Data Engineering: Building Robust Data Infrastructure, Slides of Computer science

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Data Engineering

Presented by: Abigail Atiwag

Data engineering is a field within data science and computer

science that focuses on designing, building, and maintaining

systems and infrastructure for collecting, storing, processing,

and analyzing large volumes of data. Here are some key topics

related to data engineering:

Data Storage Data engineers design and manage data storage solutions to store large volumes of structured, semi-structured, and unstructured data. This includes relational databases (SQL databases), NoSQL databases (document stores, key-value stores, column-family stores, graph databases), data warehouses, data lakes, distributed file systems (HDFS), and cloud storage services (Amazon S3, Google Cloud Storage, Azure Blob Storage).

Data Processing Data engineers develop data processing pipelines and workflows to clean, transform, enrich, aggregate, and prepare raw data for analysis. They use tools and frameworks such as Apache Hadoop, Apache Spark, Apache Flink, Apache Kafka, Apache Airflow, and distributed computing platforms to handle large-scale data processing tasks.

Data Modeling Data engineers design and implement data models and schemas to structure and organize data for storage and analysis. This includes defining entity-relationship models, dimensional models (star schema, snowflake schema), data cubes, data marts, and data structures optimized for specific analytical queries and use cases.

Data Pipelines Data engineers build and manage data pipelines that automate the flow of data from source systems to target systems. Data pipelines orchestrate data processing tasks, data transformations, data loading, and data movement across different stages of the data lifecycle. They ensure data pipelines are scalable, reliable, fault-tolerant, and efficient.

Data Governance Data engineers establish data governance policies, standards, and practices to ensure data quality, data security, data privacy, data compliance, and data ethics. They implement data governance frameworks, data lineage tracking, data cataloging, data access controls, data encryption, and data masking techniques to protect sensitive data and ensure regulatory compliance.

Data Monitoring and Management Data engineers monitor data pipelines, data workflows, and data systems to detect issues, anomalies, and performance bottlenecks. They implement data monitoring tools, logging mechanisms, alerting systems, and performance optimization techniques to maintain data integrity, availability, and reliability.

Scalability and Performance Data engineers design scalable and high-performance data solutions that can handle increasing data volumes, user concurrency, and analytical workloads. They optimize data storage, data processing algorithms, database indexing, query optimization, and resource allocation to achieve optimal performance and scalability.

Cloud Computing Data engineers leverage cloud computing services and platforms (Amazon Web Services, Google Cloud Platform, Microsoft Azure) to build and deploy data engineering solutions in the cloud. They use cloud-based infrastructure, managed services, serverless computing, and scalable storage to reduce infrastructure costs, improve agility, and support elastic data processing capabilities.

Real-Time Data Processing Data engineers design real-time data processing systems and architectures to handle streaming data, event-driven processing, and real-time analytics. They use technologies such as Apache Kafka, Apache Flink, Apache Spark Streaming, and stream processing frameworks to ingest, process, and analyze data in real time for timely insights and actions.

Data Collaboration and Teamwork Data engineers collaborate with data scientists, business analysts, data architects, software developers, and cross-functional teams to understand data requirements, design data solutions, and deliver data-driven projects. They communicate effectively, document data processes, share knowledge, and foster teamwork to achieve shared data goals and objectives.

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