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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to Understand AWS CloudFormation Easily

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AWS CloudFormation is a service that helps you model and set up your Amazon Web Services resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS.

You create a template that describes all the AWS resources you want (like Amazon EC2 instances or Amazon RDS DB instances), and AWS CloudFormation provides and configures those resources for you.

cloud formation

 

You don't need to individually create and configure AWS resources and figure out what's dependent on what; AWS CloudFormation handles all of that. 

Managing Infrastructure

  • For a scalable web application that also includes a back-end database, you might use an Auto Scaling group, an Elastic Load Balancing load balancer, and an Amazon Relational Database Service database instance. 
  • Normally, you might use each individual service to provide these resources. And after you create the resources, you would have to configure them to work together. All these tasks can add complexity and time before you even get your application up and running. 
  • Instead, you can create or modify an existing AWS CloudFormation template. A template describes all of your resources and their properties. 
  • When you use that template to create an AWS CloudFormation stack, AWS CloudFormation provisions the Auto Scaling group, load balancer, and database for you. After the stack has been created, your AWS resources are up and running. You can delete the stack quickly. which deletes all the resources in the stack. By using AWS CloudFormation, you easily manage a collection of resources as a single unit.

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