickHouse with a 4‑replica cluster holding 177 billion rows and examples querying ~170 million rows. Key… | ||
| Quick Reflexes - Building Real-Time Data Analytics with Redpanda & ksqlDB | Sri Sakthivel M.D. | 23m |
Shows a concrete pipeline: MySQL change events flow via dbcm connect into Redpanda, are consumed by ksqlDB, and the demo runs entirely from a docker-compose… | ||
| Using ClickHouse® Database to Power an Analytics and Customer Engagement Platform | Prafulla Gupta | 17m |
Times Internet describes GrowthRx, an in‑house analytics and engagement platform built on ClickHouse that ingests 30–40k req/s (tested to 100k by adding… | ||
| Specifics of Data Analysis in Time Series Databases | Roman Khavronenko | 34m |
Compares VictoriaMetrics, Prometheus and ClickHouse with concrete examples: Grafana uses Prometheus API start/end/step so time filtering and resolution are… | ||
| Building Event Collection SDKs and Data Models | Paul Boocock | 40m |
Describes building and shipping 20+ SDKs for web, mobile, IoT and server use, all grounded by JSON Schema validation so events are strongly typed and… | ||
| Building a Real-time Analytics Application with Apache Pulsar and Apache Pinot | Mary Grygleski, Mark Needham | 43m |
Live pipeline ingests Wikimedia server‑sent events with the Pulsar Python client, publishes to a Pulsar topic, and lets an Apache Pinot real-time table consume… | ||
| The Open Source Analytic Universe, Version 2022 | Robert Hodges | 23m |
Open Source analytics now shows concrete shifts: ClickHouse powers many cloud services and forks, Altinity runs altinity.cloud as an early managed ClickHouse… | ||
| State of Open Source Databases | Peter Zaitsev | 25m |
Surveys 2022 trends: analytical systems move to distributed architectures with storage/compute separation, many teams run 10 or more databases, and serverless… | ||
| HTAP vs OLTP/OLAP | Peter Zaitsev, Ed Huang, Alexander Zaitsev, Robert Hodges | 29m |
HTAP claims to let a single system serve OLTP and OLAP simultaneously; implementations either keep row and column replicas synchronized or separate engines… | ||
| Open Source Analytics, Public Clouds, and VC Mega-Investment | Doug Cutting, Maxime Beauchemin, David Nalley, Robert Hodges | 44m |
Venture money accelerates work but creates a real risk when a single open project becomes a single startup: one-to-one project→startup risk leads to… | ||
| Signal Correlation, the Ho11y Grail | Michael Hausenblas | 15m |
Demonstrates using ClickHouse exporter plus OpenTelemetry to ingest logs, traces and metrics and actually link them; shows a working metrics→traces flow where… | ||
| Arrow in Flight - New Developments in Data Connectivity | David Li | 33m |
Apache Arrow standardizes a columnar memory layout and IPC so projects avoid costly conversions and share data zero‑copy across languages. Real-world wins… | ||
| Apache Iceberg - An Architectural Look Under the Covers | Alex Merced | 52m |
Explains how Apache Iceberg replaces folder-based tables with a metadata tree (catalog → metadata file → manifest list → manifests → data files) and uses… | ||
| Building an Analytic Extension to MySQL with ClickHouse® and Open Source | Vadim Tkachenko, Kanthi Subramanian | 42m |
Argument: move analytics out of MySQL because a 170 million row aggregation runs 170M rows: MySQL 9 min vs ClickHouse® 0.5s and another average-by-year takes… | ||
| Streaming Data Made Easy | Timothy J Spann, David Kjerrumgaard | 35m |
Claim: Apache Pulsar can run as a single, cloud‑native streaming platform that speaks multiple protocols and streams into analytics stores. Concrete stack… | ||
| What Data Engineering Can Learn from Front-end Engineering | Pete Hunt | 39m |
Cloud warehouses (BigQuery 2010, Redshift 2012, Snowflake 2014) enable more ambitious pipelines, producing thousand-line SQL and brittle imperative… | ||
| Presto for Your Fast, Open SQL Lakehouse | Steven Mih | 22m |
Presto is presented as the SQL engine for an open S3-backed lakehouse using Parquet and table formats like Apache Hudi, Iceberg and Delta Lake, claiming… | ||
| Switching Jaeger Distributed Tracing to ClickHouse® to Enable APM | Satbir Chahal | 18m |
Migrates Jaeger storage from Cassandra to ClickHouse, replacing a Bitnami Cassandra Helm chart with the Kubernetes ClickHouse operator and wiring the Jaeger… | ||
| Extract, Transform and Learn About Your Developers | Alexandra Gronemeyer, Brian Leonard | 29m |
Built a pipeline that extracts GitHub data for four repos with Airbyte, loads into ClickHouse, transforms with dbt and visualizes in Metabase. Daily syncs hit… | ||
| How Can HTAP Databases Simplify Your Data Infrastructure? | Liquan Pei | 36m |
Demo ingests 5+ billion GitHub events into TiDB and serves real-time analytics without Kafka/Spark/warehouse/caching layers, claiming a single HTAP system… | ||
| To Warehouse or to Lakehouse? And How Do Various Open Source Project Compare? | Rachel Pedreschi | 40m |
Compares warehouses (Snowflake, Redshift, ClickHouse, Druid, Pinot) vs lakehouses built on Parquet/ORC with table formats like Hoodie, Iceberg, Delta Lake and… | ||
| Scaling Your Pandas Analytics With Modin | Doris Lee | 21m |
Ponder offers Modin as a drop-in replacement for pandas that parallelizes DataFrame ops (supports Dask and Ray backends and single-node use), claiming… | ||
| Navigating the Modern Data Stack with Open Source Headless BI | Artyom Keydunov | 29m |
Headless BI extracts the semantic layer — data model as code, access control, caching/pre-aggregations and an API — and serves it upstream so multiple… | ||
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