Unconference: Shift-Left Data Architecture
From the same track
Beyond Durability: Enhancing Database Resilience and Reducing the Entropy Using Write-Ahead Logging at Netflix
Tuesday Nov 19 / 10:35AM PST
In modern database systems, durability guarantees are crucial but often insufficient in scenarios involving extended system outages or data corruption.
![Speaker image - Prudhviraj Karumanchi](https://qconsf.com/sites/qcon_sf/files/styles/medium/public/pictures/2024-06/prudhviraj_qcon.png?itok=ZxuU4YjJ)
Prudhviraj Karumanchi
Staff Software Engineer at Data Platform @Netflix, Building Large-Scale Distributed Storage Systems and Cloud Services, Previously @Oracle, @NetApp, and @EMC/Dell
![Speaker image - Vidhya Arvind](https://qconsf.com/sites/qcon_sf/files/styles/medium/public/pictures/2024-06/five.png?itok=zWpNvWzz)
Vidhya Arvind
Staff Software Engineer @Netflix Data Platform, Founding Member of Data Abstractions at Netflix, Previously @Box and @Verizon
OpenSearch Cluster Topologies for Cost-Saving Autoscaling
Tuesday Nov 19 / 11:45AM PST
The indexing rates of many clusters follow some sort of fluctuating pattern - be it day/night, weekday/weekend, or any sort of duality when the cluster changes from being active to less active. In these cases how does one scale the cluster?
![Speaker image - Amitai Stern](https://qconsf.com/sites/qcon_sf/files/styles/medium/public/pictures/2024-06/Amitai_Stern_profile.png?itok=A0eITrO8)
Amitai Stern
Engineering Manager @Logz.io, Managing Observability Data Storage of Petabyte Scale, OpenSearch Leadership Committee Member and Contributor
Stream and Batch Processing Convergence in Apache Flink
Tuesday Nov 19 / 02:45PM PST
The idea of executing streaming and batch jobs with one engine has been there for a while. People always say batch is a special case of streaming. Conceptually, it is.
![Speaker image - Becket Qin](https://qconsf.com/sites/qcon_sf/files/styles/medium/public/pictures/2024-07/bqin.jpeg?itok=8z_z9xuB)
Becket Qin
Principal Staff Software Engineer @LinkedIn
Efficient Incremental Processing with Netflix Maestro and Apache Iceberg
Tuesday Nov 19 / 03:55PM PST
Incremental processing, an approach that processes only new or updated data in workflows, substantially reduces compute resource costs and execution time, leading to fewer potential failures and less need for manual intervention.
![Speaker image - Jun He](https://qconsf.com/sites/qcon_sf/files/styles/medium/public/pictures/2024-08/jun_he.jpg?itok=NdOiu9Cc)
Jun He
Staff Software Engineer @Netflix, Managing and Automating Large-Scale Data/ML Workflows, Previously @Airbnb and @Hulu
Stream All the Things — Patterns of Effective Data Stream Processing
Tuesday Nov 19 / 01:35PM PST
Details coming soon.
![Speaker image - Adi Polak](https://qconsf.com/sites/qcon_sf/files/styles/medium/public/pictures/2024-05/adi.polak_.jpeg?itok=VLJ4ZPm_)
Adi Polak
Director, Advocacy and Developer Experience Engineering @Confluent