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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 Collection Data engineers design and implement systems to collect data from various sources such as databases APIs web scraping IoT devices sensors logs social media platforms and streaming data sources They ensure data quality reliability and consistency during the collection process 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 Integration Data engineers integrate data from multiple sources and systems to create unified and consistent datasets They perform data integration tasks such as data ingestion data merging data consolidation data synchronization and data federation to ensure data interoperability and accessibility across the organization 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 Big Data Technologies Data engineers work with big data technologies and platforms to handle large scale data processing and analytics This includes distributed computing frameworks Hadoop ecosystem Spark ecosystem data streaming platforms Kafka Flink NoSQL databases MongoDB Cassandra Redis and cloud based data services Amazon EMR Google Dataflow Azure HDInsight 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 Data Security and Privacy Data engineers implement security measures and privacy controls to protect data assets from unauthorized access data breaches cyber threats and data leaks They apply encryption techniques authentication mechanisms authorization policies data masking and data anonymization methods to safeguard sensitive data and ensure data privacy compliance 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 Data Quality and Cleansing Data engineers implement data quality checks data validation rules data profiling techniques and data cleansing processes to ensure data accuracy completeness consistency and reliability They address data anomalies duplicates missing values outliers and data errors to maintain high quality data assets for analytics and decision making 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 Data engineering plays a critical role in enabling organizations to harness the power of data for decision making business intelligence predictive analytics machine learning artificial intelligence and data driven innovation Data engineers contribute to building scalable reliable and efficient data infrastructure that supports data driven initiatives and accelerates digital transformation efforts THANK YOU

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SLU ITM 630 - Data Engineering: Building Robust Data Infrastructure

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