How can you apply data engineering to different data types?

1 Structured data

Structured data is data that has a predefined schema, format, and rules, such as relational databases, spreadsheets, and CSV files. Data engineering for structured data involves creating and managing tables, schemas, indexes, queries, and transactions, as well as optimizing performance, security, and scalability. Some of the common tools and technologies for data engineering with structured data are SQL, PostgreSQL, MySQL, Oracle, SQL Server, and SQLite.

2 Semi-structured data

Semi-structured data is data that has some level of organization and hierarchy, but not a fixed schema or format, such as JSON, XML, HTML, and YAML files. Data engineering for semi-structured data involves parsing, validating, and extracting data elements, as well as transforming and loading them into structured or unstructured data stores. Some of the common tools and technologies for data engineering with semi-structured data are Python, Java, Spark, Hadoop, MongoDB, and Cassandra.

3 Unstructured data

Unstructured data is data that has no predefined structure, format, or rules, such as text, images, audio, video, and social media posts. Data engineering for unstructured data involves applying natural language processing, computer vision, speech recognition, and machine learning techniques to extract, analyze, and generate insights from data, as well as storing and retrieving them in distributed and scalable systems. Some of the common tools and technologies for data engineering with unstructured data are TensorFlow, PyTorch, OpenCV, NLTK, Elasticsearch, and S3.

4 Data quality

Data quality is the degree to which data is accurate, complete, consistent, reliable, and timely for its intended purpose. Data engineering for data quality involves implementing data validation, cleansing, standardization, deduplication, and enrichment processes, as well as monitoring and reporting on data quality metrics and issues. Some of the common tools and technologies for data engineering for data quality are Apache Airflow, Apache Kafka, Apache NiFi, Talend, and Dataiku.

5 Data integration

Data integration is the process of combining data from different sources and formats into a unified and consistent view. Data engineering for data integration involves designing and building data pipelines, workflows, and ETL (extract, transform, load) processes, as well as ensuring data compatibility, integrity, and security. Some of the common tools and technologies for data engineering for data integration are Apache Spark, Apache Beam, Apache Flink, AWS Glue, and Google Cloud Dataflow.

6 Data visualization

Data visualization is the process of presenting data in graphical or interactive forms to communicate information, patterns, and insights. Data engineering for data visualization involves preparing and transforming data for visualization, as well as selecting and using appropriate tools and frameworks to create and deploy data visualizations. Some of the common tools and technologies for data engineering for data visualization are Matplotlib, Seaborn, Plotly, D3.js, and Tableau.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?