Rencana pembelajaran mata pelajaran kimia tentang teori asam basa ini membahas:
1. Materi pelajaran tentang teori asam basa, sifat larutan, pH, dan aplikasinya dalam lingkungan.
2. Metode pembelajaran melalui ceramah, diskusi, dan eksperimen di laboratorium.
3. Tujuan pembelajaran agar siswa memahami konsep asam basa dan dapat menghitung pH serta menentukan kadar zat melal
Dokumen tersebut membahas tentang standar kompetensi dan kompetensi dasar stoikiometri larutan, termasuk definisi stoikiometri larutan, jenis-jenis perhitungan stoikiometri seperti stoikiometri sederhana, dengan pereaksi pembatas, dan campuran larutan, serta contoh soal perhitungan stoikiometri.
Rencana pembelajaran mata pelajaran kimia tentang teori asam basa ini membahas:
1. Materi pelajaran tentang teori asam basa, sifat larutan, pH, dan aplikasinya dalam lingkungan.
2. Metode pembelajaran melalui ceramah, diskusi, dan eksperimen di laboratorium.
3. Tujuan pembelajaran agar siswa memahami konsep asam basa dan dapat menghitung pH serta menentukan kadar zat melal
Dokumen tersebut membahas tentang standar kompetensi dan kompetensi dasar stoikiometri larutan, termasuk definisi stoikiometri larutan, jenis-jenis perhitungan stoikiometri seperti stoikiometri sederhana, dengan pereaksi pembatas, dan campuran larutan, serta contoh soal perhitungan stoikiometri.
Bab pendahuluan mendiskusikan ruang lingkup ilmu kimia yang mempelajari susunan, sifat, dan perubahan materi serta manfaat mempelajari kimia untuk memahami alam sekitar dan menghasilkan produk berguna. Juga membahas hubungan kimia dengan ilmu lain dan perkembangan sejarah kimia dari peradaban Mesir kuno hingga teori atom modern John Dalton. Bab ini memperkenalkan pengantar ke laboratorium kimia dan alat-
Bab pendahuluan mendiskusikan ruang lingkup ilmu kimia yang mempelajari susunan, sifat, dan perubahan materi serta manfaat mempelajari kimia untuk memahami alam sekitar dan menghasilkan produk berguna. Juga membahas hubungan kimia dengan ilmu lain dan perkembangan sejarah kimia dari peradaban Mesir kuno hingga teori atom modern John Dalton. Bab ini memperkenalkan pengantar ke laboratorium kimia dan alat-
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.