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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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Hadoop: How to Improve College a Mini Project

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This is based on my research of developing an engineering college using data analytics. This is a great subject that can be applied by all engineering aspirants in their final project. In my view it has dual benefits.

The one is for student they can gain lot of analytics knowledge and application to develop engineering college to keep it in the list of top colleges. The second is for Engineering colleges they can benefit to improve quality of education and to become one of the top colleges.

Hadoop: How to Improve College a Mini Project

Hadoop: How to Improve College a Mini Project



The project theme is data analytics:

There are total 2 parts:
  1. Use Hadoop technologies to study student database what they did in School level- This gives lot of insights on the Student interests. Approach each student and get some innovative ideas to improve the college
  2. Use Faculty database to get the skills and projects what they did in previous years. This helps to get right faculty for new innovative project
Basically the qualities of good engineering college you can be classified based on the below criteria.
  1. Infrastructure
  2. Lab facilities
  3. Transport facilities
  4. Practical oriented study
  5. Connection to industry
If the students find in their Hadoop project the improvements needed, then this can be showcase to industry. So that it improves industry connections. This is not only to one branch. This can be applied to any branch.

The areas where data analytics can be applied based on my research are

  • Good infrastructure
  • Best educational environment
  • Latest technologies used by the college
  • A better placement cell
  • Academic reputation of college
  • No. of accreditation college have
  • Placement percentage
  • No. of merit students
  • and so on....

So, the above are the key areas you can improve engineering college to get top rank.

The technologies for data analytics.
  1. Hadoop Platform
  2. Data base
  3. NoSQL database
  4. Presentation tool
Once you get the week areas, you will get a Chance to improve your college. So trail and error will take lot of time and it was possible in olden days.

Now a days you need technology and Tools. This is also a mutual benefit project.

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