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Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake... show more

Zach Wilson

50,936 subscribers

20,368 次观看 • 11 个月前 •via X (Twitter)

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Nina Taft11 个月前

Great advise, I would also encourage folks who just started and setting up own infra to setup billing alerts to a)don’t get unexpected bills b)get a legit biz sense in the price of data manipulation

Mobile Scanner 的头像

Mobile Scanner1 年前

Scan any documents, convert images into text, PDF files, etc. 👍

prodigal son 的头像

prodigal son11 个月前

Going to incorporate this with my cloud dev studies to add some level of functionality to my pipelines

Row Skilli-Vitzky 的头像

Row Skilli-Vitzky11 个月前

What of doing all this with Airbyte , Snowflake and dbtFusion for Ingestion, Storage and Orchestration respectively I’ve been curious to know your thoughts on the future of loading data with python especially as these tools get cheaper

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Fran11 个月前

Gonna try this

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Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:

Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:

Zach Wilson

29,164 次观看 • 1 年前

We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:

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Andrew Ng

118,937 次观看 • 1 年前

Perplexity Computer can now use Snowflake as a data source and work as a personal Data Science team if you are on Snowflake And the Data Analytics team as well 👀

Perplexity Computer can now use Snowflake as a data source and work as a personal Data Science team if you are on Snowflake And the Data Analytics team as well 👀

🚨 AI News | TestingCatalog

20,048 次观看 • 1 个月前

Import large data sets in seconds! With Appwrite’s new CSV imports feature, you can import documents into your Appwrite collections using a simple CSV file. And the best part, you don’t even need a custom script. Check out this video to learn how it works.

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Appwrite

44,969 次观看 • 11 个月前

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Reflex

55,024 次观看 • 1 年前

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.

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298,504 次观看 • 2 年前

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P.M

7,328,078 次观看 • 3 个月前

v0’s Snowflake integration is now in public preview. Prompt v0 to use Snowflake, connect your account, and watch as it generates polished dashboards with your data.

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v0

112,352 次观看 • 16 天前

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12,637 次观看 • 3 个月前

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Google Cloud

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GitHub

29,212 次观看 • 1 年前

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Akshay 🚀

65,672 次观看 • 7 个月前

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Santiago

125,928 次观看 • 8 个月前

Urban planners spend hours wrangling GIS data for stakeholder presentation, analysis, and reviews. Unify your city planning data in Google Earth with new SHP imports. Securely combine zoning data, environmental constraints, and property boundaries as performant, cloud-native data layers to create a single source of truth for your entire team. Upload your SHP files to Google Earth today.

Urban planners spend hours wrangling GIS data for stakeholder presentation, analysis, and reviews. Unify your city planning data in Google Earth with new SHP imports. Securely combine zoning data, environmental constraints, and property boundaries as performant, cloud-native data layers to create a single source of truth for your entire team. Upload your SHP files to Google Earth today.

Google Earth

19,706 次观看 • 1 个月前