Rolling AI tools out across large organisations.
Organizations start with simple initial wins where AI feels exceptionally fast. Next, teams apply AI to bigger problems, causing failures, bugs, and a loss of trust. Finally, teams build real skills by learning proper guardrails, prompting, and context engineering.
▶ 1:11
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Traditional cascaded systems rely on separate speech recognition, language models, and turn-taking detectors that do not work well together. When interacting with confused users like an elderly person, rigid timing parameters cause the agent to aggressively interrupt them. Collapsing these modules into an audio-native model allows the system to better process pause semantics and adapt to real conversational dynamics.
▶ 6:24
How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
Acceptance rate only tracks whether a suggestion was taken, not whether that code actually runs in production or drives business value. Relying on it ignores whether developers had to heavily rewrite or debug the suggested code.
▶ 9:16
Measuring the impact of AI on software engineering – with Laura Tacho
Uber initially saw a modest 10 to 15 percent bump in diff velocity using synchronous tab completion and IDE chat in 2022 and 2023. Over the past year, the paradigm shifted to peer programming, where developers hand off asynchronous workloads to AI agents and act as tech leads directing these agents. This allows engineers to delegate toil like dead code cleanup and library migrations to focus on creative work.
▶ 3:59
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
Industry data shows that approximately ninety-two percent of developers use an AI coding assistant at least once a month. About seventy-five percent use one at least weekly, while AI-authored code hitting production has risen to nearly twenty-seven percent.
▶ 3:58
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
You can use Zerobus, which is an API that handles high-rate, bursty, or tiny-row data ingestion directly into your open lake without requiring a traditional message bus or buffering. Additionally, Spark Real-time Mode enables low-latency streaming operations down to tens of milliseconds.
▶ 17:21
Data + AI Summit Keynote 2026 | Day 1
Microsoft builds massive data center infrastructure by linking power and network optics across multiple regions using a high-bandwidth AI WAN. This allows them to aggregate flops for huge training jobs across sites like Fairwater and Wisconsin.
▶ 0:54
Satya Nadella – How Microsoft thinks about AGI
Large tech incumbents and AI labs are often burdened by operations, product managers, and slow PRD review processes that delay shipping. In contrast, early-stage startups can leverage relentless execution and rapid iteration to capture market feedback. For instance, Cursor famously utilized one-day sprint cycles to ship updates daily, outmaneuvering slower competitors.
▶ 6:13
The 7 Most Powerful Moats For AI Startups
Engineers often lose job satisfaction and burn out when reduced to prompt cycles waiting for AI outputs. Meanwhile, senior engineers holding institutional knowledge become overwhelmed bottlenecks trying to prevent bad agent code from entering the codebase.
▶ 2:57
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
The base model is an audio-native large language model that processes streaming audio chunks and outputs text rather than raw speech. This design enables the enterprise to apply strict safety guardrails and generate structured tool calls, citations, and debugging transcripts. It predicts an initial turn-taking token based on audio frames to determine if the user is still speaking before processing the response.
▶ 10:02
How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
Data from over 180 companies shows that stack trace analysis and refactoring existing code save more time than midloop code generation. Developers spend significant time reviewing generated code, whereas debugging error outputs provides a direct net positive time savings.
▶ 24:34
Measuring the impact of AI on software engineering – with Laura Tacho
As soon as agentic workflows were made available, 70 percent of the workloads pushed by developers were toil tasks. This happened because the start and end states of tasks like library upgrades and migrations are straightforward, leading to much higher accuracy. Because of this success, tackling toil with AI became a top priority for the developer platform.
▶ 6:45
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
AI dramatically reduces onboarding time, cutting the time to a developer's tenth pull request in half between 2024 and late 2025. Furthermore, research shows that these initial productivity gains stick with engineers for at least their first two years at a company.
▶ 6:28
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
Apache Iceberg v3 support allows managed Iceberg tables to share data files with Delta tables without requiring any file rewrites. This establishes a unified data layer where data laid out on disk looks identical regardless of the format.
▶ 20:56
Data + AI Summit Keynote 2026 | Day 1
Microsoft approaches AI pricing by leveraging existing meters like consumption and tiered subscriptions that encapsulate consumption rights. They also rely on market expansion to offset lower incremental margins, similar to how the cloud expanded overall IT spend.
▶ 10:32
Satya Nadella – How Microsoft thinks about AGI
While a basic demo can be hacked together in a weekend, real-world utility requires navigating complex edge cases and maintaining extreme reliability. Process power is earned by meticulously refining AI agents over years to handle high-stakes environments like banking KYC and loan origination. This relentless execution of the final 5% consistency creates a massive barrier against casual competitors.
▶ 10:34
The 7 Most Powerful Moats For AI Startups
Shifting verification left from humans to agents prevents bad code and unlocks major productivity gains. For instance, tools like Playwright and MCP allow agents to autonomously explore and verify code instead of relying on human navigation.
▶ 5:04
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Training datasets incorporate real conversational data from enterprise platforms along with deliberately added background noises using generative models. Sanitizing the environment too much with traditional denoising pipelines actually harms model performance because it removes realistic production conditions. This deliberate exposure ensures the model remains robust against environmental interference and crosstalk.
▶ 18:54
How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
Research by Dora reveals that AI accelerates the coding tasks that developers actually enjoy doing. This leaves them with a higher proportion of unpleasurable work like toil, meetings, and administrative duties.
▶ 31:08
Measuring the impact of AI on software engineering – with Laura Tacho
Uber built a central MCP gateway using a tiger team to proxy both external and internal MCPs from service infrastructure. It exposes these services consistently to engineers while managing authorization, telemetry, and logging. Additionally, they provide a registry and sandbox so developers can discover, test, and ensure new MCPs work as expected.
▶ 9:53
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
AI acts as an organizational multiplier that drives teams in opposite directions depending on their prior health. Dysfunctional organizations experience twice as many customer-facing incidents, while healthy systems see fifty percent fewer incidents.
▶ 8:59
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
Lakebase Postgres allows you to instantly branch petabyte-scale databases in less than a second using a copy-on-write mechanism on lake storage. This allows AI agents to safely experiment, test, and roll back changes without duplicating underlying storage.
▶ 26:00
Data + AI Summit Keynote 2026 | Day 1
Mission Control acts as a control plane and package for multiple AI coding agents, letting developers issue tasks and steer them across independent branches. This allows GitHub to grow regardless of which specific coding agent or model ultimately wins market share.
▶ 13:17
Satya Nadella – How Microsoft thinks about AGI
Forward-deployed engineers embed directly with enterprise customers to understand boring, manual workflows and translate them into software. By working closely with clients who lack access to good software, startups capture tailored time-in-motion data. This custom operational knowledge eventually feeds into proprietary prompts, evals, and fine-tuned models.
▶ 16:44
The 7 Most Powerful Moats For AI Startups
Once you find a useful agent workflow, encoding it into a deterministic flow saves time and tokens. It reserves LLM usage strictly for reasoning tasks while guaranteeing fast, repeatable results for known processes.
▶ 5:46
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
The system employs latency-budgeted reasoning to cap processing time and ensure the agent responds within a predictable window. To prevent users from panicking when backend systems experience delays, developers use audio tricks like computer typing sounds or hold music. Providing this constant feedback builds user trust over the phone and avoids awkward silences.
▶ 28:45
How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
Measuring AI impact requires a holistic framework that tracks utilization, impact, and cost across the lifecycle. Because system data alone cannot capture cognitive load or flow state, combining workflow metrics with developer sentiment surveys is essential.
▶ 13:28
Measuring the impact of AI on software engineering – with Laura Tacho
AIFX is a CLI tool introduced to centrally provision, configure, and update agent clients like Cloud Code, Codepilot, or Cursor. It allows developers to easily install and discover MCPs from the registry and apply standard configuration management. This ensures that engineers new to the space immediately get effective prompts, configurations, and connections to background task infrastructure.
▶ 11:42
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
While individual tool adoption is over ninety percent, organizations struggle because deep transformation is uncomfortable and often abandoned. Applying AI only to individual coding tasks creates a low ceiling of productivity rather than driving true bottom-line results.
▶ 10:43
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
The Unity AI Gateway serves as a single pane of glass to manage all AI spend, models, and MCP tools across an organization. It provides granular observability dashboards, budget limits and rate-limiting down to the individual level, and enforces security and compliance guardrails.
▶ 34:09
Data + AI Summit Keynote 2026 | Day 1
Microsoft places models directly into the middle tier of applications like Excel to teach them native business logic and tool usage. This allows the system to understand formulas and fix reasoning mistakes directly rather than relying on a pixel-level UI wrapper.
▶ 25:20
Satya Nadella – How Microsoft thinks about AGI
AI startups build deep custom workflows for large enterprises through lengthy pilot periods that can last up to a year. Although these long pilots involve heavy custom software development, they result in lucrative seven-figure contracts. Once integrated, enterprises face immense operational friction and won't easily switch to another vendor.
▶ 21:04
The 7 Most Powerful Moats For AI Startups
Instructing agents to follow a test-driven development style yields much better results than writing code before tests. This approach sets a clear goal for the agent to strive toward rather than letting it fit tests to pass already-written code.
▶ 6:29
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Enterprises recognize that artificial intelligence is a critical technology they need to own rather than just buy. Since tuning and hosting underlying models requires a massive GPU budget, they choose to use model APIs instead. Consequently, they focus on owning the harnessing layer to maintain control over their brand intelligence and custom workflows.
▶ 42:48
How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
Engineering leaders are recommitting to clean, well-defined interfaces and APIs between services. Clear boundaries make it easier for agentic models to navigate the codebase, which simultaneously benefits human developers.
▶ 36:34
Measuring the impact of AI on software engineering – with Laura Tacho
Minion is Uber's formal background agent platform built on top of state-of-the-art agents, CLIs, and SDKs to run agents fully autonomously on internal infrastructure. It runs inside Uber's CI platform with access to monorepos and network infrastructure, and it integrates into workflows via a web interface, Slack, GitHub pull requests, and the AIFX CLI. Furthermore, it provides good defaults per monorepo to increase task success rates.
▶ 14:26
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
Among companies advanced enough to instrument telemetry, eighty percent of developers use agentic workflows at least once a week. Over fifty percent of developers in these leading teams use agentic workflows every single day to complete their work.
▶ 15:38
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
Live retrieval loops force agents to perform random walks through massive enterprise data corpora, leading to high token costs and runtimes of 10 to 15 minutes. Furthermore, this ad-hoc search often results in poor accuracy because the agent lacks proper upfront context.
▶ 37:52
Data + AI Summit Keynote 2026 | Day 1
Microsoft continues to use the OpenAI GPT family to the maximum across their core products while strategically allocating internal MAI flops for research and cost-optimized specialized models. This dual approach allows them to build a world-class superintelligence team without duplicating foundational training waste.
▶ 37:41
Satya Nadella – How Microsoft thinks about AGI
Per-seat pricing acts as an Achilles heel because successful AI agents automate tasks and reduce the number of employees a company needs. Consequently, the more effective the AI product is, the more it directly cannibalizes the incumbent's revenue. Startups avoid this by pricing around work delivered or tasks completed instead.
▶ 25:57
The 7 Most Powerful Moats For AI Startups
Teams should shift as much verification as possible down to deterministic analysis like linters, compilers, and unit tests. Agents can automatically review architectural standards, leaving human reviewers to focus only on high-level functionality and product intent.
▶ 6:57
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
The bottleneck has shifted from producing content to validating and auditing it, often resulting in developers receiving unread walls of text. To combat this, organizations must educate teams to summarize their points and refrain from passing on unread AI slop. Ensuring developers actually read and verify content before sharing helps preserve team focus and prevent cognitive overload.
▶ 53:39
How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
Companies should combine system workflow metrics with self-reported developer sentiment surveys to track utilization, impact, and cost. Using a developer experience index helps reveal broader organizational gains like an 11% boost in developer experience.
▶ 43:02
Measuring the impact of AI on software engineering – with Laura Tacho
Code Inbox is a unified inbox designed to remove noise by displaying only actionable pull requests directly relevant to a user when they need attention. It uses smart assignments based on code ownership, compliance, time zones, and calendar availability to find the right reviewer and tracks strict SLOs for escalations. It also integrates with Slack to batch notifications and respect developer focus time.
▶ 19:28
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
Companies use agentic workflows to quickly turn design and project artifacts into functional patient portals and prototypes. This approach yields high-quality code with excellent documentation and tests much faster than traditional manual methods.
▶ 17:28
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
Genie Ontology automatically connects to structured and unstructured sources like the lakehouse, Google Drive, and SharePoint in the background to build a comprehensive knowledge graph. It uses an algorithm called OntoRank, inspired by PageRank, to index snippets and incorporate human-curated semantics.
▶ 39:10
Data + AI Summit Keynote 2026 | Day 1
Microsoft builds an open, flexible hyperscale infrastructure fleet capable of supporting multiple model lineages and architectures. This flexibility prevents their entire network topology from becoming obsolete due to sudden industry breakthroughs.
▶ 42:34
Satya Nadella – How Microsoft thinks about AGI
AI network effects emerge through data accumulation where more user interactions train better custom models and improve the product. For instance, capturing enterprise private data or user telemetry helps teams iterate on evals and context engineering. This creates a compounding flywheel that hardens the startup's defensive moat.
▶ 38:27
The 7 Most Powerful Moats For AI Startups
Spending up to a week writing a comprehensive implementation plan allows developers to make and flush out decisions before sending it to the agent. This approach reintroduces the intellectual craft and joy of building back into the development process.
▶ 7:50
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Turn-taking must become entirely end-to-end and collapse into the core pipeline to eliminate the single most annoying engineering bottleneck. Getting turn-taking right remains exceptionally difficult in voice architecture. Overcoming this will allow voice agents to properly separate into specialized consumer, enterprise, and productivity tiers.
▶ 1:02:58
How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
AI makes it trivially easy to write large changes all at once, leading to bigger batch sizes and increased risk. Research predicts that a 25% increase in AI adoption could result in a 7.2% reduction in delivery stability.
▶ 53:50
Measuring the impact of AI on software engineering – with Laura Tacho
U review uses a multi-layer pipeline featuring general defect bots, best practice plugins, and APIs for external tools to preprocess code comments. It passes these through a review grader to filter out low-value nits and focuses exclusively on high-confidence changes. Finally, it checks for duplicates across different systems and categorizes the comments before presenting them to the developer.
▶ 22:06
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
Technology cannot automatically fix underlying human and systems-level organizational problems. Without addressing these foundational constraints, companies will simply amplify their existing dysfunctions faster.
▶ 20:50
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
Genie 1 connects to multiple enterprise data sources, including email, calendar, Salesforce, and Databricks, to deliver comprehensive customer profiles. In benchmarks on novel enterprise questions, coding agents only succeeded half the time and hit time limits, whereas Genie 1 provided grounded and accurate results.
▶ 1:02:45
Data + AI Summit Keynote 2026 | Day 1
The pause was a course correction to ensure fleet fungibility and balance training capacity with global serving needs. It allowed them to adapt to rapidly changing hardware generations, cooling requirements, and power densities rather than getting stuck with a single generation.
▶ 49:57
Satya Nadella – How Microsoft thinks about AGI
A strong plan starts with a clear executive summary to prevent agent drift and keep the end goal in sight. It must also be broken down into small, independently verifiable phases that correspond to manageable pull requests.
▶ 8:41
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Highly regulated industries like financial, insurance, and pharma are experiencing the best results. Their success stems from being deliberate and structured in their rollouts, managing acceptable use policies and security intentionally.
▶ 59:16
Measuring the impact of AI on software engineering – with Laura Tacho
Autocover is a custom agent built on internal LangX SDKs and LangChain designed to automatically generate unit tests. It currently generates and merges about 5,000 tests per month across Uber with a 3x quality rate compared to generic agents. To prevent false confidence, it incorporates a critic engine and operates as an independent test validator for both human and AI-generated tests.
▶ 25:02
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
Winning organizations set concrete goals and track their progress against specific metrics rather than just handing out developer licenses. Frameworks like the AI measurement framework help track speed, quality, developer experience, and cost relative to business value.
▶ 22:23
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
Genie Ontology extracts expressions, relationships, and expertise from underlying data assets, running them through an onto-rank algorithm to inject authoritative context. This approach yields a consistent 30 percentage point improvement in accuracy while cutting runtime in half compared to leading agents.
▶ 1:07:58
Data + AI Summit Keynote 2026 | Day 1
Microsoft develops custom silicon to manage overall fleet total cost of ownership in balance with existing hardware. They maintain a close loop between their internal MAI models and custom microarchitecture to generate true demand and keep pace with their own models.
▶ 1:04:02
Satya Nadella – How Microsoft thinks about AGI
Instead of forcing skeptics to use AI, organizations should bring them in and take their critical feedback seriously. Their skepticism highlights real tool failures and validation gaps, making their feedback the ideal roadmap for making AI safe.
▶ 11:53
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Shephard is a campaign management web UI that handles large-scale changes by tracking all pull requests associated with a migration defined simply via YAML files. It automates the generation of PRs using deterministic transformers like Open Rewrite or Minion agents, keeps them fresh on a defined cadence, and integrates with Code Inbox to place them in the correct reviewer queues.
▶ 27:42
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
Solid testing, quality practices, and good documentation are critical prerequisites for running agentic workflows effectively. Investing in developer experience fixes friction points like slow CI and environment toil that AI tools alone cannot overcome.
▶ 24:51
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
PepsiCo deployed Genie within its supply chain AI brain platform called Spend Wise to handle direct and indirect procurement insights. Within the first few weeks of launch, users drove roughly 30,000 queries, shifting away from traditional dashboards toward conversational data engagement.
▶ 1:28:54
Data + AI Summit Keynote 2026 | Day 1
OpenAI's stateless API business is Azure-exclusive, requiring customers to use Azure for these calls. However, their SaaS products like ChatGPT can run on any infrastructure and partner with anyone.
▶ 1:06:50
Satya Nadella – How Microsoft thinks about AGI
Because human attention is a scarce resource in an AI-heavy workplace, teams must clearly distinguish between human-written and AI-generated content. Marking your intent allows readers to know how much scrutiny and attention a message requires.
▶ 12:58
How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Uber experienced relatively slow initial adoption of AI tools because developers were unaccustomed to stepping away from traditional IDE workflows and taking risks with new methods. While top-down executive directives had some impact on metrics, sharing peer wins proved far more successful. Key engineering promoters shared examples of successful techniques, which drove a massive organic increase in adoption because engineers trust their peers over directors.
▶ 32:58
Uber: Leading engineering through an agentic shift - The Pragmatic Summit
Models like the DORA AI capabilities model and the Thoughtworks forest framework provide research-backed readiness benchmarks. These frameworks help teams audit their practices and convince leadership to implement clear AI strategies.
▶ 27:20
Data vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit
LakeFlow replaces legacy Spark with open declarative pipelines that unify batch and streaming workloads using both SQL and Python. Because the frameworks are open and declarative, developers can run them anywhere, while enabling AI agents to easily write the code.
▶ 1:34:38
Data + AI Summit Keynote 2026 | Day 1
Microsoft respects local sovereignty concerns by building sovereign clouds and regional high-density infrastructure with strong data residency and privacy controls. They implement features like confidential computing to build trust in their tech stack worldwide.
▶ 1:19:42
Satya Nadella – How Microsoft thinks about AGI
Zerobus Ingest is a fully managed service that is 100% wire compatible with Kafka, allowing existing producers to land data directly into the lakehouse in open formats. It eliminates small file issues and processes millions of small files at high speeds without requiring complex cluster management.
▶ 1:37:16
Data + AI Summit Keynote 2026 | Day 1
Traditional lake houses hit a latency wall around one second for stringent workloads, forcing companies to maintain separate serving stacks. Raiden flips traditional database engineering by training a machine learning model on exabytes of execution traces to dynamically dispatch the right algorithms at runtime. This enables Lakehouse RT to deliver millisecond performance and massive concurrency directly on governed lake data.
▶ 1:54:49
Data + AI Summit Keynote 2026 | Day 1
Lakebase decouples storage and compute by moving data storage into the lake while introducing specialized services to maintain performance. Safekeepers handle writes using the Paxos consensus protocol for low-latency transactions, while page servers deliver low-latency reads. This architecture enables fully managed serverless Postgres instances with fast provisioning and scaling.
▶ 2:11:53
Data + AI Summit Keynote 2026 | Day 1
Lakebase offers rapid provisioning in under 500 milliseconds and supports instant branching to create isolated dev, test, and staging environments. Developers and agents can also use snapshot restore via a simple click to test code changes or schema modifications. If an agent's work is unsatisfactory, the database can be instantly rolled back to the previous snapshot.
▶ 2:13:11
Data + AI Summit Keynote 2026 | Day 1
Lakebase provides fully managed cross-cloud disaster recovery to ensure continuous business operations during major cloud outages. Users can configure replication across different cloud providers, such as AWS and Azure. In the event of an outage, the system can instantly fail over to the secondary cloud.
▶ 2:17:32
Data + AI Summit Keynote 2026 | Day 1
Mastercard built a shared foundation using Lakebase to support thousands of issuing banks on a single platform. This architecture ensures strict data isolation and complies with data residency requirements to maintain client trust. Simultaneously, it allows insights to be consolidated securely so agents can reason across data without leaking sensitive multi-tenant details.
▶ 2:19:37
Data + AI Summit Keynote 2026 | Day 1
Mastercard leverages performance pools within Lakebase to maintain strict data separation for thousands of issuing banks while enabling continuous insights. This architecture allows them to scale horizontally while ensuring secure, anonymized data consolidation.
▶ 2:21:39
Data + AI Summit Keynote 2026 | Day 1
Traditional change data capture pipelines read database binary logs to replicate changes, making them fragile and prone to data corruption or maintenance overhead. They often require external infrastructure and can fail under heavy production workloads.
▶ 2:43:32
Data + AI Summit Keynote 2026 | Day 1
LTAP stands for lake transactional analytical processing, which unifies storage by transcoding row-oriented operational data into columnar formats directly on storage services. Unlike traditional failing HTAP systems, it avoids query engine compromises while providing real-time analytics without hurting OLTP performance.
▶ 2:47:55
Data + AI Summit Keynote 2026 | Day 1
Storage services use their underutilized CPUs to transcode row data into columnar Delta and Iceberg formats during local disk writes. Because columnar formats offer high compression ratios, this conversion minimizes data volume and leverages I/O bound resources efficiently.
▶ 2:46:02
Data + AI Summit Keynote 2026 | Day 1
Developers do not need to set up custom pipelines or change their operational workflows to use LTAP. Once enabled, Lakebase tables automatically appear in Unity Catalog in Delta and Iceberg formats for real-time querying.
▶ 2:49:25
Data + AI Summit Keynote 2026 | Day 1
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