Asheesh ..’s Post

𝘼𝙯𝙪𝙧𝙚 𝙏𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙖𝙩𝙞𝙤𝙣𝙨 𝙈𝙖𝙠𝙚 𝙒𝙤𝙧𝙠𝙞𝙣𝙜 𝙬𝙞𝙩𝙝 𝘿𝙖𝙩𝙖 𝙀𝙖𝙨𝙮 👩🏻💻 These tools are part of Azure Data Factory and help you change and organize your data with just a few clicks 🖼️ Why are Azure transformations so amazing for mapping data flows ❓ bcz .... 🚫 𝙉𝙤 𝙘𝙤𝙙𝙞𝙣𝙜 𝙣𝙚𝙚𝙙𝙚𝙙 You can create and design your data changes without writing any code. This means fewer mistakes and more fun with your data! 🌲 𝙒𝙤𝙧𝙠𝙨 𝙬𝙚𝙡𝙡 𝙬𝙞𝙩𝙝 𝙗𝙞𝙜 𝙙𝙖𝙩𝙖 Azure transformations are perfect for Apache Spark clusters, so you can handle lots of data without spending a lot of money. 📂 𝙀𝙖𝙨𝙮 𝙩𝙤 𝙛𝙞𝙭 𝙞𝙨𝙨𝙪𝙚𝙨 Mapping data flows make it simple to find and fix any issues with your data, so everything runs smoothly. Hence, Azure transformations are super important and helpful for your project and your interviews as well ✅ Learn & Prepare for your next Spark / PySpark / Azure Data Engg. Interview with me ➡️ https://lnkd.in/dt5qchck follow Asheesh .. for more contents ❄️ #azuretransformation #azurecloud #azuredatafactory #adf #azure #azuredataengineer #interview #preparation #dataengineering #dataengineer

🚀 Day-23: AWS Cloud Computing for Data Engineering 🚀 Today, I tackled an ETL Pipeline project using AWS Data Analytics Services, including S3, Glue, Athena, and Redshift. Here’s what I learned: 🎯 Project Aim: To build an efficient, scalable ETL pipeline to extract, transform, and load data using AWS services. The project aimed to process raw data from S3, model it, and load it into a Redshift cluster for further analysis. 🔑 Key Challenge: Integrating multiple AWS services and managing large datasets posed a complexity in handling data transformation and schema modeling. 💡 How I Overcame It: 1️⃣ Glue Crawlers: I automated schema discovery from the raw data in S3. 2️⃣ Python Scripts: Used Python for data transformation and to build a Star Schema. 3️⃣ Athena: I leveraged SQL queries to analyze and validate the transformed data. 4️⃣ Redshift: Successfully loaded the final datasets into Redshift, enabling optimized data querying. This project enhanced my understanding of AWS data pipelines and solving real-world data engineering challenges! 💪💻 #AWS #CloudComputing #DataEngineering #ETL #AWSGlue #Athena #S3 #Redshift #DataAnalytics #TechSkills #TechLearning #CloudServices #TechCommunity #LearningJourney #Innovation

Roadmap to become Azure Data Engineer in 2024. ❇️𝗣𝗿𝗲-𝗿𝗲𝗾𝘂𝗶𝘀𝗶𝘁𝗲𝘀 - SQL Python ❇️𝗔𝘇𝘂𝗿𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 - Azure Data Factory Azure Databricks Azure Synapse Azure Functions Azure Storage Azure Data Lake Azure HDInsight 👉🏻𝗔𝘁 𝗹𝗲𝗮𝘀𝘁 𝟮 𝗘𝗻𝗱 𝘁𝗼 𝗘𝗻𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 👉🏻𝗔𝗧𝗦 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁 𝗥𝗲𝘀𝘂𝗺𝗲 👉🏻𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 #Azuredataengineer #dataengineering

I see there are many new comers has one question in mind -- how to get good hike or how to get a job into Data Engineering domain? I have said this multiple times let me tell you again 1. Learn SQL from Youtube - 10000 channels are available for free 2. Learn Python from Youtube - 100000 channels are available for free 3. Learn Databricks and Spark - Don't learn just basic - Check my course (reference) https://lnkd.in/daGsXPFb 4. Learn Cloud technologies - Azure and then ETL ADF 5. Understand Datalake and Lakehouse , Modelling,etc 6. Learn how to deploy pipelines and jobs using Azure Devops and Github 7. Build Projects which are more complex -- Don't fall in covid-19 similar kind project For projects, check out my 4 project course which helped me to get 100% hike https://lnkd.in/gFsWvvKM #databricks #azure #llm

Introduction to Big Data Processing with PySpark for AWS Glue #bigdata #aws

Data Engineering isn't going anywhere anytime soon! With the rising innovations in AI, the demand for quality data is also pretty high! To leverage various data opportunities in the market it's important to grab good hold of the following skillset: 1. Programming Languages - Python, Scala, Java 2. Relational Databases - SQL Server, MySQL, PostgreSQL 3. Data Modeling - Creating database blueprints like Designing star/snowflake schemas for analytics 4. Cloud Platforms - AWS, Azure, GCP for Deploying data pipelines, serverless computing 5. Big Data Technologies - Apache Spark, Kafka, Hadoop for data processing 6. Data Warehousing - Snowflake, Databricks, BigQuery 7. Data Streaming - Kafka, Spark Streaming 8. Data Management Architectures - Data Lakes, Data Meshes for Centralized data repositories, decentralized data governance 9. Data Quality & Governance - Ensuring data accuracy, consistency, and compliance 10. DevOps & DataOps - Automation of data engineering workflows for Continuous integration, automated testing To begin with you can focus on the following: - Start with Linux, MySQL, programming basics - Progress to distributed computing (Hadoop, Spark) - Explore streaming (Kafka), databases (SQL,Neo4j) - Learn cloud platforms - Develop fundamental DSA skills Do not miss the soft skills for good communication and collaboration to understand the business aspects of data engineering, such as project management and stakeholder engagement. To learn cloud, here are few amazing resources to leverage and upskill: Google Cloud- https://lnkd.in/gNwpp2RT Microsoft Azure Cloud - https://lnkd.in/gwc3By9h Amazon Web Services (AWS) - https://lnkd.in/gJeUGfjS #data #engineering #programming #sql #bigdata #cloud #innovation #growth

I enrolled in the "End to End Azure Data Engineering Project" course with high expectations, and I can confidently say that it exceeded them in every way. Whether you’re looking to advance your career as an Azure Data Engineer, prepare for interviews, or simply deepen your understanding of Azure data tools, this course is an invaluable resource. It not only equips you with technical skills but also prepares you to tackle real-world challenges confidently. I wholeheartedly recommend it to anyone serious about mastering Azure Data Engineering. This is the End to End Azure Data Engineering Project course that covers all the major tools and resources. You will be taught how to set up each resource till CI-CD pipelines like Azure Databricks, Azure DataFactory, Logic App, Keyvault, Automation Account, Autoloader, SQL Database, App Registration, Azure Blob Storage, Azure Gen2, Azure DevOps CI-CD Pipelines. This project covers most of the real-time scenarios of the End to End Project starting from the Analysis phase to the Reporting phase including CI CD Pipeline. For examples: ✅ Parameterization of the multiple ADF pipelines to reduce the cost of ADF and reduce dev work ✅ Data Quality framework of cleansed tables ✅ Send Failure/Success Alerts to End users using Python and Logic App ✅ Automate the process to send data files to end users ✅ Setup CI-CD pipelines for Azure Resources ✅ Cleanse data files using Autoloader in Databricks Prerequisites of this course: ✅ Understanding of ADF, Databricks ✅ Understanding of Python, SQL Azure Data Engineering Project | Announcement | Interview Preparation Course: https://lnkd.in/d_qdgAXQ Website: https://lnkd.in/dn9yQjXm Youtube Video: https://lnkd.in/dVK7X-aP HASTAGS TAGS hashtag #hashtags hashtag #tags hashtag #usajobs hashtag #apachespark hashtag #spark hashtag #pysparkforbeginners hashtag #hastag hashtag #hashtags hashtag #hashtag hashtag #tags hashtag #azuredataengineer hashtag #interviewpreparation hashtag #dataengineer

𝙏𝙤𝙥 5 𝘿𝙖𝙩𝙖 𝙀𝙣𝙜𝙞𝙣𝙚𝙚𝙧𝙞𝙣𝙜 𝙎𝙠𝙞𝙡𝙡𝙨 ❓ Getting started with Data Engineering can be challenging, but here are the key skills you'll need to focus on. 🌲 SQL: Learn the basics and how to use window functions , group by , subquery, joins , cte and more. recommend resources: https://lnkd.in/d4Nt7ehH 🌲 Python: Know the basics and some data structures like lists, maps, queues, and stacks. Learn OOPs concepts as well and some functional programming. recommended resources: https://lnkd.in/dYpqkuh3 🌲 Cloud & Distributed Computing: You should be familiar with tools like Spark, Snowflake or Databricks and a cloud like Azure or AWS based data engineering services. recommend resources: https://lnkd.in/dUbnSjba https://lnkd.in/dt5qchck 🌲 Data Quality: Learn how to validate a data set to ensure it's high quality by checking for duplicates, unusual distributions, NULLs, etc. SQL skills are very useful here! 🌲 Job Orchestration: Automate your data processes so they run automatically. Tools like Airflow or Databricks can help you get started. recommend resources: https://lnkd.in/dt5qchck follow Asheesh . for more contents ❄️ #aws #azure #dataengineer #dataengineering #azuredataengineering

AWS Glue vs AWS EMR? Which would you prefer for your use case? Recently, I was working on a batch processing project and I was in a dilemma to choose between AWS Glue or AWS EMR services. I'm basically ingesting data from an API at regular intervals and want to perform the ingestions and processing for different regions simultaneously. To make a decision, first we need to understand what these services offer: 🔎 AWS Glue is a serverless ETL tool that can be used to perform ad-hoc tasks. It also has a Crawler to identify data schema automatically and store it in a Data catalog. You only need to specify the ETL logic using Glue Studio and the Python/ Scala orchestration code would be automatically created. ⚒ AWS EMR(Elastic MapReduce) is a managed big data processing tool that can help with Presto SQL querying, Spark real time and batch processing, Hive data warehousing and Tensorflow machine learning by leveraging all the functionalities of a Hadoop ecosystem. It also provides a fully managed cluster infrastructure that is auto scalable based on workload demands. Both these services use Apache Spark for parallel processing and can be for used for ETL tasks. But to decide between the two we can consider the following: ✅ When to use AWS Glue: 1. Need for easy integration with Glue services and small workloads 2. Data processing focus and not infrastructure 3. Require automatic logging and monitoring with Cloudwatch ✅ When to use AWS EMR: 1. Need for more control and config flexibility 2. Separate clusters for different operations and access to performance tuning 3. Real time processing where clusters need to be up all time and autoscalable 💡 For my use case, I chose AWS Glue because the workloads were not too heavy and the overhead of designing the cluster architecture was not feasible. Also, I needed the Crawler facilities to understand the underlying data features and perform the transformation logic accordingly. You can use both of these tools together for several use cases as well. ➕ Please add your suggestions in the comments based on your experiences with these tools? #awsglue #awsemr #dataengineering #dataarchitecture #batchprocessing #spark

I’ve been diving deep into Amazon Glue, AWS’s serverless ETL service, and here’s why it’s a game-changer for data engineering: Why Use Amazon Glue? Amazon Glue simplifies preparing and loading data for analytics by automating much of the traditional ETL effort. It enables efficient data integration, ensuring data is ready for analysis and machine learning. Key Features I’ve Learned: 1. Serverless ETL: Focus on ETL logic without worrying about infrastructure management. 2. Data Catalog: Automatically discovers and maintains metadata, making data organization seamless. 3. Schema Discovery: Simplifies data preparation by automatically detecting and classifying data. 4. Flexible Job Authoring: Create ETL jobs using Glue Studio, Jupyter notebooks, or custom Python/Scala scripts. 5. AWS Integration: Seamlessly integrates with S3, Redshift, RDS, Athena, and more for efficient data processing. 6. Apache Spark Support: Leverage Spark for powerful, scalable data processing. 7. Data Cleaning: Built-in transforms for cleaning and normalizing data, reducing preparation time. 8. Developer Endpoints: Allows interactive testing and debugging of ETL scripts. Applications: 1. Building and managing data lakes 2. Loading data into data warehouses 3. Preparing data for real-time analytics 4. Preprocessing data for machine learning #DataEngineering #AWS #AmazonGlue #ETL #CloudComputing #DataIntegration #BigData #MachineLearning #DataLakes #RealTimeAnalytics #LearningJourney #TechSkills