Course Content
Introduction to Data Engineering
In this topic, you'll learn the basics of data engineering, its significance, and the role of a data engineer. You will also get familiar with key terminologies used in the field.
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What & Why Data Engineering?
The Role of Data Engineer
Key Concepts and Terminologies
Data Engineering Concepts
00:15:00
Data Engineering Ecosystem
In this section, you will explore the data engineering ecosystem. We will cover the data engineering lifecycle and delve into the key components involved in the process. This will provide a clear understanding of how data flows through a system and how it is managed, processed, and utilized.
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Overview of the Data Engineering Lifecycle
Data Engineering Lifecycle Quiz
Data Quality
In this section, you will learn about the importance of data quality in data engineering. We will cover the basics of data quality, why it is crucial, and explore various techniques and tools used to ensure high data quality.
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What is Data Quality and Why is it Important?
Ensuring Data Quality Concepts
Data Quality Quiz
Data Warehousing
In this section, you will delve into the world of data warehousing. We will cover the basics of data warehousing, explore different architectures, and discuss popular data warehousing solutions used in the industry.
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Introduction to Data warehousing
Data Warehousing Architectures
Data Warehouse Modeling
Practical Demonstration and Recap
In this topic, you'll see a detailed demonstration of a complete ETL pipeline, followed by a recap of the key concepts covered in the course. You'll also participate in an interactive reflection session.
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Data Ingestion phase
Data Transformation phase
Loading Phase
Pipeline Orchestration
Data Pipelines for Newbies: Building Strong Data Fundamentals

The responsibilities and skills required for a data engineer

Typical Responsibilities of a Data Engineer

Data Engineers play a critical role in managing and optimizing data systems. Their daily tasks often include:

Data Pipeline Development

Creating and maintaining data pipelines that automate the process of moving data from source systems to data warehouses or data lakes. For instance, Developing a pipeline that extracts sales data from a company's transaction system, transforms it into a consistent format, and loads it into a data warehouse for analysis.

Data Quality Assurance

Ensuring the data is accurate, complete, and reliable by performing data validation, cleaning, and profiling. Regularly checking data for inconsistencies or missing values and implementing procedures to correct any issues.

Database Management

Setting up and maintaining databases and data storage solutions, ensuring they are optimized for performance and scalability. Configuring a cloud-based data warehouse to store large volumes of data and ensure fast query performance.

Collaboration

Working closely with data scientists, analysts, and other stakeholders to understand their data needs and provide the necessary support. Collaborating with data scientists to provide clean, preprocessed data for machine learning model training.

Key skills and Technologies

Data Engineers need a diverse set of skills and familiarity with various technologies, including:

Programming Languages

SQL for querying databases. Python for scripting, data manipulation, and building data pipelines. Java/Scala for working with big data tools like Apache Spark.

Data Storage Solutions

Relational Databases for structured data. NoSQL Databases for unstructured data. Data Warehouses for large-scale storage and analytics. Data Lakes for raw data storage.

Data Processing Tools

Apache Spark for distributed data processing and large-scale data analytics. Apache Kafka for real-time data streaming and event-driven data pipelines. ETL Tools: Such Apache NiFi, Mage for designing and managing ETL processes.

Cloud Platforms

Amazon Web Services: S3 for storage, Redshift for data warehousing. Azure: Azure Data Lake, Azure SQL Database. Google Cloud Platform: BigQuery for data warehousing, Cloud Storage for data lakes.

Daily Tasks and Examples

A day in the life of a data engineer can be quite dynamic, involving various tasks to ensure data systems are running smoothly and efficiently:
  • Monitoring Data Pipelines: Checking the status of overnight ETL jobs, ensuring they completed successfully without errors.
  • Example: Reviewing logs to verify that a nightly data ingestion pipeline processed all incoming data correctly.
  • Collaborating with Teams: Meeting with data scientists to discuss data requirements for a new machine learning project.
  • Example: Understanding the specific data transformations needed to prepare the dataset for model training.
  • Developing New Pipelines: Writing code to create a new data pipeline that integrates data from a new source system.
  • Example: Building a pipeline that extracts social media data, processes it to remove noise, and loads it into a data lake for analysis.
  • Data Quality Checks: Running scripts to profile data and identify any quality issues that need to be addressed.
  • Example: Identifying missing values in a customer dataset and implementing a process to fill in the gaps with appropriate data.

Summary

In this lesson, we explored the role of a data engineer, highlighting their typical responsibilities, the key skills and technologies they use, and examples of their daily tasks. Data engineers are essential in creating and maintaining the infrastructure that enables organizations to leverage data for decision-making and innovation.

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