# csv Published articles for csv. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Shared Selective Persistent Memory for Agentic LLM Systems DevFeed: [Shared Selective Persistent Memory for Agentic LLM Systems]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [agent observability](), [Framework](), [Code](), [configuration](), [Access Control](), [Git](), [Model Context Protocol (MCP)](), [CSV](), [SQL](), [Grafana]() Tags: [access-control](), [agentic](), [agents](), [code](), [configuration](), [csv](), [dashboards](), [git](), [llm](), [mcp](), [memory](), [platform](), [replication](), [sql]() ### AI overview This research introduces shared selective persistent memory for agentic LLM systems. The architecture retains reusable task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces. Shared workspaces support role-based collaborative reuse, and experiments report higher task completion than no memory or full-history persistence, along with zero-token data refresh and lower token costs. ### Source excerpt Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is both token-inefficient and counterproductive--irrelevant context degrades generation quality. We introduce shared selective persistent memory, a memory architecture for agentic systems that identifies and retains four categories of reusable context--task specifications, data... ## DuckDB Skills for Claude Code DevFeed: [DuckDB Skills for Claude Code]() Original publisher: [Read original article]() Author: The DuckDB team Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [DuckDB]() Topics: [DuckDB](), [Claude Code](), [Command-line interface](), [data](), [CSV](), [parquet]() Tags: [claude-code](), [cli](), [csv](), [data](), [duckdb](), [parquet](), [using-duckdb]() ### AI overview This post introduces the duckdb-skills plugin for Claude Code. The plugin uses the DuckDB CLI to inspect data files, run SQL queries, convert formats, explore object storage and spatial data, search documentation, and recall earlier sessions. It explains installation, how the skills select appropriate commands, and how Claude uses DuckDB for exact query results. ### Source excerpt The duckdb-skills plugin gives Claude Code a growing number of skills that use the DuckDB CLI to read data files, run queries, convert formats, explore object storage, work with spatial data, search the documentation and recall earlier sessions. ## CodeSOD: An Odd Sort DevFeed: [CodeSOD: An Odd Sort]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-09-15T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [Code](), [PowerShell](), [Script](), [Sorting]() Tags: [code](), [codesod](), [csv](), [excel](), [powershell](), [report](), [script](), [sorting]() ### AI overview The article critiques a PowerShell script that queries Active Directory for users and their last logon times. It explains that the script's alphabet-based approach does not correctly sort names, performs unnecessary searches and property loading, and generates a CSV report for Excel despite these inefficiencies. ### Source excerpt Let's say we wanted to query Active Directory and print out a report of all of our users, and their last logon time. That seems like a pretty normal task for a Powershell script. It'd probably be short and easy to read, at least if it were written by a normal person. Alice sends us one that wasn't. She's already done us a favor, as she writes: "Code cleaned up and indented for the whitespace-missing-impaired." ##################################### # lists accounts and selected attributes alphabetically ##################################### foreach( $letter in "a", "b", "c"......"z") { $strfilter = $letter + "*" $objdomain = New-object System.DirectoryServices.DirectoryEntry $objSearcher = New-object System.DirectoryServices.DirectorySearcher $objSearcher.SearchRoot = $objdomain $objSearcher.Filter = $strFilter $objSearcher.PropertiesToLoad.Add("name"); $colResults = $objSearcher.FindAll() foreach($result in $colResults) { $name = $result.Properties.Name $searcher = New-Object DirectoryServices.DirectorySearcher([adsi]"") $searcher.filter "(&(objectCategory=User)(sAMAccountName=$name))" $users = searcher.FindAll() foreach($user in $users) { Write-Output $user.properties.item("name") + "," + $user.properties.item("lastLogon") } } } This accomplishes sorting alphabetically by iterating across the alphabet. Which, I suspect, isn't going to actually get them in alphabetical order; it makes sure that albert and alice appear before bob, but doesn't enforce that albert must come before alice. In any case, we iterate across the alphabet, and then create a searcher that finds a*, then b*, etc. We explicitly tell the searcher that the only property we care about is the name field, so that we don't load unnecessary fields, like the ones we want to report on. We then iterate across the list of names, construct a new searcher, and search for the account with the username we fetched. That lets us get all of the fields we need, including the ones we aren't going to use. Now, we sea ## New site for my John Snow Cholera Map data DevFeed: [New site for my John Snow Cholera Map data]() Original publisher: [Read original article]() Author: Robin Wilson Published: 2026-09-10T12:13:16Z Content type: release Language: en Sources: [Robin Wilson]() Topics: [data](), [GeoJSON](), [CSV](), [Website]() Tags: [academic](), [blog](), [csv](), [data](), [formats](), [gis](), [map](), [website]() ### AI overview The author announces a new website hosting digitized data from John Snow's cholera map. The site provides downloads in GeoPackage, GeoJSON, Shapefile, and CSV formats, and older blog posts now redirect to it. ### Source excerpt I've finally crossed something off my todo list, after putting it off for years. I've created a new site to host the data from John Snow's cholera map that I digitised back in 2012. Previously the data was only available from posts on this blog (specifically this original post and a follow-up), and it wasn't [...] ## osmo-remsim - osmo-remsim 1.2.0 released DevFeed: [osmo-remsim - osmo-remsim 1.2.0 released]() Original publisher: [Read original article]() Author: lynxis Published: 2026-08-05T16:54:21Z Content type: release Language: en Sources: [Open Source Mobile Communications: News]() Topics: [bug](), [Concurrency](), [log management]() Tags: [bug](), [bug-fixes](), [client](), [crash](), [csv](), [deadlock](), [debug](), [issue](), [logging](), [mutex](), [tracker]() ### AI overview osmo-remsim 1.2.0 is a release containing stability and bug fixes across bankd, remsim-client, remsim-bankd, and related ASN.1 and logging code. The fixes address invalid CSV and TPDU handling, crashes, memory leaks, race conditions, signal handling, slot mapping, byte order, and a logging mutex deadlock. ### Source excerpt 1.2.0 contains stability fixes and bug fixes. [ Alexander Couzens ] * bankd: csv: fix crash on invalid lines * bankd: csv: fail on invalid csv lines * rspro_server: don't access the msg on error cases * rspro_util: asn1 decode: fix memleak when decoding fails * asn1: ber_decoder: disable stack size check on ctx = NULL * asn1: move asn_debug into librspro * Remove unconditional assignment of asn_debug = 0 * rspro_server: use correct byte order in log message "Bankd IP/Port changed to : [ Andreas Eversberg ] * Fix: Remove slot mapping at bankd when client disconnects * Fix: Prevent race conditions when accessing slotmap in bankd * Fix: Prevent bankd from exiting upon SIGPIPE * Fix unhandled (un)mapping events [ Harald Welte ] * remsim-client: Don't attempt to pass on illegal TPDU length * remsim-bankd: Don't pass on illegal TPDUs of illegal length * bankd: Avoid osmocom logging mutex deadlock in signal handling ## Open source Chrome extension recreates LinkedIn from your data exports DevFeed: [Open source Chrome extension recreates LinkedIn from your data exports]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-06-28T17:41:27Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [Extension](), [Chrome extension](), [Open Source](), [App](), [data](), [CSV](), [GitHub](), [Single-page application (SPA)]() Tags: [app](), [chrome](), [chrome-extension](), [code](), [csv](), [extension](), [gdpr](), [github](), [offline](), [open-source]() ### AI overview The article introduces LinkedOut, a free open-source Chrome extension and offline single-page application that lets users inspect and browse data exported from LinkedIn. It describes the author's findings about the breadth of the export and explains how the app processes the data locally without gathering or transmitting it. ### Source excerpt An open source extension to recreate LinkedIn from your data exports ## SIMD-accelerated integer-to-string conversion DevFeed: [SIMD-accelerated integer-to-string conversion]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-05-18T19:39:49Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [C++](), [cpu](), [Compiler](), [Logging]() Tags: [amd](), [avx](), [c-plus-plus](), [compiler](), [csv](), [intel](), [json](), [logging](), [performance]() ### AI overview This article explains SIMD-based techniques for converting 64-bit integers to decimal strings. It describes division and lookup-table approaches, then discusses using AVX-512 and IFMA instructions on recent AMD and Intel processors to process multiple values in parallel. ### Source excerpt Converting a 64-bit integer to its decimal string representation is a mundane task that shows up everywhere: logging, JSON serialization, CSV output, debug prints, etc. In C++, you might use std::to_chars, sprintf, or some library routine. How do these functions work? At a high level, they repeatedly divide by ten. Start with your integer k. ... Continue reading SIMD-accelerated integer-to-string conversion ## Good Data For Bad Golf DevFeed: [Good Data For Bad Golf]() Original publisher: [Read original article]() Author: jgamblin Published: 2026-05-13T14:05:06Z Content type: article Language: en Sources: [Jerry Gamblin]() Topics: [Python](), [CSV](), [GitHub Pages](), [GitHub Actions](), [Self-hosted]() Tags: [analytics-pipeline](), [csv](), [github-actions](), [github-pages](), [pipeline](), [python](), [self-hosted](), [uncategorized]() ### AI overview The article presents jgamblin/golf, a self-hosted Python analytics pipeline that converts Garmin Golf app CSV exports into a multi-page GitHub Pages dashboard. It describes features including session analysis, club-level consistency, session replay, gapping, coaching recommendations, and data-quality tracking, with deployment automated through GitHub Actions. ### Source excerpt I play golf. I am not good at golf. But I have a Garmin Approach R10 launch monitor, a Python interpreter, and too much free time, so naturally I spent way more time building a dashboard to analyze my swing data than I did actually swinging a club. The result is jgamblin/golf, a self-hosted analytics ... Read more ## The 5 Silent Failures in Data Pipelines DevFeed: [The 5 Silent Failures in Data Pipelines]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-04-24T19:06:02Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Data pipelines](), [data](), [CSV]() Tags: [csv](), [dashboard](), [data-pipeline](), [data-pipelines](), [pipelines](), [reports](), [schema]() ### AI overview The article explains how data pipelines can fail silently without triggering errors or obvious warnings, causing stale or incorrect data to reach dashboards and reports. It introduces schema drift as one failure mode, including unexpected changes to CSV or XML files loaded from SFTP. ### Source excerpt How Your Pipelines Lie to You Without Throwing a Single Error ## Agentic Workloads on Airflow: Observable, Retryable, and Auditable by Design DevFeed: [Agentic Workloads on Airflow: Observable, Retryable, and Auditable by Design]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Artificial Intelligence](), [Large Language Model](), [SQL](), [datasets](), [Agent Harness]() Tags: [agentic](), [ai](), [airflow](), [community](), [csv](), [llm](), [sql](), [survey](), [tutorial](), [workflow]() ### AI overview This article explains how to build agentic workloads on Airflow 3 using the 2025 Airflow Community Survey dataset and the apache-airflow-providers-common-ai provider. It distinguishes single-query LLM pipelines from workflows that query multiple independent dimensions and synthesize their results, mapping each sub-query to a named Airflow task and using Dynamic Task Mapping and XCom for orchestration and observability. ### Source excerpt A question like "How does AI tool usage vary across Airflow versions?" has a natural SQL shape: one cross-tabulation, one result. A question like "What does a typical Airflow deployment look like for practitioners who are actively using AI in their workflow?" does not. It requires querying executor type, deployment method, cloud provider, and Airflow version independently, each filtered to the same respondent group, then synthesizing the results into a coherent picture. No single query returns the answer. The answer emerges from the relationship between all of them. This is where Airflow's agentic pattern begins: not when you add an LLM to a workflow, but when the structure of the work itself depends on running multiple LLM calls whose outputs feed a synthesis step. This post builds that pattern using the 2025 Airflow Community Survey data set and the apache-airflow-providers-common-ai provider for Airflow 3. If you haven't read the introductory survey analysis post yet, start there for a walkthrough of the single-query interactive and scheduled pipelines. This post picks up where that one ends. The Agentic Gap in the Single-Query Pattern The interactive and scheduled survey DAGs from the introductory post each do one thing: translate a natural language question into SQL, execute it against the CSV, and return the result. The LLM is involved once. The structure of the pipeline does not change based on what that LLM call returns. That is not a limitation to fix. It is the right design for that class of question. For a large fraction of production AI workflows, a single well-structured LLM call with good context is sufficient and preferable. The pattern becomes agentic when two things are true simultaneously: The question requires querying multiple independent dimensions The synthesis step, the thing that produces the final answer, depends on all of those results In an agent harness framework, this would be handled inside a reasoning loop: the LLM decides to call a to ## Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3 DevFeed: [Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Artificial Intelligence](), [Large Language Model](), [SQL](), [CSV](), [data]() Tags: [ai](), [airflow](), [apache-airflow](), [automated](), [community](), [configuration](), [csv](), [executor](), [llm](), [pipelines](), [providers](), [scheduled](), [sql](), [tutorial]() ### AI overview A tutorial showing how to build interactive and scheduled AI analysis pipelines with Airflow 3. The pipelines use survey CSV data, generate and run SQL queries, validate schemas, and either request human approval or email results automatically. ### Source excerpt The 2025 Airflow Community Survey collected responses from nearly 6,000 practitioners across 168 questions. You can open a spreadsheet and filter, or write SQL by hand. But what if you could just ask a question and have Airflow figure out the query, run it, and bring the result back for your approval? This post builds two pipelines that do exactly that, using the apache-airflow-providers-common-ai provider for Airflow 3. The first pipeline is interactive: a human reviews the question before it reaches the LLM and approves the result before the DAG finishes. The second is scheduled: it downloads fresh survey data, validates the schema, runs the query unattended, and emails the result. If you haven't seen the common.ai provider overview yet, start there for a tour of all the operators. This post goes deep on a concrete end-to-end example. Two Pipelines, One Example File Both DAGs live in example_llm_survey_analysis.py and share the same schema context and datasource configuration. example_llm_survey_interactive: trigger manually, review at both ends: prompt_confirmation -> generate_sql -> run_query -> extract_data -> result_confirmation (HITLEntryOperator) (LLMSQLQuery) (Analytics) (@task) (ApprovalOperator) example_llm_survey_scheduled: runs @monthly, no human in the loop: download_survey -> prepare_csv -> check_schema -> generate_sql -> run_query -> extract_data -> send_result (HttpOperator) (@task) (LLMSchema (LLMSQLQuery) (Analytics) (@task) (@task / Email) Compare) The Data The Airflow Community Survey 2025 CSV has 5,856 rows and 168 columns covering everything from Airflow version and executor type to cloud provider, company size, and AI tool usage. A few highlights from the data: 3,320 respondents identify as Data Engineers 2,032 use AWS as their primary cloud provider for Airflow 1,445 are already running Airflow 3 1,351 say they often use AI tools to write Airflow code Those last two numbers together are part of why this example exists: the people most likely to use th ## Managing Sensitive Data in jOOQ 3.21+ Logs DevFeed: [Managing Sensitive Data in jOOQ 3.21+ Logs]() Original publisher: [Read original article]() Author: lukaseder Published: 2026-03-27T12:45:19Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [Logging](), [sensitive data](), [Security](), [configuration](), [Code]() Tags: [configuration](), [csv](), [debug-logging](), [jooq](), [jooq-in-use](), [json](), [logging](), [logs](), [maven](), [redacted-columns](), [security](), [sensitive-data](), [xml]() ### AI overview This article explains how jOOQ debug logging can expose query bind values and fetched records in application logs, creating security concerns in production. It describes a code-generation configuration available in commercial jOOQ distributions to mask exported data, including text, HTML, and optionally CSV, JSON, or XML output. ### Source excerpt One of jOOQ's most popular feature is the out-of-the-box debug logging experience. jOOQ developers find this feature very useful when developing their applications. Assuming you run a jOOQ query and configure your logger to print DEBUG log output: When this query is executed, your log output might contain something like this: Executing query : select ... Continue reading Managing Sensitive Data in jOOQ 3.21+ Logs -> ## Airflow Survey 2025 DevFeed: [Airflow Survey 2025]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-01-22T00:00:00Z Content type: article Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [data](), [CSV]() Tags: [2025](), [airflow](), [analysis](), [apache-airflow](), [community](), [csv](), [download](), [form](), [graphics](), [interactive](), [report](), [responses](), [share](), [survey](), [users](), [view]() ### AI overview This page presents the Airflow Survey 2025 interactive report, hosted by Astronomer and sponsored and supported by the Apache Airflow community. The supplied evidence does not include the survey findings. ### Source excerpt The interactive report is hosted by Astronomer. The Apache Airflow community thanks Astronomer for running this survey, for sponsoring it and providing the report in this form, and for their effort in marketing, analysis, and preparing the graphics. View raw data Download survey responses (CSV) ## Quick and dirty loading of CSV files DevFeed: [Quick and dirty loading of CSV files]() Original publisher: [Read original article]() Author: depesz Published: 2026-01-07T00:10:32Z Content type: article Language: en Sources: [select \* from depesz;]() Topics: [CSV](), [PostgreSQL](), [Database](), [JavaScript]() Tags: [ai](), [announcements](), [claude](), [csv](), [database](), [irc](), [javascript](), [node](), [pg-csv-loader](), [postgresql](), [uncategorized]() ### AI overview The author describes building a JavaScript/Node tool that automatically detects CSV delimiters, quote characters, column names, and data types before loading the data into a default PostgreSQL database. The tool was tested on several CSV files and handled PostgreSQL CSV logs, while the author added tests and improved datatype detection after using Claude to create the basics. ### Source excerpt Back in September 2025, David Fetter asked on IRC, about a tool to quickly load CSV to database. One that would require minimal configuration, will try to figure out as much as possible on its own. I thought that it would be great idea. Plus, I'm trying to learn more JavaScript / Node, so figured ... Continue reading "Quick and dirty loading of CSV files" ## Best places to commute from in Warsaw: public transit heatmap DevFeed: [Best places to commute from in Warsaw: public transit heatmap]() Original publisher: [Read original article]() Published: 2025-12-01T23:00:00Z Content type: article Language: en Sources: [Tomasz Nurkiewicz around Java and concurrency]() Topics: [data](), [API](), [CSV](), [Database](), [dataset](), [Google]() Tags: [api](), [csv](), [data](), [database](), [dataset](), [google]() ### AI overview The article describes a project to estimate commute times from Warsaw addresses to Metro Świętokrzyska using public transit and car travel. It uses Warsaw address data, Google Distance API, and a SQLite3 database, but the author collected data for only about 4% of the records after exhausting the API's free plan. ### Source excerpt Location, location, location! That's what the real estate agent will tell you when asked what's the most important factor when choosing the place to live. When living in a modern, busy city like Warsaw you have two choices: traveling by car or by public transit. I came up with the idea to automate finding the best place to live in Warsaw, transport-wise. My plan is to estimate the time it takes to commute from every address in Warsaw to the city center. I'm focusing on public transport and I'd like to find both hidden gems and transport black holes. That is, places with great commute time despite the distance and the opposite - respectively. ## Lessons From 9 More Years of Tricky Bugs DevFeed: [Lessons From 9 More Years of Tricky Bugs]() Original publisher: [Read original article]() Author: Henrik Warne Published: 2025-06-15T08:17:28Z Content type: opinion Language: en Sources: [Henrik Warne]() Topics: [debugging](), [coding](), [Testing](), [CSV](), [Database]() Tags: [blog](), [bugs](), [ci](), [code](), [coding](), [csv](), [database](), [debugging](), [errors](), [learning](), [programming](), [testing]() ### AI overview A retrospective review of tricky bugs tracked since 2002 examines lessons from coding, testing, and debugging. Examples include empty values, date logic, legacy data formats, aliased dictionaries, and uncommitted local changes. ### Source excerpt Since 2002, I have been keeping track of all the tricky bugs I have come across. Nine years ago, I wrote a blog post with the lessons learned from the bugs up till then. Now I have reviewed all the ... Continue reading -> ## Migrating from Simple Analytics to self-hosted Plausible DevFeed: [Migrating from Simple Analytics to self-hosted Plausible]() Original publisher: [Read original article]() Author: Harsh Shandilya Published: 2025-04-22T11:13:00Z Content type: article Language: en Sources: [Posts on Harsh Shandilya]() Topics: [migration](), [Self-hosted](), [Netlify](), [clickhouse](), [CSV](), [data](), [Website](), [MySQL](), [PHP]() Tags: [analytics](), [clickhouse](), [csv](), [data](), [elixir](), [github](), [hosting](), [migration](), [mysql](), [netlify](), [nixos](), [php](), [plausible](), [postgres](), [privacy](), [self-hosted](), [selfhosting](), [simpleanalytics]() ### AI overview A personal account of migrating a blog from Simple Analytics to self-hosted Plausible on a netcup server running NixOS. The article covers choosing Plausible over Matomo, configuring the service, replacing the website's analytics snippet, improving privacy for visitors who disable JavaScript, and beginning the migration of exported historical data. ### Source excerpt Documenting the misadventures of self-hosting my site analytics ## Airflow Survey 2024 DevFeed: [Airflow Survey 2024]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2025-02-27T00:00:00Z Content type: article Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [data](), [CSV]() Tags: [airflow](), [apache](), [community](), [csv](), [data](), [download](), [graphics](), [interactive](), [report](), [survey](), [users]() ### AI overview Airflow Survey 2024 is an interactive report hosted by Astronomer, based on a survey run with support from the Apache Airflow community. The page provides access to raw data and survey responses in CSV format. ### Source excerpt The interactive report is hosted by Astronomer. The Apache Airflow community thanks Astronomer for running this survey, for sponsoring it and providing the report in this form, and for their effort in marketing, analysis, and preparing the graphics. View raw data Download survey responses (CSV) ## The problems of Kotlin/Wasm I had when making a game DevFeed: [The problems of Kotlin/Wasm I had when making a game]() Original publisher: [Read original article]() Published: 2025-01-20T00:00:00Z Content type: article Language: en Sources: [Kt. Academy]() Topics: [wasm](), [Kotlin](), [compose-multiplatform](), [multiplatform](), [CSV]() Tags: [challenges](), [compose](), [compose-multiplatform](), [csv](), [game](), [kotlin](), [kotlin-wasm](), [wasm](), [workshop-learning-programming]() ### AI overview An account of building a fruit-based collection-processing game with Compose Multiplatform and publishing it to Kotlin/Wasm. The author describes limitations in Kotlin reflection on common modules, an illegal cast exception occurring on Wasm, and an alternative approach involving generating challenges on the JVM and loading them from a CSV file. ### Source excerpt A short story of the problems I needed to face when publishing a simple game made with Kotlin/Wasm. ## Issue #1 - The ML platform ghost ship DevFeed: [Issue #1 - The ML platform ghost ship]() Original publisher: [Read original article]() Author: Raphaël Hoogvliets Published: 2024-05-30T17:18:28Z Content type: opinion Language: en Sources: [Raphaël Hoogvliets]() Topics: [Machine learning](), [databricks](), [Data Science](), [CSV]() Tags: [csv](), [data-science](), [databricks](), [ml](), [ml-platform](), [platform]() ### AI overview A consultant recounts a data science project that produced a Databricks ML platform but could not integrate with organizational systems because of blocked IT migrations. The team resorted to manually uploaded CSV files to trigger the pipeline, and the project was eventually abandoned. ### Source excerpt Really? csv-files? 🥲 ## Nushell, after 8 months DevFeed: [Nushell, after 8 months]() Original publisher: [Read original article]() Author: Manideep Polireddi Published: 2024-04-29T11:15:00Z Content type: article Language: en Sources: [@okmanideep]() Topics: [Shell](), [Command-line interface](), [Programming](), [data]() Tags: [bash](), [cli](), [csv](), [json](), [nushell](), [powershell](), [shell](), [sqlite](), [yaml](), [zsh]() ### AI overview The author reflects on using Nushell as their primary shell for eight months. They find its table-based structured data model useful for CLI work, data wrangling, Kubernetes commands, and processing formats such as CSV, JSON, YAML, TOML, and SQLite, while reporting few problems interoperating with Unix programs that expect plain text. ### Source excerpt I have been using nushell as my primary shell for the last 8 months and I don't see myself going back to bash or zsh or powershell ## My Mac Dev Setup and Favorite Tools DevFeed: [My Mac Dev Setup and Favorite Tools]() Original publisher: [Read original article]() Author: Chris Hager Published: 2024-01-12T00:00:00Z Content type: tutorial Language: en Sources: [Chris Hager]() Topics: [Development](), [Tool](), [Command-line interface](), [Shell](), [Terminal](), [backups](), [passwords](), [Homebrew](), [Package Management](), [Encryption](), [App](), [Obsidian]() Tags: [backup](), [csv](), [database](), [dev](), [dev-setup](), [developer-setup](), [editor](), [encryption](), [homebrew](), [iterm2](), [mac](), [macos](), [setup](), [tools]() ### AI overview A practical guide to setting up a Mac for software development, covering system security, backups, password management, developer tools, favorite applications, terminal use, and keyboard shortcuts. ### Source excerpt How I setup and use my computer and tools for development. I wrote this mostly as a guide for myself, for the next time I setup a computer, and hope it might be useful to others too. I'd love to hear your thoughts, ideas and additional tips in the comments below! Related: How I setup my Terminal and Bash. Contents Mac setup General system setup Favorite apps Keyboard shortcuts Application shortcuts Arranging windows Browser Terminal & Shell Docker for Node. ## Fastest Way to Read Excel in Python DevFeed: [Fastest Way to Read Excel in Python]() Original publisher: [Read original article]() Author: Haki Benita Published: 2024-01-02T22:00:00Z Content type: article Language: en Sources: [Haki Benita]() Topics: [Python](), [data](), [CSV](), [parquet]() Tags: [article](), [articles](), [benchmarks](), [compare](), [csv](), [excel](), [generator](), [import](), [memory](), [parquet](), [performance](), [python](), [reading]() ### AI overview This article compares several ways to read Excel files from Python. It benchmarks importing data from a 25 MB XLSX file containing 500,000 rows and considers timing, memory use, generators, and preservation of Excel data types. ### Source excerpt I'm fairly sure that Excel is the most common way to store data, manipulate data, and yes(!), even pass data around. This is why it's not uncommon to find yourself reading Excel in Python. In this article I compare several ways to read Excel from Python. ## Welcome to the BAG: The Dutch Building and Addresses database DevFeed: [Welcome to the BAG: The Dutch Building and Addresses database]() Original publisher: [Read original article]() Published: 2023-06-21T08:20:43Z Content type: article Language: en Sources: [Bert Hubert's writings]() Topics: [Database](), [data](), [CSV](), [SQLite](), [API](), [Open Source]() Tags: [api](), [csv](), [data](), [database](), [gis](), [open-source](), [sqlite](), [tool](), [web]() ### AI overview This article introduces an open source tool that makes the Netherlands' official BAG database of addresses and buildings more accessible. It explains the database's coverage and describes options for obtaining the data as CSV or SQLite files, through an API, or via a web service. ### Source excerpt Recently, I wrote a small open source tool to make the official Dutch national address & building database more accessible. More about the tool below, but first an introduction. Last data update: 2026-07-08 If all you need is an up to date CSV file (or sqlite database) of all Dutch addresses, their zipcodes and coordinates, scroll down to the end. If all you need is a web service, try https://berthub.eu/pcode/2513AA/14. For more details (including a live demo), do read on. [Next page]()