Conference

When a table crosses one terabyte, MySQL does not fail. It just starts charging for everything. A single index change takes over a week, blocks every other change queued behind it on the cluster, and demands a full shadow copy of the table just to run. At Bolt, whose ride-hailing, micromobility, delivery, and car rental […]

When Databricks introduced LTAP (Lake Transactional and Analytical Processing) at this year’s Data + AI Summit, it sparked an important conversation about the future of enterprise data platforms. The announcement deserves attention because it recognizes a fundamental shift: modern AI applications can no longer treat transactional and analytical data as separate worlds. We agree. Where […]

Believing in Growth: A Hard Question From a Distributed Systems Veteran A few years ago, I was talking with the chief architect of a very well-known global company that offers a home sharing marketplace. He opened with a genuinely tough question. “Siddon, I have spent decades building distributed systems. Google Bigtable, for one. I have […]

Cloud-native databases have made it easier to ship transactional applications. Managed services, automatic failover, and elastic storage are now table stakes. The harder problems show up later: When a multi-tenant SaaS app starts hitting tail-latency spikes during compaction, when a serverless tier saturates a noisy neighbor, or when a dedicated cluster sits at 30% utilization […]

Editor’s note: This post originally appeared on The New Stack and is republished with permission. The original version is available here. When enterprises talk about artificial intelligence, the attention usually points to models: larger parameters, faster inference, cheaper tokens. But we at next-gen database maker PingCAP contend that this framing misses the most consequential change now […]

Editor’s note: This post originally appeared on The New Stack and is republished with permission. The original version is available here. For decades, database designers have built distributed databases around the assumption that storage must live close to compute. The farther data travels over the network, the reasoning goes, the greater the potential for delay. Local […]

Reducing P999 latency in distributed databases is one of the hardest challenges in modern OLTP systems. A handful of slow requests can cascade across services, break SLOs, and directly impact business outcomes, especially in latency-sensitive environments like trading platforms and real-time applications. This is the challenge of tail latency. As systems scale, variability compounds: queueing […]

Editor’s note: This post originally appeared on The New Stack and is republished with permission. The original version is available here. Data teams that thrived in the last wave of Software as a Service (SaaS) platform scale weren’t the ones that chased hype. They were the ones that made a few smart decisions: They adopted cloud-first […]

According to the 2025 Kubernetes Cost Benchmark Report, average CPU utilization across Kubernetes clusters is approximately 10 percent. For many teams, that raises an obvious question: why is database utilization so low? The answer is not poor engineering. It is structural. Modern cloud-native database scaling models are built around peak capacity planning. To protect availability, […]

Dive deep into two popular MySQL alternatives and discover why TiDB is a better option for extreme scalability and real-time analytics.

Every fast-growing SaaS platform eventually faces the same reality: scaling a single massive database becomes increasingly risky and expensive. At Bling (Part of the LWSA Group), a leading SaaS ERP platform serving the e-commerce markets in Brazil and Mexico, we hit this ceiling hard. Founded in 2009, Bling powers over 300,000 daily active users and […]

When AI workloads first started hitting production systems, many teams assumed the pressure would fall on compute. More GPUs, faster CPUs, larger memory pools — that felt like the obvious scaling path. But that’s not what broke first. Systems didn’t fail because they ran out of compute. They failed because they ran out of storage […]