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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...

Here are 5 Skills You need to Become SAS Data Analyst

Want to know what will happen in the future? Find the most lucrative opportunities? Get insights into impending outcomes? No problem. With our SAS data mining software, you can:

SAS Data Analyst.

Simplify data preparation. Interact with your data quickly and intuitively using dynamic charts and graphs to understand key relationships.

Quickly and easily create better models. Take the guesswork out of building models that are both stable and accurate using proven techniques and a drag-and-drop interface that's both easy-to-use and powerful.

sas data analysts skills


Put your best models into service. Fast. Spend less time and effort scoring new data using automated, interactive processes that work in both batch and real-time environments.

The requirement varies from company to company. I am giving here the basic skills you need for a SAS data analyst

  1. Experience in SAS or R analytics
  2. Scripting languages of Python/JavaScript/VB Script
  3. SQL and PL/SQL
  4. Databases knowledge in Oracle, DB2, SQL Server
  5. Hadoop and Big data Knowledge

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