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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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Differences: AWS Vs Other Cloud models

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The first key difference between AWS and other IT models is flexibility. Using traditional models to deliver IT solutions often requires large investments in new architectures, programming languages, and operating systems.
comparison of cloud services

Why AWS is Superior

Although these investments are valuable, the time that it takes to adapt to new technologies can also slow down your business and prevent you from quickly responding to changing markets and opportunities.

When the opportunity to innovate arises, you want to be able to move quickly and not always have to support legacy infrastructure and applications or deal with protracted procurement processes.

You May Also Like: Cloud computing certification course

Flexibility

In contrast, the flexibility of AWS allows you to keep the programming models, languages, and operating systems that you are already using or choose others that are better suited for their project.

Easy to Learn

You don’t have to learn new skills. Flexibility means that migrating legacy applications to the cloud is easy and cost-effective. Instead of re-writing applications, you can easily move them to the AWS cloud and tap into advanced computing capabilities.

Support

Building applications on AWS are very much like building applications using existing hardware resources. Since AWS provides a flexible, virtual IT infrastructure, you can use the services together as a platform or separately for specific needs. AWS run almost anything—from full web applications to batch processing to offsite data back-ups.

Integration

In addition, you can move existing SOA-based solutions to the cloud by migrating discrete components of legacy applications. 

Typically, these components benefit from high availability and scalability, or they are self-contained applications with few internal dependencies. Larger organizations typically run in a hybrid mode where pieces of the application run in their data center and other portions run in the cloud.

Summary

Once these organizations gain experience with the cloud, they begin transitioning more of their projects to the cloud, and they begin to appreciate many of the benefits outlined in this document. Ultimately, many organizations see the unique advantages of the cloud and AWS and make it a permanent part of their IT mix

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