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Data Engineering

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Data Engineering

Data Engineering

Duration
45 Hours

Course Description

        Data engineering focuses on building and maintaining the systems that collect, store, process, and prepare data for analysis. It's the foundation upon which data science and business intelligence rely, ensuring data is accessible, reliable, and usable for various stakeholders. Data engineers design and implement data pipelines, manage data storage solutions, and ensure data quality, enabling data-driven decision-making. 

Course Outline For Data Engineering

Big Data Fundamentals:

  • What is big data and why it's important.
  • Characteristics of big data (volume, velocity, variety, veracity).
  • Different types of big data analytics (batch, real-time).

Data EngineeringPrinciples:

  • Data ingestion, storage, transformation, and extraction.
  • Data warehousing and data lakes.
  • Data pipelines and ETL (Extract, Transform, Load) processes.

Cloud Computing:

  • Introduction to cloud platforms (AWS, Azure, GCP).
  • Understanding cloud services and their relevance to data engineering. 

Databricks and Apache Spark:

Databricks:

  • Introduction to Databricks platform and its functionalities.
  • Databricks notebooks and clusters.
  • Databricks SQL and Delta Lake.
  • Databricks Jobs and workflows.

ApacheSpark:

  • Spark architecture and core concepts (RDDs, DataFrames, Datasets).
  • Spark SQL API and Spark Streaming.
  • Spark MLlib (machine learning library).
  • Spark GraphX (graph processing).

PySpark:

  • Introduction to PySpark and its integration with Python.
  • PySpark DataFrames and transformations.
  • PySpark SQL and Spark Streaming with Python. 

Hadoop Ecosystem:

HDFS (Hadoop DistributedFile System):

  • Understanding HDFS architecture and its role in data storage.
  • HDFS commands and operations.

MapReduce:

  • Introduction to MapReduce paradigm and its use in data processing.
  • MapReduce programming with Python.

YARN(Yet Another Resource Negotiator):

  • Understanding YARN architecture and its role in resource management.

Other Hadoop Components:

  • Sqoop (data import/export).
  • Flume (data ingestion).
  • Kafka (messaging system). 

Hands-on Exercises and Projects:

Real-worlddata engineering projects:

  • Building data pipelines using Databricks and PySpark.
  • Performing data analysis and transformation with Spark SQL.
  • Developing machine learning models using Spark MLlib.

Case studies:

  • Analyzing real-world datasets using Databricks and PySpark.
  • Implementing data warehousing solutions with Databricks and Delta Lake. 

Data Governanceand Security:

  • Data quality and data lineage.
  • Data security and access control.

Data Visualization:

  • Using tools like Tableau or Power BI to visualize data.

Cloud DataEngineering Best Practices:

  • Optimizing data pipelines for cloud environments.
  • Cost optimization and resource management. 
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