The P/E ratio SUCKS.
It’s a flawed metric that deceives investors.
Here's exactly why the P/E ratio can be INCREDIBLY misleading (and what to use instead):
Feminine women don’t like feminine men.
Here are 12 things feminine men do that you should NOT do
Ps. Most of us are still doing it.
1. Crossing Your Legs While Sitting
Becoming an Azure Data Engineer (Quick Guide) 📊
Microsoft Azure provides a plethora of services, but as a Data Engineer, you'll only need to master a select few that are essential for your data workflows.
🔍 Azure Services for Data Engineers:
✅ Azure Blob Storage:
Object storage on Azure is where you'll store all your raw data—everything from text files to large video files, making it the foundation for your data operations.
✅ Azure Data Lake Storage (ADLS):
Azure has a service, especially for building Data Lake, Highly scalable and secure data lake functionality built on Azure Blob Storage. It is optimized for large-scale analytics workloads.
✅ Azure Data Factory:
Need to build ETL (Extract, Transform, Load) processes? Azure Data Factory is a serverless integration service that lets you create data-driven workflows for orchestrating and automating data movement and data transformation.
✅ Azure Synapse Analytics:
Once your data is processed, where should it go? Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics.
✅ Azure HDInsight:
For those who are already using Hadoop or Spark on-premises and are considering a move to the cloud, HDInsight offers a fully managed cloud service that makes it easy to handle massive amounts of data.
✅ Azure Functions:
If you need to run small pieces of code triggered by events, Azure Functions is your go-to. This serverless compute service helps you run event-triggered code without having to explicitly provision or manage infrastructure.
✅ Azure Databricks:
A powerful platform for big data analytics, Azure Databricks provides a collaborative Apache Spark-based environment that integrates seamlessly with other Azure services.
✅ Azure Stream Analytics:
For real-time data processing needs, Azure Stream Analytics is perfect. It allows you to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, websites, social media feeds, and other data streams.
✅ Azure Data Migration Service:
If you're tasked with moving databases to the cloud, Azure Data Migration Service simplifies the process. It's a fully managed service that streamlines the migration of your data to Azure data services.
✅ Azure Cosmos DB:
A globally distributed, multi-model database service. Excellent for adding real-time operational analytics to your applications.
✅ Azure Data Fabric:
For managing and securing data across various sources, Azure Data Fabric provides a unified data layer that ensures consistent data access and management across your enterprise.
Did I miss anything? Let me know 👇🏻
Steve Jobs once said:
“You can’t win on innovation unless you have a way to communicate it to customers.”
Here are 10 strategies to talk to anyone about anything:
AWS Data Engineering Project (500k+ views) 🚀
Learn important AWS Services such as Glue, Athena, Lambda, Redshift, S3, IAM, EC2, etc... by doing these amazing projects
1. YouTube Data Analysis (End-To-End Data Engineering Project) - https://t.co/VoWVgzb614
What will you learn?
✅ Python and PySpark
✅ SQL
✅ How to understand the business problem
✅ AWS Services - Athena, Glue, Redshift, S3, IAM
✅ Building Data Pipeline and Scheduling it
2. Spotify Data Pipeline using AWS (BEST PROJECT)-
https://t.co/m5GWodDr7C
👉🏻 Phase 1: In the Python for DE course we used simple Python to build this entire pipeline using AWS Lambda, S3, Glue Crawler, Athena
👉🏻 Phase 2: In the Data Warehouse for the DE course we replaced the load part with Snowpipe and Snowflake for analytics
👉🏻 Phase 3: In Apache Spark for DE course we are going to replace Lambda Part for transformation using AWS Glue ETL and write our pipeline using Apache Spark.
✅ Extract Data From API
✅ Python, SQL
✅ AWS Lambda, S3, Athena, Glue
✅ Snowpipe & Snowflake
✅ Apache Spark with Glue ETL
✅ Build Quality Data Engineering Project
3. Building Data Model and Writing ETL Job -
https://t.co/bUS6GWY2Ix
Data modeling is an essential part of Data Engineering, DO NOT SKIP THIS!!!
What will you learn?
✅ Python
✅ SQL
✅ Building Data Models
✅ Basics of DBMS
✅ Writing ETL Job
✅ Querying Data Programmatically
✅ PostgreSQL
4. ETL Pipeline on AWS Cloud -
https://t.co/ksYQpEMMgK
If you have no idea about Cloud Computing then this project is for you
What will you learn?
✅ Python
✅ SQL
✅ Cloud Computing Basics
✅ AWS Services - Athena, Glue, Redshift, S3, IAM
✅ Creating a Data Pipeline
5. Covid Data Analysis Project -
https://t.co/HfPG08uuga
This will be your first end-to-end Data Engineering project on Covid-19 Data
What will you learn?
✅ Python
✅ SQL
✅ Building Data Model
✅ AWS Services - Athena, Glue, Redshift, S3, IAM
✅ Creating a Data Pipeline
✅ PostgreSQL