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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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RDBMS Vs Key-value Four Top Differences

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This post tells you differences between rdbms and distributed key-value storage.

Rdbms is quite  different from key-value storage.

RDBMS Vs Key-value Four Top Differences

RDBMS (Relational Database)

  1. You have already used a relational database management system — a storage product that's commonly referred to as RDBMS. 
  2. It is basically a structured data.
  3. RDBMS systems are fantastically useful to handle moderate data.
  4. The BIG challenge is in scaling beyond a single server. 
  5. You can't maintain redundant data in rdbms.
  6. All the data available on single server.
  7. The entire database runs on single server. So when server is down then database may not be available to normal business operations.
  8. Outages and server downs are common in this rdbms model of database.

Key-Value Database

  1. Key-value storage systems often make use of redundancy within hardware resources to prevent outages. This concept is important when you're running thousands of servers because they're bound to suffer hardware breakdowns. 
  2. Multiple copies same data available on multiple servers.
  3. The use of redundancy makes the key-value system always available — and, more importantly, your data is always available because it's protected from hardware outages.
  4. Literally, dozens of key-value storage products are available. Many of them were first developed by so-called webscale companies, such as Facebook and LinkedIn, to ensure that they can handle massive amounts of traffic. 
  5. Currently key-value storages under open source licenses are available. Now you (or anyone else) can use them in other environments too.

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