Shravan Kumar’s Post

Hi connections, To secure a job in data engineering, you need a combination of technical skills, Cloud knowledge and domain knowledge. This can be summarized below : Programming Languages: Python: Widely used for scripting and automation. SQL: Essential for querying databases. Data Warehousing: Knowledge of data warehousing solutions like Amazon Redshift, Google BigQuery, Snowflake, or traditional databases like Oracle and SQL Server. Big Data Technologies: Apache Hadoop: For distributed storage and processing. Apache Spark: For large-scale data processing. Cloud Platforms: Familiarity with cloud services like AWS, Google Cloud Platform (GCP), or Microsoft Azure, especially their data services (e.g., AWS S3, GCP BigQuery, Azure Data Lake). Data Modeling, Lakes and Warehouses: Understanding of data modeling concepts and techniques for designing efficient databases. Experience with setting up and managing data lakes and data warehouses. Version Control and DevOps: Proficiency with version control systems like Git. Familiarity with DevOps practices and tools for continuous integration and deployment. #bigdata #dataengineering #dataanalyst

Here are some key skills that should add for a Data Engineer role: 1. Programming Languages: Python SQL Java or Scala Shell Scripting 2. Data Pipelines and ETL: Building and maintaining ETL (Extract, Transform, Load) processes Data Wrangling and Cleansing Batch and Real-Time Data Processing tools (e.g., Apache Spark, Flink) 3. Databases & Data Warehousing: Relational Databases: MySQL, PostgreSQL NoSQL Databases: MongoDB, Cassandra Data Warehousing: Snowflake, Amazon Redshift, Google BigQuery 4. Cloud Platforms: AWS (Amazon Web Services), Azure, Google Cloud Platform (GCP) Cloud-based storage systems: S3, Azure Blob Storage, Google Cloud Storage 5. Big Data Technologies: Hadoop Ecosystem Apache Kafka for real-time data streaming Apache Spark for distributed computing 6. Data Modeling & Architecture: Designing scalable data models and data warehouses Understanding of Data Lakes #dataengineer #learning #growth #datascience #career #python #sql

#Single hashtag #round hashtag #Interview with our hashtag #Direct hashtag #Client Role: Cloud Data architect Location: Mt Laurel, NJ Mode of work: Hybrid (3 days onsite) Duration: FTE/W2 Experience 12 to 13yrs on Domain: Banking Primary Skills - Azure, Databricks, Pyspark, Python Secondary Skills - AWs, Google Cloud Platform, Devops (Good to have), Data Modeling techniques (DV2.0) Interested consultants can share your resume to Email: karthik@zensoftit.com #Note:We also do direct profile marketing on any technology related to IT with Quick placements hashtag #dataarchitect hashtag #datapipeline hashtag #datagovernance hashtag #datainfrastructure hashtag #azurr hashtag #aws hashtag #dataquality hashtag #sql hashtag #hadoop hashtag #python hashtag #java hashtag #DataArchitecture hashtag hashtag #CloudDataArchitect

🔥#dataengineering is a fast growing field and you can get a desired hike but you need to follow 👇 Some tips that can help a data engineer advance in their career:🦾 Master Core Concepts: Ensure a strong foundation in core concepts of data engineering such as databases, data warehousing, data modeling, ETL (Extract, Transform, Load) processes, and data pipelines. Programming Skills: Become proficient in programming languages commonly used in data engineering such as Python, SQL, and possibly Scala or Java. Also, be familiar with relevant libraries and frameworks like Apache Spark, Apache Kafka, and Airflow. Cloud Platform Proficiency: Acquire expertise in cloud platforms like AWS, Google Cloud Platform (GCP), or Microsoft Azure. Understand services like AWS S3, EC2, Redshift, GCP BigQuery, Dataflow, or Azure Data Lake, and how to leverage them for data engineering tasks. Big Data Technologies: Stay updated with big data technologies such as Hadoop, Spark, Kafka, and HBase. Understanding distributed computing and how to optimize data processing for large-scale datasets is crucial. Data Modeling: Develop skills in designing effective data models that cater to specific business requirements. Understand various modeling techniques like dimensional modeling, normalization, and denormalization. #azuredataengineer #azuredatafactory #dataengineering #dataengineer #bigdata #bigdataengineer #dataanalytics #dataanalyst

‼️ Microsoft is hiring for Data Engineers ‼️ Link to apply : https://lnkd.in/gTER8eg4 Follow Nitesh Chaudhry for more data-focused content !! 🙋🏻 Edit : The Opening seems to have closed now. #data #dataEngineering #dataScience 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐢𝐬 The Apache Software Foundation's Druid 𝐭𝐨𝐨𝐥 : In short, it's a special database/ warehouse that allows super-fast queries for analytics. Druid is a column-oriented database designed for real-time analytics on large datasets, offering fast queries and efficient storage for time-series data. It can be useful when you want fast results of queries on your data, no matter it comes from Kafka or Spark !! 𝐃𝐫𝐮𝐢𝐝 𝐯𝐬 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧 : 𝐃𝐫𝐮𝐢𝐝 𝐯𝐬. 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲: 🌟 Pros of Druid: Real-time analytics for immediate insights. Optimized for time-series data with low-latency queries. 💻 Pros of BigQuery: Serverless data warehousing without infrastructure management. Seamless integration with Google Cloud Platform services. 𝐃𝐫𝐮𝐢𝐝 𝐯𝐬. 𝐇𝐢𝐯𝐞: 🚀 Pros of Druid: Real-time analytics for immediate insights. Low-latency querying optimized for time-series data. 🔍 Pros of Hive: Full SQL support for complex ad-hoc queries. Integration with Hadoop ecosystem for batch processing. 𝐃𝐫𝐮𝐢𝐝 𝐯𝐬. 𝐑𝐞𝐝𝐬𝐡𝐢𝐟𝐭: 🌟Pros of Druid: Real-time analytics for immediate insights. Low-latency querying optimized for time-series data. 📈Pros of Redshift: Fully managed data warehousing service. Petabyte-scale analytics with integration with AWS services.

🚀 Top Interview Questions for Data Engineers! Are you preparing for a Data Engineer role or looking to assess candidates? Here are some high-level questions I recently encountered evaluate technical skills, problem-solving abilities, and cloud experience in Data Engineering: How do you design and optimize end-to-end data pipelines for scalability and efficiency? What strategies do you use to ensure data quality in large-scale ETL processes? How do you handle distributed data systems across cloud platforms like AWS, GCP, or Azure? What’s your approach to building and optimizing data models for analytics and reporting? What challenges have you faced with big data tools like Spark, Hadoop, or Kafka? How did you overcome them? How do you ensure data security and governance in your pipelines? Can you share a time when you solved a complex data pipeline issue in production? What was your approach? What’s your experience with data migration to modern cloud-based platforms? How do you implement monitoring and logging in your data pipelines? How do you stay updated on new trends and tools in Data Engineering, and how do you incorporate them into your workflow? #DataEngineering #TechInterviews #BigData #CloudData #DataPipelines #ETL #SQL #AWS #Azure #GCP #DataArchitect

if you are a perfect #dataengineer you might have these....... 1. Well versed basics(Hadoop, Distributed systems, Big data) 2. Architectures(Hadoop, spark, hive, databases) 3. Advanced SQL 4. Fluent Pyspark 5. In depth apache spark, hive. 6. Good Cloud Experience(Azure, AWS, GCP) 7. Experience of orchestration(Airflow) 8. little streaming exposure(Kafka) 9. strong System designing skills, data modelling, data warehousing skills. CI/CD skills. this is the basic things a data engineer should have to even appear for #interviews too... do you have them all?

To secure a job as a data engineer, the following five essential skills are highly valuable: 1. Proficiency in SQL and Database Management: - Mastery of SQL for querying and manipulating data. - Understanding of database management systems (DBMS) such as MySQL, PostgreSQL, Oracle, and NoSQL databases like MongoDB and Cassandra. 2. Experience with ETL Tools and Data Integration: - Knowledge of ETL (Extract, Transform, Load) tools like Apache NiFi, Talend, and Azure Data Factory. - Ability to design and implement data pipelines for data integration and transformation. 3. Programming Skills: - Proficiency in programming languages such as Python, Java, or Scala for data processing. - Familiarity with frameworks like Apache Spark for large-scale data processing. 4. Knowledge of Cloud Platforms: - Experience with cloud services from providers like AWS, Azure, or Google Cloud Platform (GCP). - Understanding of services like Amazon S3, Azure Blob Storage, Google BigQuery, and cloud-based data warehouses. 5. Data Warehousing and Big Data Technologies: - Understanding of data warehousing concepts and tools like Amazon Redshift, Snowflake, and Google BigQuery. - Familiarity with big data technologies such as Hadoop, Hive, and Kafka for managing and processing large datasets. #dataengineer #sql #programmingskills #Bigdata #skillsfordataengineer

New Data Engineer for Hire 💻 I'm Haren, a seasoned Data Governance Engineer with over 4 years of hands-on experience in the IT industry. My expertise is in setting up Data Quality standards establishing industrial Data Governance Policies and harnessing the power of Big Data technologies such as Spark, MapReduce, Hive, Kafka, and HDFS to drive impactful insights from massive datasets. I am proficient in Java, Scala, and Python, I specialize in constructing resilient data pipelines and executing large-scale data transformations. With a deep understanding of Hadoop architecture and its components like HDFS, Yarn, and MapReduce. Furthermore, my familiarity with cloud platforms like AWS helps with optimal data management solutions. I'm looking for new opportunities at this moment and I would love to connect and share my experiences. #hiring #DataGovernance #dataengineer #dataengineering #bigdata #dataarchitecture #etl #datapipeline #datawarehousing #dataintegration #cloudcomputing #databasemanagement #sql #python #java #scala #hadoop #spark #dataengineer #datacenterengineer #databaseengineer #dataengineering  #dataengineers #dataengineerjobs #bigdataengineer #azuredataengineer  #databaseengineering #jbdataengineering #dataengineerindonesia #dataengineeringlatam #dataengineeringjobs #datascienceengineering  #bigdataengineering

🚀 𝗙𝗿𝗲𝗲 𝟱-𝗠𝗼𝗻𝘁𝗵 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗥𝗼𝗮𝗱𝗺𝗮𝗽! 🚀 Looking to build a career in data engineering? This 5-month roadmap is packed with essential topics and free resources to help you master the skills needed to become a successful data engineer. 📝 𝐓𝐨𝐩𝐢𝐜𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: 🌀Introduction to Data Engineering 🌀SQL for Data Engineers 🌀Python for Data Engineering 🌀Data Warehousing & ETL 🌀Big Data Tools (Hadoop, Spark) 🌀Data Pipelines 🌀Cloud Platforms (AWS, GCP) 🌀Data Modeling & Architecture 🌀Real-world Project Implementation 🎯 𝘿𝙤𝙬𝙣𝙡𝙤𝙖𝙙 𝙩𝙝𝙚 𝙧𝙤𝙖𝙙𝙢𝙖𝙥 𝙛𝙤𝙧 𝙁𝙍𝙀𝙀 𝙝𝙚𝙧𝙚: https://lnkd.in/gg_zfhuQ Let’s grow together in this exciting field! 🚀 PDF Credit : Anurag Srivastava 👉 𝐒𝐚𝐯𝐞 𝐚𝐧𝐝 𝐑𝐞𝐩𝐨𝐬𝐭 🔁 👉 𝐅𝐨𝐥𝐥𝐨𝐰 ( Harish Malviya ) 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 🚀 #DataEngineering #CareerGrowth #FreeResources #LearningJourney