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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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Apache Yarn to Manage Resources a Solution

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Apache Hadoop is one of the most popular tools for big data processing. It has been successfully deployed in production by many companies for several years. 

Though Hadoop is considered a reliable, scalable, and cost-effective solution, it is constantly being improved by a large community of developers. As a result, the 2.0 version offers several revolutionary features, including Yet Another Resource Negotiator (YARN), HDFS Federation, and a highly available NameNode, which make the Hadoop cluster much more efficient, powerful, and reliable. 

Apache Yarn

Apache Hadoop 2.0 includes YARN, which separates the resource management and processing components. The YARN-based architecture is not constrained to MapReduce.
  • New developmens in Hadoop 2.0 Architecture with YARN: 
  • ResourceManager instead of a cluster manager 
  • ApplicationMaster instead of a dedicated and short-lived JobTracker 
  • NodeManager instead of TaskTracker 
  • A distributed application instead of a MapReduce job 

Basic changes in Hadoop 2.0 architecture

  • The ResourceManager, the NodeManager, and a container are not concerned about the type of application or task.
  • All application framework-specific code is simply moved to its ApplicationMaster so that any distributed framework can be supported by YARN — as long as someone implements an appropriate ApplicationMaster for it.
  • Thanks to this generic approach, the dream of a Hadoop YARN cluster running many various workloads comes true. Imagine: a single Hadoop cluster in your data center that can run MapReduce, Giraph, Storm, Spark, Tez/Impala, MPI, and more.

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