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Getting a big tech data engineer job in 2016: - do you know SQL? - yes - here’s $500k Getting a big tech data engineer job in 2024: - do you know Spark, Kafka, Iceberg? - yes - did you shake hands with Bill Inmon when he invented the...

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Zach Wilson

48,608 subscribers

43,547 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 5

Фото профиля DataProfessorX
DataProfessorX2 лет назад

People still underestimate the AI effect, In a couple years more, it will become more clear

Фото профиля MicroSectors
MicroSectors3 лет назад

Are you a sophisticated investor that is bullish on big tech stocks? Learn more about $BULZ here:

Фото профиля XyzzyPoof
XyzzyPoof2 лет назад

4) the y401B Visa workers are paid 50% or less of what you are

Фото профиля Zach Morris Wilson
Zach Morris Wilson2 лет назад

@SeamansRC No they aren’t 😂

Фото профиля silverlightwa
silverlightwa2 лет назад

Damn you are still grifting?

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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:
1:19

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

29,420 просмотров • 1 год назад

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!
1:06

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Solana ID 🪷

30,458 просмотров • 2 лет назад

I don't know if SQL is bad or not but losing user data in the middle of a workflow certainly is! Did you know you can block React Router navigations in to prevent losing user data? let blocker = useBlocker(shouldBlock) blocker.state blocker.proceed() blocker.reset()
6:54

I don't know if SQL is bad or not but losing user data in the middle of a workflow certainly is! Did you know you can block React Router navigations in to prevent losing user data? let blocker = useBlocker(shouldBlock) blocker.state blocker.proceed() blocker.reset()

Ryan Florence

41,500 просмотров • 1 год назад

🚨Governor DeSantis says he is about to sign legislation that will effectively BLOCK hyperscale AI data centers from coming to Florida! “They come in and these big tech companies are building these massive data centers that take up as much power as a city of half a million people. Well guess what? Supply and demand. Do you think your electricity rates go up when that happens? Yes, it does. So this would BLOCK that basically. No rate payer should have to pay ONE RED CENT because of big tech and we'll be one of the first states to do it!”
1:38

🚨Governor DeSantis says he is about to sign legislation that will effectively BLOCK hyperscale AI data centers from coming to Florida! “They come in and these big tech companies are building these massive data centers that take up as much power as a city of half a million people. Well guess what? Supply and demand. Do you think your electricity rates go up when that happens? Yes, it does. So this would BLOCK that basically. No rate payer should have to pay ONE RED CENT because of big tech and we'll be one of the first states to do it!”

Chris Nelson 🏝️🇺🇸

88,417 просмотров • 6 месяцев назад

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 or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?
0:51

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 or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Zach Wilson

20,412 просмотров • 1 год назад

#Web3 and digital property rights truly excite us! Here’s why: We live in a world where our data is owned and farmed by big tech companies. But web3 presents a future where we can regain ownership of our data and subsequently assume the lead on our freedom.
1:13

#Web3 and digital property rights truly excite us! Here’s why: We live in a world where our data is owned and farmed by big tech companies. But web3 presents a future where we can regain ownership of our data and subsequently assume the lead on our freedom.

Animoca Brands

10,009 просмотров • 2 лет назад

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?
1:59

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

Zach Wilson

16,688 просмотров • 1 год назад

Collins: People are saying you posted the job data early. You’re obviously not supposed to share that until the following morning. Did you do that on purpose? Trump: No. I don’t know. When people give me things, I post them.
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Collins: People are saying you posted the job data early. You’re obviously not supposed to share that until the following morning. Did you do that on purpose? Trump: No. I don’t know. When people give me things, I post them.

Acyn

44,500 просмотров • 8 месяцев назад

Trump's EPA wants to kill a rule that gives you a say before data centers go up in your neighborhood. This is exactly why people are fed up with this administration. Did Big Tech donors influence this decision? Congress needs to investigate this. Now.
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Trump's EPA wants to kill a rule that gives you a say before data centers go up in your neighborhood. This is exactly why people are fed up with this administration. Did Big Tech donors influence this decision? Congress needs to investigate this. Now.

Senator Mark Kelly

17,655 просмотров • 12 дней назад

Here’s a StudyBot that can help you learn SQL in 28 Days! With the help of Vondy, I have made a ChatBot that can help you learn SQL for Data Science with a Custom Study plan in 28 days! (Link to the StudyBot below 👇)
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Here’s a StudyBot that can help you learn SQL in 28 Days! With the help of Vondy, I have made a ChatBot that can help you learn SQL for Data Science with a Custom Study plan in 28 days! (Link to the StudyBot below 👇)

Sasi 📊📈

24,163 просмотров • 2 лет назад

GM Cardano ☀️🩵 Big Tech profits from your digital data! Profila gives you the tools to take control, protect your privacy, and even get paid for sharing your data on your terms. Watch my video to find out how they do it :)
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GM Cardano ☀️🩵 Big Tech profits from your digital data! Profila gives you the tools to take control, protect your privacy, and even get paid for sharing your data on your terms. Watch my video to find out how they do it :)

Linda

30,705 просмотров • 1 год назад

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!
1:33

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

DeepLearning.AI

20,833 просмотров • 2 лет назад

BIG BROTHER is watching: 🚨 The surveillance state just invented a new enemy: Americans who do not want AI data centers in their backyard. Question the noise, energy drain, water use, or ecological damage, and suddenly you are an “anti-tech extremist.”
1:47

BIG BROTHER is watching: 🚨 The surveillance state just invented a new enemy: Americans who do not want AI data centers in their backyard. Question the noise, energy drain, water use, or ecological damage, and suddenly you are an “anti-tech extremist.”

Redacted

11,410 просмотров • 2 месяцев назад

l landed my first Data Analyst job in just 4 months-learning to code using only my phone! No laptop, no excuses - Just Start!! If you're trying to break into tech or start your data career, this is your sign! Watch the full video on YouTube to hear my full journey and how you can do it too! #DataAnalytics #TechCareer
1:07

l landed my first Data Analyst job in just 4 months-learning to code using only my phone! No laptop, no excuses - Just Start!! If you're trying to break into tech or start your data career, this is your sign! Watch the full video on YouTube to hear my full journey and how you can do it too! #DataAnalytics #TechCareer

Tina Okonkwo💜

20,592 просмотров • 1 год назад

Why are we forced to pay for data centres? Ireland to be the dumping group of these big tech companies data centres & their algorithms. We should be able to know what's going on in them. Instead, this government is allowing them to use up massive amounts of water & electricity.
0:38

Why are we forced to pay for data centres? Ireland to be the dumping group of these big tech companies data centres & their algorithms. We should be able to know what's going on in them. Instead, this government is allowing them to use up massive amounts of water & electricity.

Paul Murphy 🇵🇸

14,333 просмотров • 8 месяцев назад

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 просмотров • 10 месяцев назад

Q: Data from the nonpartisan Bureau of Labor Statistics shows that when you took office there was a 2.7% unemployment rate, and it's now 3.6%. Do you quibble with the data? Winsome Earle Sears: We're creating jobs. I don't know where they are getting their stats from
0:37

Q: Data from the nonpartisan Bureau of Labor Statistics shows that when you took office there was a 2.7% unemployment rate, and it's now 3.6%. Do you quibble with the data? Winsome Earle Sears: We're creating jobs. I don't know where they are getting their stats from

FactPost

223,039 просмотров • 11 месяцев назад

Back in February, Paul Renner called for a moratorium on AI data centers in Florida. He was right. These massive AI data centers threaten our power grid, drain our water, raise costs on families, and hand Florida’s future to Big Tech. Florida First means people first. Stand with Renner. Stop the AI data center takeover. #BigTechByron #DataCenterDonalds #BankFraudByron
2:52

Back in February, Paul Renner called for a moratorium on AI data centers in Florida. He was right. These massive AI data centers threaten our power grid, drain our water, raise costs on families, and hand Florida’s future to Big Tech. Florida First means people first. Stand with Renner. Stop the AI data center takeover. #BigTechByron #DataCenterDonalds #BankFraudByron

Ann Vandersteel™️

10,568 просмотров • 3 месяцев назад