Data Engineering Fundamentals

What is Data Engineering?

Data engineering is the process of designing, building, and maintaining the infrastructure that stores, processes, and retrieves large and complex datasets.

Key Concepts:

Data Ingestion

• Collecting data from various sources (e.g. sensors, social media, APIs)
• Handling high-volume, high-velocity, and high-variety data
• Data ingestion tools: Apache Kafka, Apache NiFi, AWS Kinesis

Data Storage

• Designing and implementing scalable data storage solutions
• Data warehousing, data lakes, and NoSQL databases
• Data storage tools: HDFS, Apache Cassandra, Amazon S3

Data Processing

• Processing large datasets using distributed computing frameworks
• Handling data transformations, aggregations, and filtering
• Data processing tools: Apache Hadoop, Apache Spark, Apache Flink

Data Retrieval

• Designing and implementing data retrieval systems
• Handling data queries, filtering, and aggregation
• Data retrieval tools: Apache Hive, Apache Impala, Amazon Redshift

Data Engineering Roles:

Data Engineer

• Designs, builds, and maintains data pipelines
• Ensures data quality, security, and scalability
• Collaborates with data scientists, analysts, and other stakeholders

Data Architect

• Designs and implements data architecture
• Ensures data integration, governance, and standards
• Collaborates with data engineers, scientists, and other stakeholders

Data Engineering Challenges:

Data Quality

• Handling noisy, incomplete, or inconsistent data
• Ensuring data accuracy, completeness, and consistency

Data Security

• Ensuring data confidentiality, integrity, and availability
• Implementing access controls, encryption, and auditing

Scalability

• Handling large and growing datasets
• Ensuring system performance, reliability, and fault tolerance

Learn the basics of data engineering, including data ingestion, storage, processing, and retrieval. Explore data engineering roles and challenges, such as data quality and security.