Data Engineers

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗨𝗻𝗹𝗼𝗰𝗸 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀!😍 Top 3 Free YouTube Playlists to Learn SQL 1)SQL Tutorial Videos 2)SQL Mastery: From Basics to Advanced 3)Learn Complete SQL (Beginner to Advanced) 𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/4hFyseX Enroll For FREE & Get Certified🎓

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍 1) Introduction to Cyber Security 2) AWS Cloud Masterclass 3)Salesforce Developer Catalyst 4) Python Basics 5) Project Management Basics 𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/4jQJfo5 Enroll For FREE & Get Certified🎓

Complete Python topics required for the Data Engineer role: ➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻: - Python Syntax - Data Types - Lists - Tuples - Dictionaries - Sets - Variables - Operators - Control Structures: - if-elif-else - Loops - Break & Continue try-except block - Functions - Modules & Packages ➤ 𝗣𝗮𝗻𝗱𝗮𝘀: - What is Pandas & imports? - Pandas Data Structures (Series, DataFrame, Index) - Working with DataFrames: -> Creating DFs -> Accessing Data in DFs Filtering & Selecting Data -> Adding & Removing Columns -> Merging & Joining in DFs -> Grouping and Aggregating Data -> Pivot Tables - Input/Output Operations with Pandas: -> Reading & Writing CSV Files -> Reading & Writing Excel Files -> Reading & Writing SQL Databases -> Reading & Writing JSON Files -> Reading & Writing - Text & Binary Files ➤ 𝗡𝘂𝗺𝗽𝘆: - What is NumPy & imports? - NumPy Arrays - NumPy Array Operations: - Creating Arrays - Accessing Array Elements - Slicing & Indexing - Reshaping, Combining & Arrays - Arithmetic Operations - Broadcasting - Mathematical Functions - Statistical Functions ➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗡𝘂𝗺𝗽𝘆 are more than enough for Data Engineer role. All the best 👍👍

𝟱 𝗠𝘂𝘀𝘁-𝗗𝗼 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀!😍 If you’re aiming for a Data Analyst, Business Analyst, or Data Scientist role, mastering SQL is non-negotiable. 📊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4aUoeER Don’t just learn SQL—apply it with real-world projects!✅️

ChatGPT Prompt to learn any skill 👇👇 I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information (Tap on above text to copy)

𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗕𝘆 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍 - JP Morgan  - Accenture - Walmart - Tata Group - Accenture 𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/3WTGGI8 Enroll For FREE & Get Certified🎓

Here’s a detailed breakdown of critical roles and their associated responsibilities: 🔘 Data Engineer: Tailored for Data Enthusiasts 1. Data Ingestion: Acquire proficiency in data handling techniques. 2. Data Validation: Master the art of data quality assurance. 3. Data Cleansing: Learn advanced data cleaning methodologies. 4. Data Standardisation: Grasp the principles of data formatting. 5. Data Curation: Efficiently organise and manage datasets. 🔘 Data Scientist: Suited for Analytical Minds 6. Feature Extraction: Hone your skills in identifying data patterns. 7. Feature Selection: Master techniques for efficient feature selection. 8. Model Exploration: Dive into the realm of model selection methodologies. 🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts 9. Coding Proficiency: Develop robust programming skills. 10. Model Training: Understand the intricacies of model training. 11. Model Validation: Explore various model validation techniques. 12. Model Evaluation: Master the art of evaluating model performance. 13. Model Refinement: Refine and improve candidate models. 14. Model Selection: Learn to choose the most suitable model for a given task. 🔘 ML Engineer: Tailored for Deployment Enthusiasts 15. Model Packaging: Acquire knowledge of essential packaging techniques. 16. Model Registration: Master the process of model tracking and registration. 17. Model Containerisation: Understand the principles of containerisation. 18. Model Deployment: Explore strategies for effective model deployment. These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.

𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁!😍 Want to break into Data Analytics but don’t know where to start? Follow this step-by-step roadmap to build real-world skills! ✅ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3CHqZg7 🎯 Start today & build a strong career in Data Analytics! 🚀

Learn This Concept to be proficient in PySpark. 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - PySpark Architecture - SparkContext and SparkSession - RDDs (Resilient Distributed Datasets) - DataFrames - Transformations and Actions - Lazy Evaluation 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀: - Creating DataFrames - Reading Data from CSV, JSON, Parquet - DataFrame Operations - Filtering, Selecting, and Aggregating Data - Joins and Merging DataFrames - Working with Null Values 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗖𝗼𝗹𝘂𝗺𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: - Defining and Using UDFs (User Defined Functions) - Column Operations (Select, Rename, Drop) - Handling Complex Data Types (Array, Map) - Working with Dates and Timestamps 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: - Understanding Partitions - Repartitioning and Coalescing - Managing Shuffle Operations - Optimizing Partition Sizes for Performance 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮: - When to Cache or Persist - Memory vs Disk Caching - Checking Storage Levels 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗪𝗶𝘁𝗵 𝗦𝗤𝗟: - Spark SQL Introduction - Creating Temp Views - Running SQL Queries - Optimizing SQL Queries with Catalyst Optimizer - Working with Hive Tables in PySpark 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Data Cleaning and Preparation - Handling Missing Values - Data Normalization and Transformation - Working with Categorical Data 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Broadcasting Variables - Accumulators - PySpark Window Functions - PySpark with Machine Learning (MLlib) - Working with Streaming Data (Spark Streaming) 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Understanding Job, Stage, and Task - Tungsten Execution Engine - Memory Management and Garbage Collection - Tuning Parallelism - Using Spark UI for Performance Monitoring Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C All the best 👍👍

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻 𝟮𝟬𝟮𝟱😍 Master industry-standard tools like Excel, SQL, Tableau, and more. Gain hands-on experience through real-world projects designed to mimic professional challenges 𝗟𝗶𝗻𝗸👇 :-  https://pdlink.in/4jxUW2K All The Best 🎉

Planning for Data Science or Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. SQL Interview Resources: https://topmate.io/analyst/864764 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Join for more: https://t.me/datasciencefun ENJOY LEARNING 👍👍

Here's what the average data engineering interview looks like: - 1 hour algorithms in Python Here you will be asked irrelevant questions about dynamic programming, linked lists, and inverting trees - 1 hour SQL Here you will be asked niche questions about recursive CTEs that you've used once in your ten year career - 1 hour data architecture Here you will be asked about CAP theorem, lambda vs kappa, and a bunch of other things that ChatGPT probably could answer in a heartbeat - 1 hour behavioral Here you will be asked about how to play nicely with your coworkers. This is the most relevant interview in my opinion - 1 hour project deep dive Here you will be asked to make up a story about something you did or did not do in the past that was a technical marvel - 4 hour take home assignment Here you will be asked to build their entire data engineering stack from scratch over a weekend because why hire data engineers when you can submit them to tests? Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C All the best 👍👍

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐒𝐩𝐚𝐫𝐤 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1. Data Processing Optimization: How would you optimize a Spark job that processes 1 TB of data daily to reduce execution time and cost? 2. Handling Skewed Data: In a Spark job, one partition is taking significantly longer to process due to skewed data. How would you handle this situation? 3. Streaming Data Pipeline: Describe how you would set up a real-time data pipeline using Spark Structured Streaming to process and analyze clickstream data from a website. 4. Fault Tolerance: How does Spark handle node failures during a job, and what strategies would you use to ensure data processing continues smoothly? 5. Data Join Strategies: You need to join two large datasets in Spark, but you encounter memory issues. What strategies would you employ to handle this? 6. Checkpointing: Explain the role of checkpointing in Spark Streaming and how you would implement it in a real-time application. 7. Stateful Processing: Describe a scenario where you would use stateful processing in Spark Streaming and how you would implement it. 8. Performance Tuning: What are the key parameters you would tune in Spark to improve the performance of a real-time analytics application? 9. Window Operations: How would you use window operations in Spark Streaming to compute rolling averages over a sliding window of events? 10. Handling Late Data: In a Spark Streaming job, how would you handle late-arriving data to ensure accurate results? 11. Integration with Kafka: Describe how you would integrate Spark Streaming with Apache Kafka to process real-time data streams. 12. Backpressure Handling: How does Spark handle backpressure in a streaming application, and what configurations can you use to manage it? 13. Data Deduplication: How would you implement data deduplication in a Spark Streaming job to ensure unique records? 14. Cluster Resource Management: How would you manage cluster resources effectively to run multiple concurrent Spark jobs without contention? 15. Real-Time ETL: Explain how you would design a real-time ETL pipeline using Spark to ingest, transform, and load data into a data warehouse. 16. Handling Large Files: You have a #Spark job that needs to process very large files (e.g., 100 GB). How would you optimize the job to handle such files efficiently? 17. Monitoring and Debugging: What tools and techniques would you use to monitor and debug a Spark job running in production? 18. Delta Lake: How would you use Delta Lake with Spark to manage real-time data lakes and ensure data consistency? 19. Partitioning Strategy: How you would design an effective partitioning strategy for a large dataset. 20. Data Serialization: What serialization formats would you use in Spark for real-time data processing, and why? Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180 All the best 👍👍

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿? Here is a complete week-by-week roadmap that can help 𝗪𝗲𝗲𝗸 𝟭: Learn programming - Python for data manipulation, and Java for big data frameworks. 𝗪𝗲𝗲𝗸 𝟮-𝟯: Understand database concepts and databases like MongoDB. 𝗪𝗲𝗲𝗸 𝟰-𝟲: Start with data warehousing (ETL), Big Data (Hadoop) and Data pipelines (Apache AirFlow) 𝗪𝗲𝗲𝗸 𝟲-𝟴: Go for advanced topics like cloud computing and containerization (Docker). 𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬: Participate in Kaggle competitions, build projects and develop communication skills. 𝗪𝗲𝗲𝗸 𝟭𝟭: Create your resume, optimize your profiles on job portals, seek referrals and apply. Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180 All the best 👍👍

Preparing for a Spark Interview? Here are 20 Key Differences You Should Know! 1️⃣ Repartition vs. Coalesce: Repartition changes the number of partitions, while coalesce reduces partitions without full shuffle. 2️⃣ Sort By vs. Order By: Sort By sorts data within each partition and may result in partially ordered final results if multiple reducers are used. Order By guarantees total order across all partitions in the final output. 3️⃣ RDD vs. Datasets vs. DataFrames: RDDs are the basic abstraction, Datasets add type safety, and DataFrames optimize for structured data. 4️⃣ Broadcast Join vs. Shuffle Join vs. Sort Merge Join: Broadcast Join is for small tables, Shuffle Join redistributes data, and Sort Merge Join sorts data before joining. 5️⃣ Spark Session vs. Spark Context: Spark Session is the entry point in Spark 2.0+, combining functionality of Spark Context and SQL Context. 6️⃣ Executor vs. Executor Core: Executor runs tasks and manages data storage, while Executor Core handles task execution. 7️⃣ DAG vs. Lineage: DAG (Directed Acyclic Graph) is the execution plan, while Lineage tracks the RDD lineage for fault tolerance. 8️⃣ Transformation vs. Action: Transformation creates RDD/Dataset/DataFrame, while Action triggers execution and returns results to driver. 9️⃣ Narrow Transformation vs. Wide Transformation: Narrow operates on single partition, while Wide involves shuffling across partitions. 🔟 Lazy Evaluation vs. Eager Evaluation: Spark delays execution until action is called (Lazy), optimizing performance. 1️⃣1️⃣ Window Functions vs. Group By: Window Functions compute over a range of rows, while Group By aggregates data into summary. 1️⃣2️⃣ Partitioning vs. Bucketing: Partitioning divides data into logical units, while Bucketing organizes data into equal-sized buckets. 1️⃣3️⃣ Avro vs. Parquet vs. ORC: Avro is row-based with schema, Parquet and ORC are columnar formats optimized for query speed. 1️⃣4️⃣ Client Mode vs. Cluster Mode: Client runs driver in client process, while Cluster deploys driver to the cluster. 1️⃣5️⃣ Serialization vs. Deserialization: Serialization converts data to byte stream, while Deserialization reconstructs data from byte stream. 1️⃣6️⃣ DAG Scheduler vs. Task Scheduler: DAG Scheduler divides job into stages, while Task Scheduler assigns tasks to workers. 1️⃣7️⃣ Accumulators vs. Broadcast Variables: Accumulators aggregate values from workers to driver, Broadcast Variables efficiently broadcast read-only variables. 1️⃣8️⃣ Cache vs. Persist: Cache stores RDD/Dataset/DataFrame in memory, Persist allows choosing storage level (memory, disk, etc.). 1️⃣9️⃣ Internal Table vs. External Table: Internal managed by Spark, External managed externally (e.g., Hive). 2️⃣0️⃣ Executor vs. Driver: Executor runs tasks on worker nodes, Driver manages job execution. Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180 All the best 👍👍

Complete topics & subtopics of #SQL for Data Engineer role:- 𝟭. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗦𝘆𝗻𝘁𝗮𝘅: SQL keywords Data types Operators SQL statements (SELECT, INSERT, UPDATE, DELETE) 𝟮. 𝗗𝗮𝘁𝗮 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗗𝗟): CREATE TABLE ALTER TABLE DROP TABLE Truncate table 𝟯. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗠𝗟): SELECT statement (SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING, JOINs) INSERT statement UPDATE statement DELETE statement 𝟰. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: SUM, AVG, COUNT, MIN, MAX GROUP BY clause HAVING clause 𝟱. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: Primary Key Foreign Key Unique NOT NULL CHECK 𝟲. 𝗝𝗼𝗶𝗻𝘀: INNER JOIN LEFT JOIN RIGHT JOIN FULL OUTER JOIN Self Join Cross Join 𝟳. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀: Types of subqueries (scalar, column, row, table) Nested subqueries Correlated subqueries 𝟴. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: String functions (CONCAT, LENGTH, SUBSTRING, REPLACE, UPPER, LOWER) Date and time functions (DATE, TIME, TIMESTAMP, DATEPART, DATEADD) Numeric functions (ROUND, CEILING, FLOOR, ABS, MOD) Conditional functions (CASE, COALESCE, NULLIF) 𝟵. 𝗩𝗶𝗲𝘄𝘀: Creating views Modifying views Dropping views 𝟭𝟬. 𝗜𝗻𝗱𝗲𝘅𝗲𝘀: Creating indexes Using indexes for query optimization 𝟭𝟭. 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀: ACID properties Transaction management (BEGIN, COMMIT, ROLLBACK, SAVEPOINT) Transaction isolation levels 𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Data integrity constraints (referential integrity, entity integrity) GRANT and REVOKE statements (granting and revoking permissions) Database security best practices 𝟭𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Creating stored procedures Executing stored procedures Creating functions Using functions in queries 𝟭𝟰. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Query optimization techniques (using indexes, optimizing joins, reducing subqueries) Performance tuning best practices 𝟭𝟱. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: Recursive queries Pivot and unpivot operations Window functions (Row_number, rank, dense_rank, lead & lag) CTEs (Common Table Expressions) Dynamic SQL Here you can find quick SQL Revision Notes👇 https://topmate.io/analyst/864817 Like for more Hope it helps :)

Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career. 📊Introduction to Data Engineering ✅Overview of Data Engineering & its importance ✅Key responsibilities & skills of a Data Engineer ✅Difference between Data Engineer, Data Scientist & Data Analyst ✅Data Engineering tools & technologies 📊Programming for Data Engineering ✅Python ✅SQL ✅Java/Scala ✅Shell scripting 📊Database System & Data Modeling ✅Relational Databases: design, normalization & indexing ✅NoSQL Databases: key-value stores, document stores, column-family stores & graph database ✅Data Modeling: conceptual, logical & physical data model ✅Database Management Systems & their administration 📊Data Warehousing and ETL Processes ✅Data Warehousing concepts: OLAP vs. OLTP, star schema & snowflake schema ✅ETL: designing, developing & managing ETL processe ✅Tools & technologies: Apache Airflow, Talend, Informatica, AWS Glue ✅Data lakes & modern data warehousing solution 📊Big Data Technologies ✅Hadoop ecosystem: HDFS, MapReduce, YARN ✅Apache Spark: core concepts, RDDs, DataFrames & SparkSQL ✅Kafka and real-time data processing ✅Data storage solutions: HBase, Cassandra, Amazon S3 📊Cloud Platforms & Services ✅Introduction to cloud platforms: AWS, Google Cloud Platform, Microsoft Azure ✅Cloud data services: Amazon Redshift, Google BigQuery, Azure Data Lake ✅Data storage & management on the cloud ✅Serverless computing & its applications in data engineering 📊Data Pipeline Orchestration ✅Workflow orchestration: Apache Airflow, Luigi, Prefect ✅Building & scheduling data pipelines ✅Monitoring & troubleshooting data pipelines ✅Ensuring data quality & consistency 📊Data Integration & API Development ✅Data integration techniques & best practices ✅API development: RESTful APIs, GraphQL ✅Tools for API development: Flask, FastAPI, Django ✅Consuming APIs & data from external sources 📊Data Governance & Security ✅Data governance frameworks & policies ✅Data security best practices ✅Compliance with data protection regulations ✅Implementing data auditing & lineage 📊Performance Optimization & Troubleshooting ✅Query optimization techniques ✅Database tuning & indexing ✅Managing & scaling data infrastructure ✅Troubleshooting common data engineering issues 📊Project Management & Collaboration ✅Agile methodologies & best practices ✅Version control systems: Git & GitHub ✅Collaboration tools: Jira, Confluence, Slack ✅Documentation & reporting Resources for Data Engineering 1️⃣Python: https://t.me/pythonanalyst 2️⃣SQL: https://t.me/sqlanalyst 3️⃣Excel: https://t.me/excel_analyst 4️⃣Free DE Courses: https://t.me/free4unow_backup/569 Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180 All the best 👍👍

10 Data Engineering Projects to build your portfolio. 1. Olympic Data Analytics using Azure https://lnkd.in/gHNyz_Bg 2. Uber Data Analytics using GCP. https://lnkd.in/gqE-Y4HS 3. Stock Market Real-time Data Analysis using Kafka https://lnkd.in/gknh7ZEr 4. Twitter Data Pipeline using Airflow https://lnkd.in/g7YPnH7G 5. Smart City End to End project using AWS https://lnkd.in/gh2eWF66 6. Realtime Data Streaming using spark and Kafka https://lnkd.in/gjH2efgz 7. Zillow Data Analytics - Python, ETL https://lnkd.in/gvEVZHPR 8. End to end Azure Project https://lnkd.in/gCVZtNB5 9. End to end project using snowlake https://lnkd.in/g96n6NbA 10. Data pipeline using Data Fusion https://lnkd.in/gR5pkeRw Data Engineering Interview Preparation Resources: 👇 https://topmate.io/analyst/910180 Hope this helps you 😊 If you've read so far, do LIKE the post👍

Complete Data Engineering Roadmap to keep yourself in the hunt in job market. 1. I will Learn SQL --variables, data types, Aggregate functions -- Various joins, data analysis -- data wrangling, operators like(union, intersect etc.) --Advanced SQL(Regex, Having, PIVOT) --Windowing functions, CTE --finally performance optimizations. 2. I will learn Python... -- Basic functions, constructors, Lists, Tuples, Dictionaries -- Loops (IF, When, FOR), functional programming -- Libraries like(Pandas, Numpy, scikit-learn etc) 3. Learn distributed computing... --Hadoop versions/hadoop architecture --fault tolerance in hadoop --Read/understand about Mapreduce processing. --learn optimizations used in mapreduce etc. 4. Learn data ingestion tools... --Learn Sqoop/ Kafka/NIFi --Understand their functionality and job running mechanism. 5. i ll Learn data processing/NOSQL.... --Spark architecture/ RDD/Dataframes/datasets. --lazy evaluation, DAGs/ Lineage graph/optimization techniques --YARN utilization/ spark streaming etc. 6. Learn data warehousing..... --Understand how HIve store and process the data --different File formats/ compression Techniques. --partitioning/ Bucketing. --different UDF's available in Hive. --SCD concepts. --Ex Hbase. cassandra 7. Learn job Orchestration... --Learn Airflow/Oozie --learn about workflow/ CRON etc. 8. Learn Cloud Computing.... --Learn Azure/AWS/ GCP. --understand the significance of Cloud in #dataengineering --Learn Azure synapse/Redshift/Big query --Learn Ingestion tools/pipeline tools like ADF etc. 9. Learn basics of CI/ CD and Linux commands.... --Read about Kubernetes/Docker. And how crucial they are in data. --Learn about basic commands like copy data/export in Linux. Data Engineering Interview Preparation Resources: 👇 https://topmate.io/analyst/910180 Like if you need similar content 😄👍 Hope this helps you 😊