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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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IBM PML Vs Google MapReduce why you need to read

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IBM Parallel Machine Learning Toolbox (PML) is similar to that of Google's MapReduce programming model (Dean and Ghemawat, 2004) and the open source Hadoop system,which is to provide Application Programming Interfaces (APIs) that enable programmers who have no prior experience in parallel and distributed systems to nevertheless implement parallel algorithms with relative ease. Google MapReduce Vs IBM PML Like MapReduce and Hadoop, PML supports associative-commutative computations as its primary parallelization mechanism .  Unlike MapReduce and Hadoop, PML fundamentally assumes that learning algorithms can be iterative in nature, requiring multiple passes over data. The ability to maintain the state of each worker node between iterations, making it possible, for example, to partition and distribute data structures across workers Efficient distribution of data, including the ability of each worker to read a subset of the data, to sample the data, or to scan the entire data...
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