Data Engineers

An important collection of the 15 best machine learning cheat sheets. 1- Supervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf 2- Unsupervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf 3- Deep Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf 4- Machine Learning Tips and Tricks https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf 5- Probabilities and Statistics https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf 6- Comprehensive Stanford Master Cheat Sheet https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf 7- Linear Algebra and Calculus https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf 8- Data Science Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf 9- Keras Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf 10- Deep Learning with Keras Cheat Sheet https://github.com/rstudio/cheatsheets/raw/master/keras.pdf 11- Visual Guide to Neural Network Infrastructures http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png 12- Skicit-Learn Python Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf 13- Scikit-learn Cheat Sheet: Choosing the Right Estimator https://scikit-learn.org/stable/tutorial/machine_learning_map/ 14- Tensorflow Cheat Sheet https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf 15- Machine Learning Test Cheat Sheet https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/ ENJOY LEARNING 👍👍

𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 Ever wondered how machines describe images in words?💻 Want to get hands-on with cutting-edge AI and computer vision — for FREE?🎊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/42FaT0Y 🎯 Start Learning AI for FREE

Data Analyst vs Data Engineer: Must-Know Differences Data Analyst: - Role: Focuses on analyzing, interpreting, and visualizing data to extract insights that inform business decisions. - Best For: Those who enjoy working directly with data to find patterns, trends, and actionable insights. - Key Responsibilities: - Collecting, cleaning, and organizing data. - Using tools like Excel, Power BI, Tableau, and SQL to analyze data. - Creating reports and dashboards to communicate insights to stakeholders. - Collaborating with business teams to provide data-driven recommendations. - Skills Required: - Strong analytical skills and proficiency with data visualization tools. - Expertise in SQL, Excel, and reporting tools. - Familiarity with statistical analysis and business intelligence. - Outcome: Data analysts focus on making sense of data to guide decision-making processes in business, marketing, finance, etc. Data Engineer: - Role: Focuses on designing, building, and maintaining the infrastructure that allows data to be stored, processed, and analyzed efficiently. - Best For: Those who enjoy working with the technical aspects of data management and creating the architecture that supports large-scale data analysis. - Key Responsibilities: - Building and managing databases, data warehouses, and data pipelines. - Developing and maintaining ETL (Extract, Transform, Load) processes to move data between systems. - Ensuring data quality, accessibility, and security. - Working with big data technologies like Hadoop, Spark, and cloud platforms (AWS, Azure, Google Cloud). - Skills Required: - Proficiency in programming languages like Python, Java, or Scala. - Expertise in database management and big data tools. - Strong understanding of data architecture and cloud technologies. - Outcome: Data engineers focus on creating the infrastructure and pipelines that allow data to flow efficiently into systems where it can be analyzed by data analysts or data scientists. Data analysts work with the data to extract insights and help make data-driven decisions, while data engineers build the systems and infrastructure that allow data to be stored, processed, and analyzed. Data analysts focus more on business outcomes, while data engineers are more involved with the technical foundation that supports data analysis. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

𝟱 𝗙𝗿𝗲𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗡𝗲𝗲𝗱𝗲𝗱!)😍 If you’re serious about starting your tech journey, Python is one of the best languages to master👨‍💻👨‍🎓 I’ve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects — absolutely FREE🔥 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4lOVqmb Start today, and you’ll thank yourself tomorrow.✅️

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍 Want to know how top companies handle massive amounts of data without losing track? 📊 TCS is offering a FREE beginner-friendly course on Master Data Management, and yes—it comes with a certificate! 🎓 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4jGFBw0 Just click and start learning!✅️

📖 Struggling with SQL commands

𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘😍 Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?👨‍💻 Here’s the truth: YouTube is packed with goldmine content, and the best part — it’s all 100% FREE🔥 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4cL3SyM 🚀 If You’re Serious About Data Analytics, You Can’t Sleep on These YouTube Channels!

Data Engineering Tools: Apache Hadoop 🗂️ – Distributed storage and processing for big data Apache Spark ⚡ – Fast, in-memory processing for large datasets Airflow 🦋 – Orchestrating complex data workflows Kafka 🐦 – Real-time data streaming and messaging ETL Tools (e.g., Talend, Fivetran) 🔄 – Extract, transform, and load data pipelines dbt 🔧 – Data transformation and analytics engineering Snowflake ❄️ – Cloud-based data warehousing Google BigQuery 📊 – Managed data warehouse for big data analysis Redshift 🔴 – Amazon’s scalable data warehouse MongoDB Atlas 🌿 – Fully-managed NoSQL database service React ❤️ for more

𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵! 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍 If you’re dreaming of starting a high-paying data career or switching into the booming tech industry, Google just made it a whole lot easier — and it’s completely FREE👨‍💻 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4cMx2h2 You’ll get access to hands-on labs, real datasets, and industry-grade training created directly by Google’s own experts💻

SNOWFLAKES AND DATABRICKS Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs? 🌐  𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 ❄️ 𝐍𝐚𝐭𝐮𝐫𝐞: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup. ❄️ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading. ❄️  Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility. ❄️  𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads. ❄️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools. 🌐 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 ❄️  𝐂𝐨𝐫𝐞: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently. ❄️ 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework. 🌐 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 ❄️ 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐍𝐞𝐞𝐝𝐬: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements. ❄️ 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing. ❄️ 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricks—with its schema-on-read technique—may be more advantageous. 🌐 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧: Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.

𝟰 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜😍 Dreaming of Mastering AI? 🎯 Harvard and Stanford—two of the most prestigious universities in the world—are offering FREE AI courses👨‍💻 No hidden fees, no long applications—just pure, world-class education, accessible to everyone🔥 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3GqHkau Here’s your golden ticket to the future!✅

Lets say you have 5 TB of data stored in your Amazon S3 bucket consisting of 500 million records and 100 columns. Now, suppose there are 100 cities and you want to get the data for a particular city, and you want to retrieve only 10 columns. ~ considering each city has equal amount of records, we want to get 1% of data in terms of number of rows and 10% in terms of columns thats roughly 0.1% of the actual data which might be 5 GB roughly. Now lets the pricing if you are using serverless technology like AWS Athena - the worst case you end up having the data in a csv format (row based) with no compression. you end up scanning the entire 5 TB data and you pay $25 for this query. (The charges are $5 for each TB of data scanned) Now lets try to improve it.. - use a columnar file format like parquet with snappy compression which takes lesser space so your 5 TB data might roughly become 2 TB (actually it will be even lesser) - partition this based on city so that we have 1 folder for each city. This way you have 2 TB data sitting across 100 folders, but you have to scan just one folder which is 20 GB, Not just this you need 10 columns out of 100 so roughly you scan 10% of 20 GB (as we are using columnar file format) This comes out to be 2 GB only. so how much do we pay? just $.01 which is 2500 times lesser than what you paid earlier. This is how you save cost. what we did? - using columnar file formats for column pruning - using partitioning for row pruning - using efficient compression techniques

𝟱 𝗙𝗥𝗘𝗘 𝗜𝗕𝗠 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍 From mastering Cloud Computing to diving into Deep Learning, Docker, Big Data, and IoT Blockchain IBM, one of the biggest tech companies, is offering 5 FREE courses that can seriously upgrade your resume and skills — without costing you anything. 𝗟𝗶𝗻𝗸:-👇 https://pdlink.in/44GsWoC Enroll For FREE & Get Certified ✅

Join our WhatsApp channel for more data engineering resources 👇👇 https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗙𝗹𝗼𝘄 From real-time streaming to batch processing, data lakes to warehouses, ETL to BI, etc this covers it all ! Simple Example: ◾ The project starts with data ingestion using APIs and batch processes to collect raw data. ◾ Apache Kafka enables real-time streaming, while ETL pipelines process and transform the data efficiently. ◾ Apache Airflow orchestrates workflows, ensuring seamless scheduling and automation. ◾ The processed data is stored in a Delta Lake with ACID transactions, maintaining reliability and governance. ◾ For analytics, the data is structured in a Data Warehouse (Snowflake, Redshift, or BigQuery) using optimized star schema modeling. ◾ SQL indexing and Parquet compression enhance performance. ◾ Apache Spark enables high-speed parallel computing for advanced transformations. ◾ BI tools provide insights, while DataOps with CI/CD automates deployments. 𝗟𝗲𝘁𝘀 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: - ETL + Data Pipelines = Data Flow Automation   - SQL + Indexing = Query Optimization   - Apache Airflow + DAGs = Workflow Orchestration   - Apache Kafka + Streaming = Real-Time Data   - Snowflake + Data Sharing = Cross-Platform Analytics   - Delta Lake + ACID Transactions = Reliable Data Storage   - Data Lake + Data Governance = Managed Data Assets   - Data Warehouse + BI Tools = Business Insights   - Apache Spark + Parallel Processing = High-Speed Computing   - Parquet + Compression = Optimized Storage   - Redshift + Spectrum = Querying External Data   - BigQuery + Serverless SQL = Scalable Analytics   - Data Engineering + Python = Automation & Scripting   - Batch Processing + Scheduling = Scalable Data Workflows   - DataOps + CI/CD = Automated Deployments   - Data Modeling + Star Schema = Optimized Analytics   - Metadata Management + Data Catalogs = Data Discovery   - Data Ingestion + API Calls = Seamless Data Flow   - Graph Databases + Neo4j = Relationship Analytics   - Data Masking + Privacy Compliance = Secure Data 

𝟲 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜😍 Power BI Isn’t Just a Tool—It’s a Career Game-Changer🚀 Whether you’re a student, a working professional, or switching careers, learning Power BI can set you apart in the competitive world of data analytics📊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3ELirpu Your Analytics Journey Starts Now✅️

⌨️ MongoDB Cheat Sheet

MongoDB is a flexible, document-orientated, NoSQL database program that can scale to any enterprise volume without compromising search performance.

This Post includes a MongoDB cheat sheet to make it easy for our followers to work with MongoDB. Working with databases Working with rows Working with Documents Querying data from documents Modifying data in documents Searching

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 Want to kickstart your career in Data Analytics but don’t know where to begin?👨‍💻 TCS has your back with a completely FREE course designed just for beginners✅ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4jNMoEg Just pure, job-ready learning📍