An account manager preparing for a renewal wants to know whether more people at a customer company are using AI. Suppose 8 people used it in one workspace and 6 used it in another, with two people in both. Adding the workspace counts gives 14. The account has 12 distinct users. Either number can come from SQL without running into an error.

To answer these types of questions, Notion has built an internal agent called Data Scout. Employees ask in plain language, the agent queries governed data in Snowflake and uses company knowledge in Notion to interpret the answer. Within two months, weekly requests to its data team fell by about 80%. Roughly half of its employees now use Data Scout.

Now before we dive into it, just wanted to say referring 1 person to Data Tinkerer gives you access to a library of data resources:

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Now, with that out of the way, let’s get into how Notion created its own AI analyst.

TL;DR

Situation

Routine questions could spend days in the data team’s Slack queue.

Task

Let employees investigate approved data themselves while preserving their Snowflake permissions and making the agent’s choice of metric, source and interpretation reviewable.

Action

Notion connected Data Scout to Snowflake under each employee’s identity, indexed approved guidance and added query tracing and nightly review.

Result

Notion reports about 80% fewer weekly requests to the data team after two months, questions answered in minutes rather than waiting days and used by about half of employees.

Use Cases

Data quality validation, internal support bot, automated BI reporting

Tech Stack/Framework

Snowflake, SQL, Slack, GitHub

Explained further

One question, several kinds of evidence

Notion sampled 200 real questions and found uses across Sales, Engineering, Customer Experience, Product and Marketing. In a GTM example, an account manager preparing for a renewal asks whether AI adoption is growing in a customer’s account.

Snowflake can provide usage history. The account notes and past meetings in Notion can explain the customer’s rollout plan. If usage rises, the account manager may ask whether more people started using AI or whether existing users became more active. A subsequent question might split the trend by team or workspace. The agent has to carry the account scope and the chosen definition through those follow-ups.

Data Scout Reporter uses saved report definitions to produce scheduled reports in Notion and Slack. Teams have also built specialised agents that connect to Data Scout over its shared MCP connection, reusing its data access and context while adding domain-specific guidance. Scheduled reporting requires its own approved access model.

The permission boundary follows the person

There are two permission systems here. Notion controls who can access the agent, its pages and its available tools. Snowflake controls what the authenticated person can query. Each employee signs in to the Snowflake MCP connection using their own Snowflake credentials, so the query executes under that identity and the relevant role and grants. Masking and row-level policies apply where configured.

If a query is denied under the person’s Snowflake identity, the agent should report the access limitation. A person may also be allowed to see a result that should not be pasted into a broad Slack channel or a widely shared report.

Find the definition before the table

An LLM with a warehouse connection can browse schemas and produce executable SQL without establishing whether workspace memberships are an acceptable proxy for people, which account mapping is authoritative, or whether a daily snapshot should be summed. Notion indexes Skills, Metric Definitions, Table Schemas, FAQs and known data changes in a Notion database for Data Scout to consult.

The row properties act as retrieval metadata: title, domain, type, description or keywords, and priority. The page body contains the procedure, calculation, caveats and links. A Skill can say how to investigate account adoption before a renewal; a Metric Definition specifies the numerator, denominator and exclusions; a Table Schema gives grain, relationships and join cautions. When a definition is maintained in another system, the page links to it instead of creating a second version.

The agent begins with one or two likely domains and relevant Skills. An exact high-priority entry can answer a point lookup. Otherwise it opens the selected pages, follows their references and expands its search only if guidance is missing. It does not load the entire library into every conversation or choose a table before deciding what the question means.

Notion tried putting all of the guidance into database properties for quicker retrieval. The metadata became overloaded and harder for people to maintain, so properties now locate a page; they do not substitute for reading its body. Priority is a retrieval hint, not proof that an entry is approved or current. Ownership, review status and freshness still need to be managed.

Data context determines the calculation: source, metric, grain, required filters and freshness. Business context helps explain a result through account plans, rollout notes, incidents or decisions. Routing guidance directs the agent to the appropriate connection and sources, potentially across more than one platform. Notion’s instructions keep business context from redefining the approved metric calculation.

The page library does not enforce metric definitions at query time or replace a semantic layer. Notion acknowledges that limitation and says teams can keep definitions in existing tooling, including Snowflake Semantic Views or Cortex Analyst while linking them to business context in Notion.

What happens between retrieval and an answer

Notion’s agent instructions require it to resolve the metric, account or population, period and comparison before calculating. Then it routes to the approved connection and source and checks the query logic and returned data for join cardinality, filters, denominators, incomplete periods, tracking changes and backfills.

For the renewal question, these checks catch different mistakes:

• If one person belongs to several workspaces, joining account and workspace data may multiply that person’s activity. A syntactically valid COUNT is still wrong for a people-based adoption metric.

• If the numerator and denominator use different populations or exclusion rules, the rate is meaningless even if both counts are individually correct.

• If a daily snapshot has one row per entity per day, summing several days double-counts a current-state population. A single as-of date answers a current-state question; a date range belongs in an explicit trend.

• If the latest period is incomplete or an event was renamed, the apparent change may reflect data collection rather than customer behaviour.

The discovery instructions also cover failure handling. When a necessary column is undocumented, inspect the schema. When Snowflake returns an invalid identifier, verify the actual schema rather than guessing another plausible column. When the loaded guidance already documents the fields, avoid an unnecessary wide schema retrieval. If a definition is missing, the query is denied, or a check fails, the agent should say what it cannot establish.

Only after checking the measurement does Data Scout use account notes to discuss whether the change fits the customer’s planned rollout. It should distinguish “usage increased under this definition” from “the rollout caused the increase”. The latter is an interpretation and the account team still owns the renewal decision.

An answer needs a trace

Suppose two people ask about the same account and receive different adoption figures. Their Snowflake roles might legitimately expose different data. Or the agent may have selected different account mappings, time windows or definitions. A transcript containing only the polished answer does not make that easy to diagnose.

Notion built an internal execution tool called run_tagged_query. It accepts SQL and metadata together, appends the metadata as a structured JSON comment in the SQL and calls the upstream Snowflake query tool. The metadata can identify a canonical metric, definition reference, context pages, filters, time window and question ID. The wrapper also emits execution telemetry. A question ID can tie several investigative queries to one question; an execution ID identifies a particular run when returned.

A JSON comment in the SQL text does not set Snowflake’s native QUERY_TAG session parameter. The comment can be a searchable marker if the execution tool preserves it. To populate QUERY_TAG, the execution layer must set the parameter on the session that runs the SQL. Separate tool calls need not share a session, and pooled sessions must clear or replace a previous tag. Otherwise query-history attribution can be wrong. Notion’s guide also notes that Snowflake’s AGENT_TYPE, when the connection is recognised as agentic, gives coarse classification rather than question-level lineage.

This wrapper is Notion’s internal tool, not something automatically installed with a Snowflake MCP connection. Merely mentioning its name in an agent prompt will not tag direct Snowflake calls.

If a trace shows the correct metric definition but the wrong table, the routing guidance needs attention. Duplicated people point to the procedure or join guidance. An unsupported explanation of a rollout points to interpretation. Notion says its wrapper is lenient by default: incomplete metadata does not block an otherwise valid query. A successful call can therefore still leave gaps in the trace.

Who updates the agent when the warehouse changes?

Schemas, event names, backfills and business plans move. Notion runs a nightly review agent across Data Scout conversations and queries, recently edited context pages, relevant Slack and Notion material, and GitHub changes. It records missing, stale or conflicting guidance with supporting evidence in a review database. A separate triage agent deduplicates findings, applies small, well-supported and authorised edits, and sends ambiguous or higher-risk ones to a person. A weekly Slack summary surfaces patterns and unresolved findings.

A correction in Slack gives the review process evidence to investigate; changing a metric still requires approval. A merged PR does not prove the change is deployed or historical data has been backfilled. Before editing, the triage process checks current guidance and preserves newer human changes; afterwards it verifies the saved update and rechecks affected questions. Metric meaning, source selection, grain, filters, access and sensitive-data handling remain owner-review decisions.

The maintenance agents review approved sources and update Notion pages. Scheduled runs do not inherit an employee’s Snowflake access. If query logs feed the review, they need a separately approved source.

The full scoop

To learn more about this, check Notion's Engineering Blog post on this topic

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Without further ado, let’s get to the round up for October!

Data science & AI

• AI Skills Data Scientists Need in 2027 (From 7K+ Job Postings) (12 minute read)

  analyses 7,756 unique job postings to separate widely requested AI skills from changing tool demand, seniority effects and production expectations.

• What’s a Decision Model? (8 minute read)

  contrasts decision models with generative LLMs and conventional classifiers, including the speed, cost, calibration and interpretability trade-offs.

  

• How to Prepare Your Codebase for AI Coding Agents (12 minute read)

  turns agent-ready coding advice into a concrete setup covering repository instructions, scoped context, planning documents and validation steps.

• Do Agent Teams Pay Off? A Case Study on Vibe Code Bench (18 minute read)

  Katrina Drozdov tests single coding agents against multi-agent teams and finds that the extra cost rarely produces a statistically significant gain.

  

• Artificial Analysis v5 and the Benchmarker’s Dilemma (11 minute read)

  Nathan Lambert explains how a single model ranking hides choices about capability, aggregation and transparency that can steer both markets and model development.

• Can AI Automate AI R&D Yet? (25 minute read)

  tests whether frontier models can independently produce a genuine machine-learning innovation and finds that even after thousands of GPU hours, their best result captured only 15% of the human-developed method’s gains while overstating what they had achieved.

  

• Knowing How Is Not Knowing What (14 minute read)

  Yunze Xiao argues that realistic agent environments need organisational context, linked records and consistent permissions across graphical, command-line and API surfaces.

• AI Benchmarks: Climbing the Wrong Hills (24 minute read)

  Michael Kim and Claire Schultz trace how contaminated or poorly targeted benchmarks can turn measurement errors into training objectives.

  

Data engineering

• Change Data Capture (CDC) for Data Engineers (18 minute read)

  works from polling and triggers to log-based CDC, then covers offsets, bootstrapping, schema changes and the small-file trade-offs of streaming into a lakehouse.

  

• DuckDB vs Polars vs Spark on Databricks Serverless With Joins and Window Functions (14 minute read)

  Daniel Beach runs the same join and window workload across DuckDB, Polars and Spark on Databricks Serverless, making the practical trade-offs visible across 285 million rows.

• Are Data Teams Solving the Right Problems? (9 minute read)

  asks whether dashboards and pipelines changed a business outcome then reframes data work around causes, ownership and the teams able to act.

  

• The Pendulums of Data (8 minute read)

  Good article by explaining why every dominant data idea eventually creates the problems that bring its opposite back into fashion and why today’s hot architecture is likely tomorrow’s legacy system.

• AI Changes the Data Interface, Not the Query Engine (16 minute read)

  Peihao Yuan separates deterministic computation from probabilistic interpretation, then shows when DuckDB beside an agent is enough and when a shared lakehouse becomes necessary.

• dbt + DuckDB in 2026: You Don’t Need a Warehouse for That (6 minute read)

  Egor Burlakov describes an ephemeral dbt and DuckDB transformation layer that writes stable metrics to Postgres, reserving a warehouse for needs that actually appear.

  

• From 35 Seconds to Half a Second: 70x Faster Dashboards in Plain Postgres (5 minute read)

  Devon Theriault cuts repeated dashboard scans with daily rollups while preserving late-arriving data, recent events and the limits of additive metrics.

  

• The Self-Organizing Data Stack (16 minute read)

  sketches an orchestration engine that maintains physical data models while people retain authority over definitions, contracts and exceptions.

  

Data analysis and visualisation

• What to Do When Someone Sends You an AI-Slop Dashboard (6 minute read)

  provides a practical framework for stripping away an AI-generated dashboard’s polished surface, testing whether its data and charts support a real decision and rebuilding it around one clear user need.

• How Much Data Is There in Your Chart? (15 minute read)

  Adithya Manavalan, Michael Lai and Mike Morrison make the data-ink ratio interactive, including the point where removing more ink begins to remove meaning.

• Validating Data Analyses Written by Coding Agents (6 minute read)

  Chris Brown proposes specifications, adversarial review, simulated truth and independent implementations for checking analyses when reading every generated line no longer scales.

• The Analyst’s New Competitive Advantage (12 minute read)

  and argue that cheaper analysis makes reproducibility, trusted definitions and organisational consensus more valuable than producing another dashboard.

Other interesting reads

• Here Be Monsters: What Do You Still Need to Know When Agents Do the Work? (10 minute read)

  Adrian Quiroga argues that agent-heavy work shifts value from producing every line to recognising failure modes, mapping systems and taking responsibility for unfamiliar output.

  

• We Studied 1,584 Data Centers — and Found Modest Local Job Growth (6 minute read)

  Simon Buechler and Lyndsey Rolheiser compare counties before and after data centres arrive, finding delayed job gains concentrated outside retail and hospitality.

• Data Leader Therapy: Worries (11 minute read)

  distils interviews with 25 data leaders into five recurring anxieties, from weak internal credibility and unreliable foundations to unclear AI mandates.

  

• What Has to Go Right to Justify the Trillions Being Spent on AI CapEx? (20 minute read)

  estimates the revenue needed to sustain the AI build-out, then tests that threshold against supply constraints, adoption and the economics of frontier models.

  

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There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for September!

Data science & AI

• Policy Gradient for LLMs, Explained Visually (21 minute read)
  Tyler Romero builds policy gradients from token probabilities and rewards, using visual examples to explain REINFORCE and variance reduction.

• LLM Classification Is Feature Engineering (15 minute read)
  Taylor Pospisil demonstrates how treating LLM outputs as features restores calibration, threshold control and the ability to combine text with structured data.

• Learning to Solve Hard Problems in RL for LLMs by Never Giving Up (14 minute read)
  Michael Noukhovitch shows how aggregate reinforcement-learning scores can hide stalled progress on hard problems and explores reallocating training effort towards them.

• Language Models for Text Classification: From Bag-of-Words to Jev (40 minute read)
  traces text classification from bag-of-words to transformers and decision models, explaining architectural choices and why probability calibration matters.

• Pretraining progress is mostly coming from data (15 minute read)
  and compare training recipes and datasets to examine how much recent pretraining progress comes from better data.

  

• Issue #70 – The Debt AI Creates (16 minute read)
  Interesting article by examining how inherited context, unreviewed outputs and unreliable agent memory create AI debt and what can be done about it.

• Swarm Scaling (10 minute read)
  Toby Ord analyses published agent-swarm results to separate speed gains from compute efficiency and examine diminishing returns as swarms grow.

• I Asked 100 Agents to Hack Me (9 minute read)
  reports a personal red-team experiment with autonomous agents, highlighting account-security weaknesses and the limits of stopping activity by shutting down inference.

Data engineering

• Agents in Action #8: What Makes an AI Agent Production-Ready? (21 minute read)
  sets out the identity, permissions, lifecycle controls and observability needed to move an agent from a local demonstration into production.

  

• The unbearable lightness of one more index (16 minute read)
  Radim Marek measures the combined write, storage and maintenance costs of otherwise reasonable AI-generated PostgreSQL indexes.

• Ten years of Postgres logical replication (30 minute read)
  Dimitri Fontaine builds a reproducible hub-and-workers architecture to explain PostgreSQL logical replication, including routing, identifiers, conflicts and operational limits.

  

• Training a 4B model to produce 81% faster query plans than Postgres (70 minute read)
  Rohan Bansal documents training a small model to propose faster PostgreSQL query plans, including the harness, timing controls and reward-design lessons.

  

• Thinking Like a Data Architect (10 minute read)
  explains how explicit ownership, publication contracts, metric definitions and recovery promises reduce repeated work for data consumers.

  

• One round trip beats a thousand, and your batch API probably is not batching (6 minute read)
  Ankit Rana explains how driver settings, transaction boundaries and bulk-loading methods determine whether batching actually reduces database round trips.

• Streaming Events to DuckLake (7 minute read)
  builds a local Kafka and Flink pipeline that transforms events and writes them into DuckLake, with supporting code for reproduction.

• Do You Actually Need Real-Time Data? (9 minute read)
  connects data freshness to the timing of business decisions, helping teams judge when real-time infrastructure is worth its operational cost.

  

• This Year’s Better Mousetrap: New Tech, Same Old Problems, and The Semantic Swamp (4 minute read)
  argues that new data tools cannot resolve disagreements about meaning, ownership and business decisions without organisational work.

• Back to the Basics. Threads. (13 minute read)
  revisits concurrency through a Rust data-processing example, showing why understanding execution and shared work still matters when agents write code.

Data analysis and visualisation

• Sample size needed for central limit theorem to kick in (10 minute read)
  Peter Ellis uses simulations to show why heavily skewed distributions can require far larger samples than familiar central-limit-theorem rules of thumb suggest.

• Why fitting a logistic is nearly impossible from early data (3 minute read)
  John D. Cook demonstrates why small errors in early observations can produce extreme changes in a logistic curve’s estimated eventual limit.

Other interesting reads

• AI Is Making Us Build Too Much (12 minute read)
  Simon Green examines how cheap AI-generated code, documentation and rules create lasting maintenance costs and argues for judging whether each addition deserves to exist.

• Starting a Career in Data Science in the Age of AI (9 minute read)
  Stephanie Kirmer argues for broad fundamentals, critical judgement and adaptable problem-solving when planning a data-science career around uncertain technological change.

• The €2.4M Slide That Got Rejected in 4 Minutes (5 minute read)
  explains how to preserve concrete operational costs and ownership risks when presenting a data-governance business case to executives.

Quick favor - need your take

Was there any standout article or topic from September I missed? Feel free to drop a comment or hit reply, even a quick line helps.

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for July!

Data science & AI

• Kimi K3 Architecture Notes (4 minute read)
  dissects Kimi K3’s sparse architecture, attention residuals, NoPE design and inference-efficiency trade-offs

  

• The Cheapest Way to Make Open Weight AI Models Better (27 minute read)
  explains why targeted post-training can improve open-weight models more economically than scaling pre-training alone.

• A Taxonomy of Self-evolving Agents (14 minute read)
  Shilong Liu proposes a practical taxonomy for agents that adapt their memory, tools, prompts or policies through experience.

  

• The LLM Critics Are Right. I Use LLMs Anyway. (19 minute read)
  accepts the strongest criticisms of language models while explaining the disciplined uses that still make them valuable.

• Closing the Verification Loop (12 minute read)
  Kieran Klaassen argues that agents become dependable only when verification is built into the loop rather than left to a final human check.

  

• How an AI Token Travels Through a Data Center (26 minute read)
  traces one token through gateways, schedulers, KV caches, accelerators and networks to explain the real mechanics and economics of inference.

• How to Implement a Unified Memory From Scratch (16 minute read)
  builds a unified memory layer from scratch and explains how agents can retain useful state without turning context into clutter.

  

Data engineering

• I Spent 10 Hours Learning Multithreading and Multiprocessing (14 minute read)
  compares multithreading and multiprocessing through hands-on examples so Python practitioners can choose the right concurrency model.

  

• The Data Anarchy Tax: Why your team is firefighting 45% of the time (9 minute read)
  Good article by on why fragmented ownership and unclear operating rules create a measurable data-anarchy tax that keeps teams in reactive work.

• The Most Expensive Mistake in System Design (12 minute read)
  explains why designing for imagined scale too early can make data systems harder and more expensive to operate than the problem requires.

  

• The Startup’s Postgres Survival Guide (11 minute read)
  Alexander Belanger lays out pragmatic Postgres choices for startups that need reliability without prematurely adopting a more complex data platform.

• Your Data Warehouse Isn’t Integrated Just Because the Tables Are in One Place (12 minute read)
  argues that co-locating tables is not integration when semantics, contracts and ownership remain fragmented.

  

• So, is data modeling dead? (16 minute read)
  Daniel Beach examines what AI-assisted development changes about data modelling and why deliberate structures still matter.

• Backfilling: The Most Underrated Feature of a Data Pipeline (10 minute read)
  Lucie Thimus explains why safe, repeatable backfills are a core capability for correcting history and evolving production pipelines.

• FastAPI for Model Serving: The Standard You Should Actually Use (12 minute read)
  shows how to structure a FastAPI service for model inference with production concerns such as validation and deployment in mind.

Data analysis and visualisation

• The Model Is Smart. Your Company Is the Problem (10 minute read)
  argues that failed AI analysis is often rooted in unclear metrics and organisational fragmentation rather than model capability.

• Going Bayesian Automates Your Manual Data Analysis (9 minute read)
  Eric J. Ma shows how robust likelihoods and hierarchical Bayesian models can replace manual outlier handling with a reproducible analytical process.

Other interesting reads

• How Tech Workers Are Feeling in 2026: A Workforce Splitting in Two (22 minute read)
  Interesting analysis by and using a large sentiment survey to show how AI is dividing tech workers between amplification and destabilisation while burnout rises.

  

• You Don’t Have to Be Smart If You Can Think Clearly (4 minute read)
  argues that methodical, clear thinking under uncertainty is more dependable than flashes of intuition when engineers face genuinely hard problems.

Quick favor - need your take

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for June!

Data science & AI

• Stop Monitoring AI Systems Like Web Services (11 minute read)
  explains why production AI needs monitoring built around quality, behaviour, cost, feedback and changing data rather than ordinary web-service health metrics.

  

• When Is It Actually a RAG Problem? (10 minute read)
  provides a decision framework for separating genuine retrieval problems from issues better solved with tools, semantic layers or improved context.

• Using Local Coding Agents (34 minute read)
  tests open-weight models in local coding-agent setups and examines how close they come to replacing hosted coding subscriptions.

• Stateful Swarms make AI 100x More Intelligent per Dollar (24 minute read)
  examines how persistent memory changes multi-agent performance, coordination and cost compared with stateless agent architectures.

  

• When Trees Still Beat Tabular Foundation Models (4 minute read)
  examines the dataset characteristics that still favor gradient-boosted trees over increasingly capable tabular foundation models.

• Running Local Models Is Good Now (8 minute read)
  draws on hands-on experiments to explain why local models have become practical for coding, research and personalised development assistance.

• Scaling Laws, Carefully (25 minute read)
  Lilian Weng provides a careful guide to neural scaling laws, covering their mathematical foundations, practical value and important limitations.

Data engineering

• Vibe Coding Is Dangerous, Agentic Engineering Isn’t (15 minute read)
  argues that coding agents become useful when humans remain responsible for specifications, architecture, tests and review.

• How to Build a Simple, Bulletproof Data Pipeline (9 minute read)
  demonstrates how a deliberately simple batch pipeline can deliver robustness and maintainability without unnecessary real-time infrastructure.

  

• Five dbt Mistakes I See in Every Startup (11 minute read)
  examines five recurring dbt mistakes involving CI, contracts, incremental models, sources and environment separation.

• Broker-Visible vs Client-Local Parallelism (6 minute read)
  compares two approaches to messaging parallelism and explains how each affects coordination, throughput and failure handling.

  

• Basic Spark Concepts (5 minute read)
  explains the foundational Spark concepts practitioners need before tuning jobs or adopting more complicated distributed-processing patterns.

• What Is Apache Arrow Flight? (13 minute read)
  Daniel Beach builds an Arrow Flight server and client to explain how Arrow and gRPC move columnar data without traditional serialisation overhead.

• In 2026, the Data Fundamentals Matter More Than Ever (12 minute read)
  argues that modelling, SQL and dependable data foundations remain the real constraints on AI adoption despite the focus on agents and new tools.

• How AI Changes Four Core Data Roles (7 minute read)
  examines how AI is changing the responsibilities and required skills of analysts, analytics engineers, data engineers and data scientists.

Data analysis and visualisation

• On Data Quality (12 minute read)
  Great article by arguing that data quality is conditional on use and should be evaluated according to the decisions and outcomes the data must support.

  

• Why General AI Models Still Cannot Be Trusted for Data Analysis (9 minute read)
  Interesting review by examining where general AI models continue to fail at analytical reasoning, validation and reliable interpretation of data.

• The Tableau Exodus Has Begun (7 minute read)
  Interesting observation by arguing that organisations leaving Tableau should use the migration to rethink metrics, workflows and the value of BI.

Other interesting reads

• Consulting in an AI-World is Fine. Big Consulting Isn’t (8 minute read)
  Interesting take by arguing that AI threatens the economics of large consulting firms more than the value of independent specialist expertise.

• What I’d Do As a Junior Candidate in Mid-2026 (6 minute read)
  practical advice by for junior candidates trying to develop credible skills and stand out in a difficult data job market.

Quick favor - need your take

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It’s time for another round-up on all things data and AI!

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There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for May!

Read more

Today we will look at how Grab used AI agents to cut repetitive data support from hours to minutes.

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There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Now, with that out of the way, let’s get into how Grab turned repetitive data support into an AI agent workflow.

Read more

Today we will look at how Lyft cut translation latency from days to minutes using AI

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Now, with that out of the way, let’s get to how Lyft turned localisation into a 30-minute AI workflow!

TL;DR

Read more

It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Without further ado, let’s get to the round up for April!

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Today we will look at how decision making can be improved.

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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With that out of the way, let’s get to the today’s topic of decision making!

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Today we will look at how Airtable cut archive storage costs by 100x and saved millions.

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Now, with that out of the way, let’s get into how Airtable pulled it off.

TL;DR

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Today we will look at how Pinterest used multimodal AI to organise products into relevant shopping collections

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Now, with that out of the way, let’s get to PinLanding: Pinterest’s AI system for shopping collections

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for March!

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Today we will look at how Notion scaled its AI Q&A to millions of users.

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for February!

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Today we will look at how Shopify scales its product categorisation using agentic AI

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Now, with that out of the way, let’s get to Shopify’s multi-agent taxonomy

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Today we will look at how LinkedIn ingests data at scale.

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

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Now, with that out of the way, let’s get to Venice: LinkedIn’s data storage platform

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It’s time for another round-up on all things data and AI!

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Without further ado, let’s get to the round up for January!

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Following on from previous posts talking to people in the field, today I will be talking with Ahsaas Bajaj who is a Senior Machine Learning Engineer at Instacart. He works on large-scale recommendation systems that serves millions of customers.

We talked about his rise from software engineering to machine learning at Instacart, how does he decide between rules based vs ML approaches and how he approaches the work now as a more senior stakeholder.

So without further ado, let’s get into it!

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Today we will look at how DoorDash uses anomaly detection to save millions of dollars by flagging fraud trends early.

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Now, with that out of the way, let’s get to DoorDash’s fraud detection!

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