TiDB SCaiLE 2026: Five Sessions You Don’t Want to Miss

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LinkedIn, AMD, Braintrust and more share what actually works in production. October 15, Mountain View.

SCaiLE-2026 (https://info.pingcap.com/e3t/Ctc/I4+113/cxmyM04/VVKjdD7nMpp8N8f2b-vz2SxpW3Dl4Sm5Vxx3fN8fXWCq3prCCW95jsWP6lZ3mLW8qnTm_6DGLXsW3S1j4D2ryZnRW859y4B8DD_5TW4b5f426cjwhCW8Rt9ts1MhVfSW1_GRQw4H5ndjW2LgDXF8sGKZnW3R2Xvt6RCKw3W3gqVRZ4qYzRkW551pvL7jsD3PW67nHGT6sTRlVW26BrFR8wTYWxW5BFYXP4xsYxDW4TSCvl6MN7tRW618Y677ZTPMhN8YLrvKVH5YrW3yDsl441qRCtW61NBRw7mJ5ftW36mhxQ84vxDSVgQ2F06nxcR_W2sMpvn8JhdbVW9c7w8H2NYr17W3J-52n8_mQCsW9bjw7l97Fmt_W57gKbp62XwMfW5t6xGJ8yD0LnW1bNwq78WtzzXW1ScTp-4skgHwW7rS2M48mFcTqW2R9Fst7yxYs6dMGgHM04 )

Hi Emre,

October 15 is coming up fast, and the SCaiLE stage is filling up. Before the full agenda drops, here's an early look at five sessions that are worth the trip on their own.

These talks are field reports from the engineering teams working on the hardest problems in data infrastructure. Each one covers a real system, real numbers, and the tradeoffs the team made along the way.

Register Now
(https://info.pingcap.com/e3t/Ctc/I4+113/cxmyM04/VVKjdD7nMpp8N8f2b-vz2SxpW3Dl4Sm5Vxx3fN8fXWCq3prCCW95jsWP6lZ3mLW8qnTm_6DGLXsW3S1j4D2ryZnRW859y4B8DD_5TW4b5f426cjwhCW8Rt9ts1MhVfSW1_GRQw4H5ndjW2LgDXF8sGKZnW3R2Xvt6RCKw3W3gqVRZ4qYzRkW551pvL7jsD3PW67nHGT6sTRlVW26BrFR8wTYWxW5BFYXP4xsYxDW4TSCvl6MN7tRW618Y677ZTPMhN8YLrvKVH5YrW3yDsl441qRCtW61NBRw7mJ5ftW36mhxQ84vxDSVgQ2F06nxcR_W2sMpvn8JhdbVW9c7w8H2NYr17W3J-52n8_mQCsW9bjw7l97Fmt_W57gKbp62XwMfW5t6xGJ8yD0LnW1bNwq78WtzzXW1ScTp-4skgHwW7rS2M48mFcTqW2R9Fst7yxYs6dMGgHM04 )

Here's what's on stage:

Varun Mohta (https://info.pingcap.com/e3t/Ctc/I4+113/cxmyM04/VVKjdD7nMpp8N8f2b-vz2SxpW3Dl4Sm5Vxx3fN8fXWCq3prCCW95jsWP6lZ3n5W3fd2Yr46W4BqN1f9fFWhj-8pW7Yzrjy59V0TcW5xN8Pg1BDGtxW6lw0xG2LL8DTW8r0cMm1YzRfgW7BTkjD4LZzZCW1Fhfg58yVX0HN34sGWG4Vv9wW6xrX1g7ZYvSdW6cnrS38sSB_7W1S3MhN5KbF7yN4_Fr5ZrBLmpW7vNqfD34HSyvW43rz9T3G8sYhN7lYN8zty7pRW5gYM7c8nsV57W6w__Cg2VH2mRW9fCbx_7H5kCPW17ktTl7dGkltW5LQT9N1S3Fv3VbRNN638Z_4fW4s8rf62W_8gHW3n4jMl3_vfDJV5z63k6KXcl1VFqTQQ16c8w9W5J7GRz1MWV82W4cJnmL6rdMBBW24h8Mx4_GzLlW3D-6Kv8654Pdf4thysH04 )

Beyond Monolithic & NoSQL: Scaling High-Concurrency State Machines on TiDB

Varun Mohta | LinkedIn

Stateful orchestration engines like Temporal hit hard limits on monolithic databases. Static sharding across MySQL only moves the ceiling, and it dead-ends when a workflow's rows are pinned to a shard that can't be rebalanced. Varun shows how LinkedIn broke through that wall with TiDB, including the shared-lock fix they built in production. Their cluster now sustains roughly 170,000 QPS and thousands of workflows per second, and it's scaling toward 3,000 per second.

Terry (https://info.pingcap.com/e3t/Ctc/I4+113/cxmyM04/VVKjdD7nMpp8N8f2b-vz2SxpW3Dl4Sm5Vxx3fN8fXWCq3prCCW95jsWP6lZ3p8W6Q1mrY2Xrj7ZW5KbCMh65cc9xW7LxCth8slKMqW48zpY47Dnv0LW25yscq4jGHbSW5yT2Dc86LnHhW4Lz8cy1d-9ppW5PkKqK5N_fjWW5DjvGy61njKQW1yjfVj7vKt7TW6-hbRb1Ct1ShW61CKgT8lFQlpW5-pPnj39K0wRW6CQJkL36hN6BW6vQJWd45HXXmN2LB5PG8lkb-W83Q74x7gHYpcW6zVlH343zSxVN8dz3DY3L0-sW98h_l57FzkzmVBlytq6QHZBTW3bdG9w8GFpCJW2yfRs73WLcxXW2TrBbS37S6G1W91HfQp4MZQH-W1pzcd-2YrJ5sW47Sl6t5F0g_gW8bC8qt3GmpHdW5Rfp9s5vQx5_W1dhlJl5-CZnqf8CXhGP04 )

Scaled Into Complexity

Terry Purcell | TiDB

Every growth phase adds another shard, cluster, or pipeline, until infrastructure teams are managing sprawl instead of building what's next. Terry reviewed more than 60 TiDB customer stories and found the same pattern, along with the same way out: consolidating fragmented architectures into a unified data layer. He breaks down how that shift simplifies operations without sacrificing scale, and how it builds a foundation for what comes next, including AI.

Running Production with a Raft of Agents

Tenny Zhuang | Botiverse

Your agents already write code. What happens when they also run your release trains, review each other's work, and hold the production gates? Tenny brings a year of field data: seven production release trains, one live perf incident, and zero data loss. You'll see the evidence and authority structures that make agent work trustworthy at scale. He'll also cover the graduated risk tiers that decide which schema changes ride a release train, and the human red lines that never move.

Ameya Bhatawdekar (https://info.pingcap.com/e3t/Ctc/I4+113/cxmyM04/VVKjdD7nMpp8N8f2b-vz2SxpW3Dl4Sm5Vxx3fN8fXWC63prCCW8wLKSR6lZ3p_W31yBGF325S0gW3yVr4_5qDSxfW1TzYmY6s8Kt5W7PHzjY6cHtN9MYQv0qL82glW5S3fcS8tDHhmW48qxJB4K5TkRW8MTxzF6STCmGW62_CVj29yVxQW2kYnjP2Plyc4W6_K5zm6zXgcGN8gXvSRmV9JMW6jZcyP1LHMVyW32knm_8NTVpLVnK7kD8XyNqwW8W80jc7sKQjMW4mHTlB3Jb79FN1Z8HfqJH_lJW4jV82c3VmDj3W8H1-0w7pr9LhW4mglHb3gXnPlW7JBzV63Sb4PVW7Kx_KS3Vv5wSW40x8y71TKgjGVBr6Wj6n6FwvVYLbRH63pXMWW1bD4Fk3QqdhqN6dPRDvdtpT3f9hpnZK04 )

From Evals to Active Observability: The Continuous Improvement Loop for AI Agents

Ameya Bhatawdekar | Braintrust

Evals only catch the failures you already know to look for. In production, agents invent new ones across more traces than any team can review by hand. Ameya introduces active observability, a continuous quality loop that connects production traces, pattern discovery, debugging, and evals. It turns what agents actually do in the wild into prioritized fixes, and it gives you a way to measure whether agent quality is improving over time.

Nilanjan Chatterjee (https://info.pingcap.com/e3t/Ctc/I4+113/cxmyM04/VVKjdD7nMpp8N8f2b-vz2SxpW3Dl4Sm5Vxx3fN8fXWCq3prCCW95jsWP6lZ3m3W61Tvy_6kSQzQW5qDKDZ5rxNjPW2rDSqb7cnKswW2Qqly18cmgMKW7Tbj6W7HYLt7W6N3TCF4Ym7sQW5DJL7t2BmsKRW77WtcY8Qgs0WW2t3XPP36D__LW3WR91K4zln7DVkl95g8_TS5TN40-zYMzYlR8W1Pn8VN770fZjW3G-ysK2CY9x_W2fNwdx7fjs_6W7pZGJT682_ddW2S2_vF3qt2WvVld_DX7nyPQ0VKwv6R8Y1Lk9W5W1pz391Y7c3W18g1n06nfMvDW5c1z-Q2rfRxGV2qY1M4hSjpXW66S4Qw3z7VGqW4qy1WV7Fp_h1W8_GB9k6kPglTW3DV72_4dN4MBW6mtcFt8jWh09VtDdKh4ywmzjVDrv7q5X9k3Ff51ZwkK04 )

1 Log, 2 Shapes

Nilanjan Chatterjee | AMD

The Lambda vs. Kappa debate asks the wrong question. Nilanjan reframes enterprise data mart design around the reconciliation boundary, and argues that its position matters more than the execution mode. He works through Snowflake hybrid tables and latency-vs-freshness tradeoffs. He also shows the cost break-even math for columnar replicas vs. indexes, which are the calculations that decide which architecture fits your workload.

Seats are limited. Register now (https://info.pingcap.com/e3t/Ctc/I4+113/cxmyM04/VVKjdD7nMpp8N8f2b-vz2SxpW3Dl4Sm5Vxx3fN8fXWCq3prCCW95jsWP6lZ3lFW5r59TR9bNjqQVv15Zr4cMwD0W1lnTQ53Z_GyCW7lPwKk7n3MyfVpw_0M3pc59DVr20BL4zkjVYW1_K6S96Btj36W3LwJf31hpYvMW8Vj8TJ4-Q1BFW1JXnHG8V8cybW3SjMkX5gJmFzW2ZCyWp6yPK-JW5M0XfS1sZn-pW7ssmhX1nS0WwN5bFHpfr7-6TW5xMzmN3J2y7-W8lC3tl39zv4kW5sR2sp6FP27tW19Zx2_8b8GQQW3hHM5f2sLVwsW5B4P-V2l4HC7W95n6Kv7GPXzBW2K_6PD7vrsjxW1RG-FP45s8bjW2pKnN24PgjVtW72K1hn8ZfsW2W95TpV38qSzXmW7NJcNm1vgzTVW1Y565j49WHt5W6k9FxB839rQkf22VW4C04 ) to lock in your spot at the Computer History Museum on October 15.

Got questions? Connect with us at tidbscaile@pingcap.com.

See you in Mountain View.

The TiDB SCaiLE Team

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29 September 2026 · 17:00 UTC