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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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5 Key Services on AWS Cloud computing

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Amazon web services offers 5 services on its cloud computing. Top AWS Services Store Files Store Files:  AWS provides highly scalable, reliable, secure, fast storage for your digital files.  Host a Website: AWS provides many ways to host your website in order to suit the needs of large-scale enterprises,startups, and individuals. Database Run a Database: Adds or removes virtual servers to handle peaks and lulls in traffic. Requires CloudWatch.Distributes traffic among multiple virtual servers. Deployment Deploy an Application:   AWS offers application management services that help you build, deploy, and scale applications.You can use an application management service to leverage other AWS services without having to manage each of them separately and manually. Net Work Create a Network: You can create virtual private clouds on AWS. These can be isolated from the Internet, or you can add a public-facing subnet that makes part of your network acces...
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