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Data Processing

2,100 USD
3 Days
Classroom Training, Online Training

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Prices reflect a 22.5% discount for IBM employees (wherever applicable).
Prices reflect a 24% discount for Kyndryl employees (wherever applicable).
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  • Date: 8-Apr-2024 to 10-Apr-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
  • Date: 3-Jun-2024 to 5-Jun-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,100 USD
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Course Description




Introduction to Data processing. Ingesting data from multiple sources. Batch Data Ingestion, Streaming Data Ingestion. Storing data in DataLake. Data Curation. Exploratory Data Analysis. Data Organization and Data Lineage.


Data Architects, Data Engineers, Data Scientists


    • Attendants should have knowledge in:
      • Spark fundamentals
      • Pyspark Fundamentals
      • Hadoop Fundamentals (listed below)
      • Kafka
      • HBase or Hive
      • Atlas


Part I. Data Science Concepts
  • Unit 1. Introduction to Data Processing
Data Processing pipeline. Data Quality. Data Ingestion. Data Curation. Exploratory Data Analysis. Data Organization and Lineage. Data Processing Policies. Part II. Data Ingestion
  • Unit 2. Introduction to Data Ingestion
Reading data from multiple sources. Raw data and Data Curation. Batch Processing. Stream Processing using Kafka. Storing data in Data Lake.
  • Unit 3. Batch Processing
Architecture and abstractions. Reading data from multiple sources. Data transformations. Storing data in a Data Lake.
  • Unit 4. Spark Streaming
Architecture and Abstractions, Transformations, Discretized Streams (DStreams), Input Dstreams and Receivers, Transformations on DStreams Lab included
  • Unit 5. Structured Streaming
Introduction to Structured Streaming, Operations on Streaming DataFrames and DataSets. Lab Included
  • Unit 6. Spark Streaming Integrations
Performance Tuning, pySpark Streaming, Kafka and Structured Streaming, Kafka and DStreams. Lab Included
  • Unit 7. Advanced Spark Streaming
Event Time Windows, Processing time Windows, Watermarking. Lab included
  • Unit 8. Advanced Structured Streaming
Structured Streaming fault tolerance and recovery. Structured Streaming Performance Tuning considerations. Monitoring Structured Streaming Lab included Part III. Exploratory Data analysis
  • Unit 9. Exploratory Data Analysis
What is Exploratory Data Analysis (EDA). EDA steps and implementation. Lab included
  • Unit 10. Exploratory Data Analysis on Structured Data Exploratory Data Analysis functions on Different types of data Lab included
  • Unit 11. Exploratory Data Analysis on UnStructured Data
Exploratory Data Analysis functions on Unstructured Data. Part IV. Data Organization
  • Unit 12. Data Lineage
Data Lineage. Using and applying data Lineage functions.
  • Unit 13. Data Organization
DataSet organization methodology. Data Set search.
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