![Revisit consent button](../images/body-df29beacd7496220cc94.svg) We value your privacy We use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking "Accept All", you consent to our use of cookies. Customise Reject All Accept All Customise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg) We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more NecessaryAlways Active Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data. * Cookie BCTempID * Duration 10 minutes * Description No description available. * Cookie __cf_bm * Duration 1 hour * Description This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. * Cookie AWSALBCORS * Duration 7 days * Description Amazon Web Services set this cookie for load balancing. * Cookie _cfuvid * Duration session * Description Cloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services * Cookie li_gc * Duration 6 months * Description Linkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes. * Cookie __hssrc * Duration session * Description This cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session. * Cookie __hssc * Duration 1 hour * Description HubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie. * Cookie wpEmojiSettingsSupports * Duration session * Description WordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly. * Cookie BCSessionID * Duration 1 year 1 month 4 days * Description Blueconic sets this cookie as a unique identifier for the BlueConic profile. * Cookie _octo * Duration 1 year * Description No description available. * Cookie logged_in * Duration 1 year * Description No description available. * Cookie __Secure-YEC * Duration past * Description YouTube sets this cookie to stores the user's video player preferences using embedded YouTube video * Cookie __eoi * Duration 6 months * Description Description is currently not available. * Cookie AWSALBTGCORS * Duration 7 days * Description No description available. * Cookie login-status-p * Duration past * Description Description is currently not available. * Cookie AWSALBTG * Duration 7 days * Description No description available. * Cookie csrf_token * Duration session * Description No description available. * Cookie token_v2 * Duration 1 day * Description Description is currently not available. * Cookie D * Duration 1 year * Description Description is currently not available. * Cookie PHPSESSID * Duration session * Description This cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed. * Cookie VISITOR_PRIVACY_METADATA * Duration 6 months * Description YouTube sets this cookie to store the user's cookie consent state for the current domain. * Cookie cookietest * Duration session * Description The cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience. * Cookie __Host-airtable-session * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie __Host-airtable-session.sig * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie m * Duration 1 year 1 month 4 days * Description Stripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly. * Cookie BIGipServer* * Duration session * Description Marketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. * Cookie __cfruid * Duration session * Description Cloudflare sets this cookie to identify trusted web traffic. * Cookie _GRECAPTCHA * Duration 6 months * Description Google Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks. * Cookie __Secure-YNID * Duration 6 months * Description Google cookie used to protect user security and prevent fraud, especially during the login process. * Cookie cookieyes-consent * Duration 1 year * Description CookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors. Functional Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features. * Cookie lidc * Duration 1 day * Description LinkedIn sets the lidc cookie to facilitate data center selection. * Cookie brw * Duration 1 year * Description No description available. * Cookie brwConsent * Duration 5 minutes * Description Description is currently not available. * Cookie WMF-Uniq * Duration 1 year * Description Description is currently not available. * Cookie loom_anon_comment * Duration 1 year * Description No description available. * Cookie loom_referral_video * Duration session * Description Description is currently not available. * Cookie VISITOR_INFO1_LIVE * Duration 6 months * Description A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. * Cookie yt-remote-connected-devices * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie ytidb::LAST_RESULT_ENTRY_KEY * Duration Never Expires * Description The cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future. * Cookie yt-remote-device-id * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie yt-remote-session-name * Duration session * Description The yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video. * Cookie yt-remote-fast-check-period * Duration session * Description The yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos. * Cookie yt-remote-session-app * Duration session * Description The yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player. * Cookie yt-remote-cast-available * Duration session * Description The yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player. * Cookie yt-remote-cast-installed * Duration session * Description The yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video. * Cookie cp_session * Duration 3 months * Description Codepen sets this cookie for Help systems found in the website. * Cookie loid * Duration 1 year 1 month 4 days * Description This cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform. Analytics Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc. * Cookie __hstc * Duration 6 months * Description Hubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session). * Cookie hubspotutk * Duration 6 months * Description HubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts. * Cookie _ga * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors. * Cookie _ga_* * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to store and count page views. * Cookie __Host-psifi.analyticsTrace * Duration 6 hours * Description Description is currently not available. * Cookie __Host-psifi.analyticsTraceV2 * Duration 6 hours * Description Description is currently not available. * Cookie _gh_sess * Duration session * Description GitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form. * Cookie YSC * Duration session * Description YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. * Cookie ajs_anonymous_id * Duration 1 year * Description This cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before. * Cookie vuid * Duration 1 year 1 month 4 days * Description Vimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. Performance Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors. * Cookie AWSALB * Duration 7 days * Description AWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target. * Cookie acq * Duration past * Description Description is currently not available. * Cookie acq.sig * Duration past * Description Description is currently not available. * Cookie ptc * Duration 2 years * Description No description available. Advertisement Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns. * Cookie muc_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to collect user behaviour and interaction data to optimize the website. * Cookie guest_id_marketing * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie guest_id_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie personalization_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting. * Cookie guest_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences. * Cookie bcookie * Duration 1 year * Description LinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs. * Cookie __Secure-ROLLOUT_TOKEN * Duration 6 months * Description YouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment. * Cookie yt.innertube::nextId * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie yt.innertube::requests * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie session_tracker * Duration session * Description This cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website. * Cookie edgebucket * Duration session * Description Reddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content. * Cookie did * Duration 1 year * Description Arbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year. Uncategorised Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet. No cookies to display. Reject All Save My Preferences Accept All Toggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation [](https://towardsdatascience.com/) Publish AI, ML & data-science insights to a global community of data professionals. Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Data Engineering](https://towardsdatascience.com/category/data-science/data-engineering/) # I Thought Data Engineering Was Just Writing Scripts. I Was Wrong. I tried to make my ETL pipeline production-ready. Three things broke. Each one taught me something scripting alone never could. [Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) June 12, 20268 min read ![](../images/000.jpg) Created with Gemini AI After I built my first ETL pipeline, I thought I had a pretty good grip on what data engineering actually was. You extract data from somewhere, you clean it up, you load it somewhere useful. ETL. Simple enough. For context, I am a data analyst trying to transition into data engineering. I have been documenting that journey publicly, starting with a [12-month self-study roadmap](https://towardsdatascience.com/from-data-analyst-to-data-engineer-my-12-month-self-study-roadmap/) I put together earlier this year. The most recent step in that journey was [building my first ETL pipeline from scratch](https://towardsdatascience.com/i-built-my-first-etl-pipeline-as-a-complete-beginner-heres-exactly-how/) using the GitHub API, which I wrote about here on TDS. That pipeline worked. It pulled data, cleaned it, saved it to a CSV. I was happy with it. So I decided to push it further, make it more "production-ready" as the internet likes to say. What happened next genuinely surprised me. Not because things broke, but because of what the breaking revealed. ## The Original Pipeline The original pipeline was basic, which was fine because that was the point. Extract data from the GitHub API, do a bit of cleaning, save everything to a CSV file. It worked perfectly for what it was: a learning exercise. But a CSV file and a one-time script is not how data engineering works in the real world. I wanted to find out what "real world" actually meant in practice, so I decided to push the pipeline further and see what happened. Learn this step by step with the interactive [Data Engineer](https://roadmap.sh/data-engineer) roadmap. Here is the full original pipeline for anyone who hasn't read the previous article: python ``` import requests from datetime import datetime, timedelta url = "https://api.github.com/search/repositories" params = { "q": "language:python created:>2025-04-22", "sort": "stars", "order": "desc", "per_page": 30 } response = requests.get(url, params=params) data = response.json() import pandas as pd repos = [] for repo in data['items']: repos.append({ "name": repo['name'], "owner": repo['owner']['login'], "stars": repo['stargazers_count'], "forks": repo['forks_count'], "language": repo['language'], "description": repo['description'], "url": repo['html_url'], "created_at": repo['created_at'] }) df = pd.DataFrame(repos) df_clean = df.dropna(subset=['description']) df_clean = df_clean.copy() df_clean['viral'] = df_clean['stars'].apply(lambda x: 'Yes' if x > 50000 else 'No') df_clean = df_clean.sort_values('stars', ascending=False).reset_index(drop=True) df_clean.to_csv('github_trending_repos.csv', index=False) print("Pipeline complete. File saved.") ``` Simple, readable, and it works. But the moment you try to run it more than once, or come back to it the next day, the cracks start to show. ## Wall One: The Pipeline Had No Memory The first upgrade was straightforward. Instead of saving to a CSV file, I loaded the data into a SQLite database. SQLite is still just a single file, but it behaves like a real database. You can query it, check what's already in it, and build on top of it properly. It felt like a small change. It wasn't. I ran the pipeline once and got 22 repos. Then I ran it a second time without changing anything and checked the database. markup ``` Total rows: 44 Unique repos: 22 Duplicates: 22 ``` Honestly, I didn't expect it. I guessed it could happen but I never really thought it would. But I'm glad it did, because it was the first time I actually watched my pipeline break. And what it revealed was simple but important: the script had no memory. Every time it ran, it started completely fresh and blindly appended whatever it found. No warning, no error. Just "Pipeline complete" like everything was fine. This is where I came across a concept called idempotency. Idempotency is a fancy word for a simple idea. If something has already happened, it shouldn't happen again. In the context of a data pipeline, it means that running your pipeline once or running it ten times should always produce the same result. No extra rows, no duplicates, no silent corruption of your data. The fix was straightforward in principle. Before inserting anything into the database, the pipeline now checks if that record already exists. If it does, it removes it first, then inserts the fresh version. One small change in thinking, but it completely changes how reliable your pipeline is. And here's the part that stuck with me: a basic script will never think about this on its own. You have to build it in deliberately. That's not scripting anymore. That's engineering. ## Wall Two: The Data Disappeared Overnight The second wall was less technical and more unsettling. When I closed Colab for the night and came back the next day, there was this uneasy feeling. I had to run everything again from scratch and hope nothing broke, even though everything had worked perfectly the night before. The database I had carefully built was just gone. And I remembered that before this project, I had actually struggled to find my original ETL pipeline file. I spent time looking for it before I finally did. That feeling of almost losing your work stays with you. I knew there had to be a better way. A real pipeline cannot depend on someone being there to rerun it every morning. The data has to live somewhere that survives beyond the session. The fix here was mounting Google Drive directly inside Colab and pointing the database connection there instead of to the temporary Colab environment. One line change: python ``` conn = sqlite3.connect('/content/drive/MyDrive/github_repos.db') ``` Now the database lives in Google Drive. Close the session, restart the runtime, open a new notebook entirely. The data is still there waiting. But this fix revealed something bigger. If persisting data already requires thinking about where things live and how they survive, what happens when you need the pipeline to run automatically every day without you touching it at all? ## Wall Three: Nobody Can Press Run Forever The third wall was the one that excited me the most, which surprised me. Even with the duplicate problem solved and the database living safely in Google Drive, someone still has to open the notebook and press run. Colab is not a server. It is an interactive environment. It does not wake up at 3am, pull fresh data from the GitHub API, and go back to sleep. That is not what it was built for. And when I thought about it from a real world perspective, that clicked immediately. In an actual company, nobody is sitting there on alert at midnight waiting to run a script. The pipeline has to run itself. On a schedule. Reliably. Whether or not anyone is watching. This is where tools like Apache Airflow, Prefect, and cloud-based cron jobs come in. These are not Python scripts. They are systems that live on servers, manage schedules, handle failures, send alerts when something goes wrong, and keep a history of every run. Scheduling is honestly the concept I am most excited to dig into next, because it is where data engineering starts to feel like real infrastructure work. ## Walking Through the Key Changes Let me walk through the three actual changes I made to the pipeline and what each one does. ### 1. Swapping CSV for SQLite python ``` # Before df_clean.to_csv('github_trending_repos.csv', index=False) # After conn = sqlite3.connect('github_repos.db') df_clean.to_sql('repos', conn, if_exists='append', index=False) conn.close() ``` Saving to a CSV is fine for a one-time analysis. But a CSV is just a text file. You cannot easily query it, and it does not scale well as your data grows. SQLite is an actual database, which means you can run SQL queries on it, check what is already inside it, and build on top of it properly. Same simplicity, much more capability. ### 2. Fixing the duplicate problem python ``` cursor.execute(''' DELETE FROM repos WHERE url IN (SELECT url FROM repos_temp) ''') cursor.execute(''' INSERT INTO repos SELECT * FROM repos_temp ''') ``` This is the idempotency fix. Before inserting anything, the pipeline checks if that repo already exists in the database using its URL as a unique identifier. If it does, it deletes it first, then inserts the fresh version. So no matter how many times the pipeline runs, you always end up with clean, non-duplicated data. ### 3. Persisting data to Google Drive python ``` # Before conn = sqlite3.connect('github_repos.db') # After conn = sqlite3.connect('/content/drive/MyDrive/github_repos.db') ``` This is one line but it changes everything. Instead of saving the database to the temporary Colab environment where it disappears when the session closes, it saves directly to Google Drive. Close the notebook, restart the runtime, come back tomorrow. Your data is still there waiting for you. Here is the full upgraded pipeline putting all three changes together: python ``` import requests import pandas as pd import sqlite3 from datetime import datetime, timedelta # Extract yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d') url = "https://api.github.com/search/repositories" params = { "q": f"language:python created:>{yesterday}", "sort": "stars", "order": "desc", "per_page": 30 } response = requests.get(url, params=params) data = response.json() # Transform repos = [] for repo in data['items']: repos.append({ "name": repo['name'], "owner": repo['owner']['login'], "stars": repo['stargazers_count'], "forks": repo['forks_count'], "language": repo['language'], "description": repo['description'], "url": repo['html_url'], "created_at": repo['created_at'] }) df = pd.DataFrame(repos) df_clean = df.dropna(subset=['description']) df_clean = df_clean.copy() df_clean['viral'] = df_clean['stars'].apply(lambda x: 'Yes' if x > 50000 else 'No') df_clean = df_clean.sort_values('stars', ascending=False).reset_index(drop=True) # Load conn = sqlite3.connect('/content/drive/MyDrive/github_repos.db') cursor = conn.cursor() cursor.execute(''' CREATE TABLE IF NOT EXISTS repos ( name TEXT, owner TEXT, stars INTEGER, forks INTEGER, language TEXT, description TEXT, url TEXT, created_at TEXT, viral TEXT, loaded_at TEXT ) ''') df_clean['loaded_at'] = datetime.now().strftime('%Y-%m-%d') df_clean.to_sql('repos_temp', conn, if_exists='replace', index=False) cursor.execute(''' DELETE FROM repos WHERE url IN (SELECT url FROM repos_temp) ''') cursor.execute(''' INSERT INTO repos SELECT * FROM repos_temp ''') conn.commit() conn.close() print("Pipeline complete. Duplicates handled.") ``` ## So What Is Data Engineering, Really? If you had asked me what data engineering was after I built my first ETL pipeline, I would have said it was mostly about writing scripts. Extract, transform, load. Repeat. That is what it looked like from the outside. But after pushing that pipeline further and watching it break in three different ways, I think about it differently now. Data engineering is about building systems that are reliable, not just scripts that run. There is a difference. A script does what you tell it, once, when you tell it. A system handles failure, remembers what it has already done, persists data beyond a single session, and runs itself on a schedule without anyone watching. Idempotency, persistence, scheduling. None of these concepts showed up when I was running my pipeline once in a notebook. They only revealed themselves when I tried to make it work like something real. And in a real company, you cannot afford to get this wrong. The data your pipeline produces is being used to make decisions. If it is full of duplicates, or disappears overnight, or only runs when someone remembers to press a button, that is not a data pipeline. That is a liability. I still have a lot to learn. Scheduling is the next wall I am walking toward. But I am walking toward it now knowing that data engineering was never just about writing scripts. I just had to break a few things to see that. _This is an ongoing data engineering series. Follow along as I document every step of the journey, including the parts that don’t go smoothly._ _Connect with me on_[ _LinkedIn_](https://www.linkedin.com/in/ibrahim-salami-059863228/) _,_[_YouTube_](https://www.youtube.com/@ibbysalam) _, and_[ _Twitter_](https://x.com/IbbySalam) _._ ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * ![](../images/body-542334df68a32cd56c4a.jpeg) Written By ## [Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) [See all from Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) [Data Engineering](https://towardsdatascience.com/tag/data-engineering/)[Data Pipeline](https://towardsdatascience.com/tag/data-pipeline/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Etl](https://towardsdatascience.com/tag/etl/)[Python](https://towardsdatascience.com/tag/python/) Share This Article * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fi-thought-data-engineering-was-just-writing-scripts-i-was-wrong%2F&title=I%20Thought%20Data%20Engineering%20Was%20Just%20Writing%20Scripts.%20I%20Was%20Wrong.) * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fi-thought-data-engineering-was-just-writing-scripts-i-was-wrong%2F&title=I%20Thought%20Data%20Engineering%20Was%20Just%20Writing%20Scripts.%20I%20Was%20Wrong.) * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fi-thought-data-engineering-was-just-writing-scripts-i-was-wrong%2F&text=I%20Thought%20Data%20Engineering%20Was%20Just%20Writing%20Scripts.%20I%20Was%20Wrong.) Towards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program. [Write for TDS](https://towardsdatascience.com/submissions/) ## Related Articles * [![](../images/001.jpeg)](https://towardsdatascience.com/parallel-computing-92c4f818c/) ## [What is Parallel Computing?](https://towardsdatascience.com/parallel-computing-92c4f818c/) [Programming](https://towardsdatascience.com/category/programming/) Understanding the importance of parallel computing in the context of Data Engineering [Giorgos Myrianthous](https://towardsdatascience.com/author/gmyrianthous/) April 20, 20226 min read * [![](../images/002.jpeg)](https://towardsdatascience.com/my-experience-changing-careers-to-become-a-data-analyst-7d45d1f0f162/) ## [My Experience Changing Careers to Become a Data Analyst](https://towardsdatascience.com/my-experience-changing-careers-to-become-a-data-analyst-7d45d1f0f162/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) How to decide if data analytics is the right career for you [Vicky Yu](https://towardsdatascience.com/author/madfordata/) December 4, 20203 min read * [![](../images/003.jpeg)](https://towardsdatascience.com/the-most-for-your-money-from-amazon-s3-6536c78189da/) ## [The Most for Your Money from Amazon S3](https://towardsdatascience.com/the-most-for-your-money-from-amazon-s3-6536c78189da/) [Data Engineering](https://towardsdatascience.com/category/data-science/data-engineering/) Leverage the power of cloud storage with frugality in mind. [Vel Lesikov](https://towardsdatascience.com/author/vel-lesikov/) October 12, 20216 min read * [![](../images/004.jpg)](https://towardsdatascience.com/tensorflow-transform-ensuring-seamless-data-preparation-in-production-99ffcf49f535/) ## [TensorFlow Transform: Ensuring Seamless Data Preparation in Production](https://towardsdatascience.com/tensorflow-transform-ensuring-seamless-data-preparation-in-production-99ffcf49f535/) [Data Engineering](https://towardsdatascience.com/category/data-science/data-engineering/) Leveraging TensorFlow Transform for scaling data pipelines for production environments [Akila Somasundaram](https://towardsdatascience.com/author/akila29/) July 8, 20249 min read * [![](../images/005.jpg)](https://towardsdatascience.com/i-replaced-vector-dbs-with-googles-memory-agent-pattern-for-my-notes-in-obsidian/) ## [I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian](https://towardsdatascience.com/i-replaced-vector-dbs-with-googles-memory-agent-pattern-for-my-notes-in-obsidian/) [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search. [Nick Lawson](https://towardsdatascience.com/author/ccrngd1/) April 3, 202611 min read * [![](../images/006.jpg)](https://towardsdatascience.com/automate-sap-report-extraction-with-pyautogui-f115ae19b653/) ## [Automate SAP Report Extraction with Pyautogui](https://towardsdatascience.com/automate-sap-report-extraction-with-pyautogui-f115ae19b653/) [Data Engineering](https://towardsdatascience.com/category/data-science/data-engineering/) A guide to saving your time from the most tedious task in the world. [Pathairush Seeda](https://towardsdatascience.com/author/padpathairush/) April 17, 20218 min read * [![](../images/007.png)](https://towardsdatascience.com/should-an-engineer-learn-r-or-python-c91b36215a91/) ## [Should An Engineer Learn R or Python?](https://towardsdatascience.com/should-an-engineer-learn-r-or-python-c91b36215a91/) [Data Science](https://towardsdatascience.com/category/data-science/) An Honest And Actionable Review [Brock Taute](https://towardsdatascience.com/author/tautebro12/) February 18, 20217 min read * [![](../images/008.jpg)](https://towardsdatascience.com/a-common-mistake-to-avoid-when-working-with-time-series-data-eedf60a8b4c1/) ## [How NOT to Analyze Time Series](https://towardsdatascience.com/a-common-mistake-to-avoid-when-working-with-time-series-data-eedf60a8b4c1/) [Data Science](https://towardsdatascience.com/category/data-science/) One of the most common time series data mistakes I see junior data scientists and interview candidates make is to assume that the data has... [Yifei Huang](https://towardsdatascience.com/author/yifei-huang/) September 29, 20213 min read * [![](../images/009.png)](https://towardsdatascience.com/setting-a-dockerized-python-environment-the-elegant-way-f716ef85571d/) ## [Setting A Dockerized Python Environment - The Elegant Way](https://towardsdatascience.com/setting-a-dockerized-python-environment-the-elegant-way-f716ef85571d/) [Data Science](https://towardsdatascience.com/category/data-science/) This post provides a step-by-step guide for setting up a Python dockerized development environment with VScode and the Dev Containers... [Rami Krispin](https://towardsdatascience.com/author/rami.krispin/) April 2, 20249 min read * [YouTube](https://www.youtube.com/c/TowardsDataScience) * [X](https://x.com/TDataScience) * [LinkedIn](https://www.linkedin.com/company/towards-data-science/) * [Threads](https://www.threads.net/@towardsdatascience) * [Bluesky](https://bsky.app/profile/towardsdatascience.com) [](https://towardsdatascience.com/) Your home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. Subscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/) © Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network) #### Recommended Articles Close * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ "Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ "The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ "Does Your Company Have a Data Strategy?") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ "Productivity Tips, Data Career Insights, and Other Recent Must-Reads")