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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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Ideas: How Bigadata Helps HR Teams

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Big Data is the buzzword of the year. Every leader — whether they’re managing a small team or are at the helm of a multinational corporation with thousands of employees — is wondering how they can use Big Data to better get to know their people, to create a setting that better suits their needs and, in turn, drive recruitment and retention.

As co-authors of The Decoded Company: Know Your Talent Better Than You Know Your Customers, we’ve spent a lot of time thinking about this exact topic. Here are the top five trends you should be thinking about.

  1.  We are living in a data-abundant environment, and it’s changing everything. Gary Hamel, one of the world’s leading thinkers on the topic of management, has written extensively on the topic of the technology of leadership (or what he more accurately calls the technology of human accomplishment).
  2. He believes — and we tend to agree — that this might be the most important technology humanity has ever created. It gives us extraordinary superpowers to organize people into achieving feats that would be otherwise impossible, particularly from an economic perspective. Consider, for example, that Apple has achieved a market cap of $468.99B with 80,300 full-time employees (from its 2013 Annual report), or almost $6m per head.
  3. The challenge is that the management tools we use every day were designed around the assumption that data is expensive to gather and therefore infrequently available. Today’s reality is very different.
  4. Data is abundant and incredibly cheap to gather, store, process, and analyze. This epic shift has led to radically different business models on one hand, but only incremental management philosophy tinkering on the other.
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