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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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MongoDB and HCL on Bigdata

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MongoDB and HCL Infosystems have announced a global partnership which will allow HCL Infosystems to further broaden its solution offerings in the emerging big data segment by developing services around MongoDB.

Commenting on the partnership, Kamal Brar, Vice President APAC, MongoDB, said: "Big Data is more than just addressing increasing volume; organizations need solutions that can manage increasing data variety and velocity. Today, businesses need to quickly ingest, store, and access useful information from massive pools of multi-structured data.This is where MongoDB excels. HCL Infosystems will offer innovative solutions and services for organizations to gain value from their data and make smarter and faster business decisions. Partnering with HCL will further strengthen our footprint globally."

Since its inception MongoDB has collaborated with organizations worldwide and now has a thriving global community. Common MongoDB use cases include single view, Internet o ..

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