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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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Top requirements for successful MapReduce jobs

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The following techniques are needed to be successful of your map reduce jobs:
  • The mapper must be able to ingest the input and process the input record, sending forward the records that can be passed to the reduce task or to the final output directly, if no reduce step is required.
Mapreduce Jobs in Hadoop
Hadoop-MapReduce
  • The reducer must be able to accept the key and value groups that passed through the mapper, and generate the final output of this MapReduce step.
  • The job must be configured with the location and type of the input data, the mapper class to use, the number of reduce tasks required, and the reducer class and I/O types.
  • The TaskTracker service will actually run your map and reduce tasks, and the JobTracker service will distribute the tasks and their input split to the various trackers.
  • The cluster must be configured with the nodes that will run the TaskTrackers, and with the number of TaskTrackers to run per node. The TaskTrackers need to be configured with the JVM parameters, including the classpath for both the TaskTracker and the JVMs that will execute the individual tasks.
  • There are three levels of configuration to address to configure MapReduce on your cluster. From the bottom up, you need to configure the machines, the Hadoop MapReduce framework, and the jobs themselves.
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