Course Content
Prerequisites for a Data Engineering
Preparing for a Data Engineering boot-camp can enhance your experience and success. Here are the core prerequisites:
0/2
Preparing for a Data Engineering boot-camp
00:00
Roadmap to become Data Engineer
00:00
How is Data Engineer different from other Roles
0/2
The Difference
00:00
Data engineer and Data architect same or different???
00:00
Data Ingestion, Storage & Processing
Introduction to Data Engineering Overview of Data Engineering in modern architectures. Data lifecycle and pipelines. Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming). Activity: Discuss a real-world data pipeline use case.
0/5
Overview of Data Engineering in modern architectures.
00:00
Data lifecycle and pipelines
00:00
Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming).
00:00
Activity: Discuss a real-world data pipeline use case.
00:00
Assignment 1
Data Ingestion Techniques
Understanding structured, semi-structured, and unstructured data. Batch ingestion: Using Apache Sqoop, Talend. Streaming ingestion: Using Apache Kafka.
0/5
Understanding structured, semi-structured, and unstructured data.
00:00
Batch vs Stream
00:00
Batch ingestion: Using Apache Sqoop, Talend.
00:00
Streaming ingestion: Using Apache Kafka.
00:00
Activity: Set up a simple data ingestion pipeline using Kafka.
00:00
Data Storage Solutions
Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra). Cloud-based data storage (AWS S3, Azure Blob Storage). Choosing the right storage based on use cases.
0/4
Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra).
00:00
Cloud-based data storage (AWS S3, Azure Blob Storage).
00:00
Choosing the right storage based on use cases.
00:00
Activity: Hands-On Setup for Storing Ingested Data Using AWS S3 or Azure Blob Storage
01:00:00
Batch Processing with Apache Spark
Understanding Spark architecture. Loading and transforming data using Spark. Difference between RDDs, DataFrames, and Datasets. Activity: Run a sample batch processing job using Spark on a dataset.
0/4
Understanding Spark Architecture
00:00
Loading and Transforming Data Using Apache Spark
00:00
Difference between RDDs, DataFrames, and Datasets.
00:00
Activity: Run a sample batch processing job using Spark on a dataset.
00:00
Data Transformation, Orchestration & Monitoring
Data Transformation & ETL Tools Understanding ETL vs ELT. Using ETL tools: Talend, Apache Nifi, or Airflow. Data cleansing and transformation concepts. Activity: Create a data pipeline with Talend/Airflow for a simple ETL process.
0/4
Understanding ETL vs ELT.
00:00
Using ETL tools: Talend, Apache Nifi, or Airflow.
00:00
Data cleansing and transformation concepts.
00:00
Create a data pipeline with Talend/Airflow for a simple ETL process.
00:00
Data Orchestration
Introduction to orchestration tools: Apache Airflow, AWS Step Functions. Creating workflows to manage complex pipelines. Managing dependencies and retries in workflows.
0/1
Data orchestration process and benifits
00:00
Data Engineering
About Lesson

In the modern digital landscape, businesses and organizations generate massive amounts of data daily. However, data by itself is not inherently valuable until it is processed, analyzed, and transformed into actionable insights. This is where data engineering plays a critical role. Data engineering involves designing, building, and maintaining systems and infrastructure that allow data to be efficiently collected, stored, and processed for analysis. These systems form the backbone of data-driven decision-making in today’s enterprises.

 

1. Role of Data Engineering

Data engineers play a pivotal role in preparing and managing data for analysis, enabling data scientists and analysts to derive insights efficiently. Their responsibilities include:

  • Data Pipeline Development: Creating workflows that automate the extraction, transformation, and loading (ETL) of data from various sources to data warehouses or lakes.
  • Data Quality Management: Ensuring the accuracy and integrity of data throughout its lifecycle.
  • Data Modeling: Designing data structures and schemas to optimize performance and storage efficiency.
  • Collaboration: Working closely with data scientists, business analysts, and stakeholders to understand data needs and provide reliable datasets for analysis.

Overview of Data Engineering in Modern Architecture

  • In modern architectures, data engineering focuses on creating robust, scalable pipelines that handle various types of data (structured, unstructured, semi-structured) and deliver it to storage or analytics systems. Historically, data processing was primarily done in batch mode, where data was collected, processed, and analyzed in large chunks. Today, however, the landscape has evolved to also include real-time or streaming processing, where data is processed as soon as it’s generated.Modern data engineering architectures often feature:
  • Data Warehouses for structured data.

  • Data Lakes for handling both structured and unstructured data.

  • Cloud platforms for scalable infrastructure (e.g., AWS, Google Cloud, Azure).
  • Data pipelines to automate the movement and transformation of data.

2. Modern Architectural Components

This diagram depicts a high-level overview of a typical modern data architecture where data is ingested from multiple sources, processed using both batch and real-time tools, and stored in either a data lake or data warehouse for analysis

 

Previous
Next
wpChatIcon
wpChatIcon