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  1. Two habits keep our AI data pipeline bills sane: batch 20 to 30 items per LLM call instead of one-at-a-time, and use change detection so nothing gets re-enriched unless the source actually changed. go.upgradejs.com/e7w #AI #DataEngineering #LLM

  2. Two habits keep our AI data pipeline bills sane: batch 20 to 30 items per LLM call instead of one-at-a-time, and use change detection so nothing gets re-enriched unless the source actually changed. go.upgradejs.com/e7w #AI #DataEngineering #LLM

  3. Publications sur les Data Engineer Junior / ingénierie de données pour les juniors : 20k+ vues sur LinkedIn en quelques jours... Faisons du réseau ! Pas de conseils à vendre, juste échanger.

    #DataEngineer #DataEngineering #Junior #Recrutement #RH #Senior #OpenToWork #LinkedIn !

  4. New on the Psycopg blog: row-by-row streaming with server-side cursors 🐘🐍

    One of the most common mistakes with large PostgreSQL datasets is calling fetchall() and loading everything into memory. Server-side cursors solve this cleanly: stream results row by row, control batch size, and use it with async too.

    We walk through how it works, a practical export example, and what to watch out for. Link in replies 👇

  5. New on the Psycopg blog: row-by-row streaming with server-side cursors 🐘🐍

    One of the most common mistakes with large PostgreSQL datasets is calling fetchall() and loading everything into memory. Server-side cursors solve this cleanly: stream results row by row, control batch size, and use it with async too.

    We walk through how it works, a practical export example, and what to watch out for. Link in replies 👇

    #PostgreSQL #Python #Psycopg #OpenSource #FOSS #DataEngineering

  6. New partner onboarding used to eat days of dev time. We rebuilt it as an AI pipeline where the LLM drafts the mapping and deterministic code validates every output before it ships. Same job, minutes of compute instead of days of debugging. Proposal engine, not decision maker.

    go.upgradejs.com/uey

    #AI #DataPipelines #LLM #DataEngineering

  7. New partner onboarding used to eat days of dev time. We rebuilt it as an AI pipeline where the LLM drafts the mapping and deterministic code validates every output before it ships. Same job, minutes of compute instead of days of debugging. Proposal engine, not decision maker.

    go.upgradejs.com/uey

    #AI #DataPipelines #LLM #DataEngineering

  8. Next Wednesday: the engineer who built ColdFront live on the architecture.

    Most #Postgres databases pay SSD prices for data nobody queries. ColdFront gives you #PostgreSQL to #ApacheIceberg using the same #SQL and the same table names with writable cold tier.

    On August 19, 8 AM PST, catch @vyruss on the engineering + TLA+-verified distributed writes. Paul Rothrock will give a live demo.

    Q&A at the end.

    📅 us02web.zoom.us/webinar/regist

    🔗 github.com/pgEdge/ColdFront

    #DuckDB #DataEngineering #OpenSource

  9. Next Wednesday: the engineer who built ColdFront live on the architecture.

    Most #Postgres databases pay SSD prices for data nobody queries. ColdFront gives you #PostgreSQL to #ApacheIceberg using the same #SQL and the same table names with writable cold tier.

    On August 19, 8 AM PST, catch @vyruss on the engineering + TLA+-verified distributed writes. Paul Rothrock will give a live demo.

    Q&A at the end.

    📅 us02web.zoom.us/webinar/regist

    🔗 github.com/pgEdge/ColdFront

    #DuckDB #DataEngineering #OpenSource

  10. Snowflake's pg_lake is now GA, and its data mirroring preview runs logical decoding inside Postgres via a snowflake_cdc extension rather than streaming to a consumer that knows nothing about the server.

    Better engineering. Also a tighter coupling.

    snowflake.com/en/blog/engineer

    pgexperts.com

  11. Snowflake's pg_lake is now GA, and its data mirroring preview runs logical decoding inside Postgres via a snowflake_cdc extension rather than streaming to a consumer that knows nothing about the server.

    Better engineering. Also a tighter coupling.

    snowflake.com/en/blog/engineer

    pgexperts.com

    #PostgreSQL #DataEngineering

  12. The underrated part of an AI data pipeline is the trash filter. Classification catches CTAs and promo junk before anything hits the database, and every human correction feeds back into the extraction configs. The pipeline gets better with use instead of rotting. go.upgradejs.com/dxi #AI #DataPipelines #DataEngineering

  13. The underrated part of an AI data pipeline is the trash filter. Classification catches CTAs and promo junk before anything hits the database, and every human correction feeds back into the extraction configs. The pipeline gets better with use instead of rotting. go.upgradejs.com/dxi #AI #DataPipelines #DataEngineering

  14. Spec-Driven Data Engineering turns business rules, schemas, validation, and orchestration into versioned contracts that guide AI coding agents. hackernoon.com/why-ai-assisted #dataengineering

  15. Spec-Driven Data Engineering turns business rules, schemas, validation, and orchestration into versioned contracts that guide AI coding agents. hackernoon.com/why-ai-assisted #dataengineering

  16. Every CDC pipeline you run pulls a logical decoding stream from outside the server. Snowflake's new mirroring inverts it: an in-server extension (snowflake_cdc) pushes batches into Iceberg change logs plus a meta log, and the target replays that log as a state machine.

    The interesting claim is about DDL sequencing, not throughput.

    snowflake.com/en/blog/engineer

    pgexperts.com

  17. Every CDC pipeline you run pulls a logical decoding stream from outside the server. Snowflake's new mirroring inverts it: an in-server extension (snowflake_cdc) pushes batches into Iceberg change logs plus a meta log, and the target replays that log as a state machine.

    The interesting claim is about DDL sequencing, not throughput.

    snowflake.com/en/blog/engineer

    pgexperts.com

    #PostgreSQL #DataEngineering #CDC

  18. Tired of Big Tech's unpredictable cloud egress fees? 💸☁️

    While storing data in the public cloud is cheap, moving your AI datasets or Kubernetes workloads costs a fortune. The smartest move in 2026? Self-hosting your own S3-compatible Object Storage!

    We just published a step-by-step guide on deploying a secure, high-performance #MinIO server on #Ubuntu 24.04.
    servers99.com/tutorials/howto/

    #SelfHosting #Linux #DevOps #CloudComputing #SysAdmin #OpenSource #DataEngineering

  19. Tired of Big Tech's unpredictable cloud egress fees? 💸☁️

    While storing data in the public cloud is cheap, moving your AI datasets or Kubernetes workloads costs a fortune. The smartest move in 2026? Self-hosting your own S3-compatible Object Storage!

    We just published a step-by-step guide on deploying a secure, high-performance #MinIO server on #Ubuntu 24.04.
    servers99.com/tutorials/howto/

    #SelfHosting #Linux #DevOps #CloudComputing #SysAdmin #OpenSource #DataEngineering

  20. SapixDB calls itself the world's first agent-native database — where every data domain is owned by an AI agent, every record is cryptographically signed and hash-linked, and schemas evolve under human governance. No migrations. No DBA gates. Built from the ground up for AI agents. #Database #AI #DataEngineering

    #Database #AI #DataEngineering

  21. SapixDB calls itself the world's first agent-native database — where every data domain is owned by an AI agent, every record is cryptographically signed and hash-linked, and schemas evolve under human governance. No migrations. No DBA gates. Built from the ground up for AI agents. #Database #AI #DataEngineering

    #Database #AI #DataEngineering

  22. Whether you’re migrating to Airflow 3, scaling data pipelines, bringing AI and ML workloads into production, or defining your organization’s data strategy, you’ll leave with ideas you can put into practice.

    📅 August 31–September 2, 2026
    📍 Hyatt Regency Austin

    Don’t just watch the AI revolution unfold. Learn how to orchestrate it.

    Secure your spot: airflowsummit.org/tickets/

  23. Whether you’re migrating to Airflow 3, scaling data pipelines, bringing AI and ML workloads into production, or defining your organization’s data strategy, you’ll leave with ideas you can put into practice.

    📅 August 31–September 2, 2026
    📍 Hyatt Regency Austin

    Don’t just watch the AI revolution unfold. Learn how to orchestrate it.

    Secure your spot: airflowsummit.org/tickets/

    #ApacheAirflow #DataEngineering #ArtificialIntelligence #MLOps #AirflowSummit

  24. Earlier this year, Daniele gave a talk at @pgconfdev in Vancouver titled "Psycopg: 20 years of mostly friendly coexistence with libpq" ❤️

    Two decades of Python and PostgreSQL working together and there are still stories worth telling. The recording is now available to watch online. Link in replies 👇

  25. Earlier this year, Daniele gave a talk at @pgconfdev in Vancouver titled "Psycopg: 20 years of mostly friendly coexistence with libpq" ❤️

    Two decades of Python and PostgreSQL working together and there are still stories worth telling. The recording is now available to watch online. Link in replies 👇

    #PostgreSQL #Python #Psycopg #OpenSource #FOSS #PGConf #DataEngineering

  26. How to Get Started with Databricks Free Edition (No Cloud Account Required)

    Getting started with Databricks doesn’t require a corporate cloud subscription or a credit card. Databricks replaced its legacy Community Edition with the updated Databricks Free Edition, giving students, hobbyists, and practitioners free access to build AI applications, run notebooks, and learn real-world data engineering tools.

    How to Access Databricks Free Edition

    1. Go to the Free Edition PageHead directly to databricks.com/learn/free-edition.
    2. Sign UpClick “Sign up for Free Edition”. Enter your basic details (name, email, and company/school).> Tip: You do not need a business email or a cloud account (AWS/Azure/GCP) to sign up.
    3. Verify Your EmailCheck your inbox for a verification email from Databricks and click the link to set your password.
    4. Launch Your WorkspaceLog in to access your free Data Intelligence Platform workspace. You can immediately create interactive Python/SQL notebooks, test LLMs via the Databricks Playground, and use the AI-powered Databricks Assistant to help you write code.

    What’s Included (and What’s Not)

    Included in Free EditionKey LimitationsDatabricks Assistant (AI coding help)Non-commercial use only (personal learning & projects)GenAI Playground & agent builder toolsShared, lightweight compute (not for heavy workloads)Interactive Dashboards & Genie natural language analyticsFair usage limits on active cluster hoursFree Databricks Academy training coursesNo enterprise cluster management or live production deployments #AI #ArtificialIntelligence #Data #DataAnalytics #DataEngineering #DataScience #Databricks #FreeEdition #GenerativeAI #MachineLearning #Programming #Python #Tech #TechTutorial #technology
  27. 🛠️ pracdata/awesome-open-source-data-engineering

    A curated list of open source tools used in analytics platforms and data engineering ecosystem

    Compiles software tools across storage, streaming, integration, and metadata management for data architecture platforms.

    ⭐ Stars: 592
    📅 Last Update: Jul 24, 2026

    github.com/pracdata/awesome-op

    #selfhosted #homelab #selfhost #selfhosting #opensource #dataengineering #databases