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𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐒𝐭𝐚𝐠𝐞 𝐢𝐧 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 ? A stage specifies where data files are stored (i.e. “staged”) so that the data in the files can be loaded into a table. Stage is a location of files, that can be internal or external to snowflake Stage Types 1.   External Stages 2.   Internal Stages 1.   User Internal Stage 2.   Table Internal Stage 3.   Named Internal Stage We will discuss about external stage 𝐄𝐱𝐭𝐞𝐫𝐧𝐚𝐥 𝐒𝐭𝐚𝐠𝐞𝐬 An external stage is the external cloud storage location where data files are stored. Loading data from any of the following cloud storage services is supported regardless of the cloud platform that hosts your Snowflake account: Amazon S3 Google Cloud Storage Microsoft Azure   •       An external stage is a database object created in a schema. •       This object stores the URL to files in cloud storage. •       Also stores the settings used to access the cloud storage account (Storage Integration Object). •       Create stages using the CREATE STAGE command.    example:        CREATE STAGE my_s3_stage  URL='s3://mybucket/path/'  STORAGE_INTEGRATION = my_s3_integration  CREDENTIALS=(AWS_KEY_ID='your_aws_key_id' AWS_SECRET_KEY='your_aws_secret_key')  FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"' COMPRESSION = 'GZIP'); #dataengineer #dataengineering #python #snowflake #datawarehouse #cloud #aws

Hello guys what's up??🫠 I hope things are going well for you all🍻 It's been a while since I posted about a new #project but that day is here finally😎. I would like to present to you a new #dataengineering project all thanks to Darshil Parmar. The outline of the project is shared in the image below. In this project I - 1. Learnt Data Modeling 2. Revised our most loved pandas library😍 2. Learnt the basics of Mage Data Engineering Tool 3. Revised my networking concepts in GCP VMs 4. Revised SQL 5. Did useful Dashboarding😂 Github - https://lnkd.in/eXBZvGHG Overall it was a fun project wherein I went from raw data to SQL analytics to dashboard. Stay tuned!!!!, a new project of my own hand is going to be made public soon🤔 Note - I will be releasing a modified version of this project wherein instead of using Mage on VM I will try utilizing Pyspark on Google Cloud's Dataproc to perform the transformations and automatic triggering of the Dataproc pipeline whenever new data is uploaded to Google Cloud Storage. This will automate the pipeline in contrast to how it is kinda manual in the project🤔. Feel free to share your thoughts below👇

Let's look at this robust architecture which is using Airflow on GCP and Google Cloud Dataproc to streamline data workflows 👇🏻 🔍 Architecture Breakdown 👇🏻 1️⃣ Extract Phase: ✅ Data Upload: Securely upload on-premises data to Google Cloud Storage (GCS). ✅ Trigger DAG: Initiate the process with an Airflow Directed Acyclic Graph (DAG) triggered via an Identity-Aware Proxy. 2️⃣ Transform Phase: ✅ Create Cluster: Spin up an ephemeral Dataproc cluster tailored for scalable data processing. ✅ Submit Spark Job: Leverage Spark for efficient data transformation within the Dataproc cluster. ✅ Temporary Storage: Store the processed data in temporary Cloud Storage, preparing it for the next stage. 3️⃣ Load Phase: ✅ Load to BigQuery: Seamlessly load the transformed data into BigQuery for advanced analysis. ✅ Cleanup: Optimize resources by destroying the ephemeral cluster and removing temporary data from GCS. 🔧 Key Benefits 👇🏻 ✅ Automation: Streamline your workflow with automated orchestration via Airflow. ✅ Scalability: Harness the power of Dataproc for dynamic, scalable data processing. ✅ Efficiency: Achieve seamless integration with GCP services for effective data management. 🚨 My most affordable and industry oriented "Complete Data Engineering 3.0 With Azure" Bootcamp is live now and ADMISSIONS ARE OPEN 🔥 This will cover Airflow & GCP Dataproc in details too 👉 Enroll Here (Limited Seats): https://lnkd.in/gajKNhie 🔗 Code "DE300" for my Linkedin connections 🚀 Live Classes Starting on 1-June-2024 📲 Call/WhatsApp on this number for career counselling and any query +91 9893181542 Cheers - Grow Data Skills 🙂 #dataengineering #gcp #bigdata #datapipelines

Hello Folks!!! Today, I will be sharing how you could use the PySpark to read data from a Json File on MultiLine parameters. And to read a file in json and csv or avro data format type, you can use the below Pyspark code: df = spark.read.csv("URL or Directory"), if you want to define the schema and header then use: df = spark.read.csv|json|avro("URL",header=True, Inferschema=True). Then when you execute the query, you can now use the df.show() to display the out of the Query. Do you know that a Select statement could be used as well to read data from a single Column, multiple Columns etc: df.select("Column1", "Column2").show() - execute the query on the DataBricks and it would output just the two selected Columns. I will share below a screenshot of the dataframe used to read data in the JSON format as a MultiLine. I am a lover of data and enjoy using services in Amazon Web Services (AWS), Microsoft Azure (Azure) and Google Cloud Platform (GCP) to perform data transformation and analytics. #DataEngineer #DataBaseAdministrator #DataAnalyst #D365FOEngineer

Did you know? 🤔 You can like 👍 this post while learning something new! 🚀 Learning AWS through Projects – Day 5 📅 Nested JSON Data File Analysis using AWS Glue DataBrew & QuickSight 🛠️ Project: Analyze and visualize nested JSON data using AWS services. Steps: 1️⃣ Upload nested JSON data to an S3 bucket’s input folder. 2️⃣ Create an AWS Glue DataBrew project and import the nested data. 3️⃣ Unnest the JSON using DataBrew recipes, making the data easier to work with. 4️⃣ Export the cleaned and transformed data to the S3 bucket’s output folder. 5️⃣ Visualize the final dataset using Amazon QuickSight for insights and reporting. Key Services: S3, AWS Glue DataBrew, Amazon QuickSight 📊 Breakdown of the Process: Data Ingestion via S3 🗂️: The nested JSON file is uploaded to the input folder in an S3 bucket. This is where the raw data is stored before processing. Data Transformation with AWS Glue DataBrew 🔄: A DataBrew project is created to handle the data transformation. Using the recipe feature, we unnest the JSON, making it easier to analyze by flattening complex structures. Export Clean Data to S3 🚀: After transformation, the clean data is exported back to an S3 output folder, ready for visualization. Visualization with QuickSight 🔍: The cleaned dataset is imported into Amazon QuickSight, where it’s visualized using charts and dashboards to gain insights. This process simplifies complex data using AWS Glue DataBrew and makes it ready for analysis using QuickSight. Perfect for turning complex nested data into actionable insights! 🚀 Amazon Web Services (AWS) #AWS #DataEngineering #DataBrew #S3 #QuickSight #DataTransformation #JSONAnalysis

Leveraging Spark with Google Cloud: A Seamless Data Engineering Integration 🌐 Today's progression in my Data Engineering #dezoomcamp course introduced me to the power of integrating Apache Spark with Google Cloud Storage and BigQuery. The ability to directly access and manipulate data stored in GCS buckets using Spark has opened up new avenues for efficient data processing and analytics. What caught my attention was the utilization of clusters in the cloud, enabling the execution of Python code directly within Google's robust infrastructure. Whether through the web interface or command line, running Spark jobs has never been more accessible or scalable. The highlight? Discovering Spark's capability to connect directly with BigQuery, facilitating the seamless generation and management of tables for in-depth data analysis. This integration not only simplifies the workflow but also enhances the potential for uncovering valuable insights from vast datasets. Embracing these cloud-based technologies marks a significant leap forward in my journey as a data engineer. The possibilities for innovation and optimization in data processing are boundless. #DataEngineering #ApacheSpark #GoogleCloudStorage #BigQuery #CloudComputing #ProfessionalDevelopment

🚀 Exciting Update! 🎉 I’m thrilled to share that I've completed a key module in Data Engineering: Big Data on AWS Cloud - Athena & EC2. Here's a quick dive into my learning journey: 🔍 Working with Athena & S3: Leveraged AWS Athena to query data stored in S3 buckets, creating tables with metadata stored in AWS Glue Catalog. This allowed me to execute SQL queries directly on data in S3. 📊 💡 Optimizing Data Queries: Initially, querying CSV files in S3 with Athena resulted in scanning entire files. To optimize, I used Databricks to convert the CSV files to Parquet format, which is columnar and more efficient for querying. This significantly reduced the data scanned by Athena queries, improving performance. 🚀 🔧 Apache Spark Integration: Explored Athena’s new support for Apache Spark, a serverless service that allows complex data processing using PySpark within Athena workgroups. This added flexibility for handling intricate data tasks using Spark's robust engine. 🔄 🛠️ Glue Crawler: Utilized AWS Glue Crawler to automatically infer the schema of data stored in S3, streamlining the process of creating accurate data tables. 🧩 🤖 Serverless Advantage: Athena’s serverless nature means no cluster management; charges are based on data scanned (SQL Trino engine) or compute resources used (Apache Spark engine). Ideal for scheduled tasks where efficiency and cost-effectiveness are key. ⏰ 💻 Hands-on with EC2: Gained practical experience in creating and managing EC2 instances through both the AWS Management Console and AWS CLI, enhancing my cloud infrastructure skills. 🌐 This module has been a fantastic blend of theory and practical application, and I’m excited to leverage these skills in real-world scenarios! 🌍 🙏 A big shoutout to Sumit Mittal for creating such an insightful course. #DataEngineering #BigData #AWS #Athena #EC2 #Serverless #Databricks #S3 #Glue #Parquet #SQL #Trino #Spark #LearningJourney #TechSkills

BigQuery organizes data tables into units called datasets. These datasets are scoped to your GCP project. These multiple scopes—project, dataset, and table - helps you structure your information logically. You can use multiple datasets to separate tables pertaining to different analytical domains, and you can use project-level scoping to isolate datasets from each other according to your business needs. https://lnkd.in/eWdEhtBd

Good morning LinkedIn fam! Excited to share my latest writing with you all! Just dropped Part 1 of my guide on fast-tracking your Azure skills through hands-on , specifically tailored for data engineers. If you're keen to level up your Azure game, this guide is your sauce! Check it out here: https://lnkd.in/gFy2ntFW Can't wait to hear your thoughts and experiences! Let's dive into Azure together! #Azure #DataEngineering #HandsOnLearning

Hurray, Leveling up my cloud game! working with GCP has given me more insights into data engineering practices. I’m excited to share I’ve earned my Build a Data Warehouse with BigQuery Skill Badge #GoogleCloudLearning #GoogleCloudSkillBadge