5 Important Data Engineering Concepts in Microsoft Azure
This article covers important data engineering concepts and the real-time enterprise application model with architecture.
These concepts form the foundation of data engineering skills and solutions in Microsoft Azure, providing data engineers with the tools and services needed to build scalable, performant, and reliable data pipelines and analytics solutions in the real world.
What is Data engineering?
Data engineering prepares data for analysis by collecting, cleaning, and organizing it. Using tools like Azure Data Factory, data engineers gather data from various sources and store it in Azure Blob Storage. They clean and transform this data into a usable format, enabling analysts to create reports and derive insights for better decision-making.
Types of Data
There are primary three types of data –
| Structured | Semi-Structured | Unstructured |
| Structured data mainly originates from sources like relational databases, where data is organized in tables, or from files like CSV files, where information is arranged in rows and columns. | Semi-structured data is data such as JavaScript object notation (JSON) files and Parquet data, which requires flattening before loading into the source system. | Unstructured data refers to information that doesn’t have a specific format like text documents, pdf, emails, social media posts, images, audio files, and videos. |
Data Operations
Azure data operations refer to the various tasks and processes involved in managing, processing, and analyzing data within the Microsoft Azure cloud platform.
- Data Ingestion: Loading data from various sources into Azure storage services like Azure Blob Storage, Azure Data Lake Storage, or Azure SQL Database.
- Data Consolidation: Data consolidation is the process of combining data that has been extracted from multiple data sources into a consistent structure – usually to support analytics and reporting.
- Data Transformation: Operational data usually needs to be transformed into a suitable structure and format for analysis, often as part of an extract, transform, and load (ETL) process to quickly ingest the data into a data lake. Using services like Azure Data Factory, Azure Databricks, or Azure Synapse Analytics to transform data into a usable format for analysis or integration.
- Data Storage: Storing data in Azure’s scalable and secure storage solutions, including Azure Cosmos DB, Azure SQL Database, Azure Data Lake Storage, Azure Blob Storage, and Azure SQL Data Warehouse.
- Data Analysis and Processing: Utilizing Azure services like Azure Synapse Analytics, Azure HDInsight, Azure Databricks, or Azure Analysis Services for data analysis, machine learning, and big data processing.
- Data Governance and Security: Implementing Azure security and governance features such as Azure Active Directory, Azure Key Vault, Azure Sentinel, and Azure Policy to ensure data security, compliance, and regulatory requirements are met.
- Data Monitoring and Management: Monitoring data pipelines, storage usage, and performance using Azure Monitor, Azure Data Explorer, Azure Log Analytics, or Azure Data Catalog to ensure optimal data management and performance.
- Data Integration: Data Integration involves establishing links between operational and analytical services and data sources to enable secure, reliable access to data across multiple systems. Integrating data from various sources and formats using Azure Data Factory, Azure Logic Apps, or Azure Functions to create unified data sets for analysis and reporting.
- Data Backup and Recovery: Implementing Azure Backup, Azure Site Recovery, or Azure Blob Storage to backup and recover data in case of emergencies or data loss.
Important data engineering concepts
These are core concepts for Azure data engineering tools and practices-
Operational and analytical data
Operational data typically consists of transactional records generated and saved by applications, commonly stored in relational or non-relational databases. On the other hand, analytical data is data specifically structured for analysis and reporting, often found in data warehouses.
A key duty of a data engineer involves designing, implementing, and managing solutions that merge operational and analytical data sources. This entails extracting operational data from various systems, transforming it into formats suitable for analytics, and then loading it into an analytical data repository, commonly referred to as ETL (Extract, Transform, Load) solutions.
Streaming data
Streaming data refers to sources of data that generate data values in real-time, often relating to specific events, application and service logs, clickstream data, and sensor data.
Data engineers are also responsible for developing solutions that capture real-time streams of data and ingest them into analytical data systems.
Data pipelines
Data pipelines are used for orchestration activities that transfer and transform the data. Using Data Pipelines, data engineers develop repeatable extract, transform, and load (ETL) solutions that can be triggered based on a schedule or in response to events.
Data lakes
Azure Data Lake is a centralized repository designed to store, process, and secure petabyte-size files and trillions of objects, structured, semi-structured, and unstructured data.
Data warehouses
A data warehouse is a federated repository that stores structured data (database tables, Excel sheets) and semi-structured data (XML files, webpages) which can be used for data mining, data visualization, and other forms of business intelligence reporting.
Apache Spark
Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. It’s a common open-source software (OSS) tool for big data scenarios.
Data Engineering Solution in Microsoft Azure
Microsoft Azure includes multiple services or Azure data engineer tools for capturing data from different data sources, processing the data and load into data models and visuals.
- Operational data is generated from various applications and devices and stored in Azure data storage services such as Azure SQL Database, Azure Cosmos DB, and Microsoft Dataverse.
- Real-time Streaming data is collected in event broker services such as Azure Event Hubs.
- Â The core Azure data engineering technologies used to develop solutions include:
- Azure Synapse Analytics
- Azure Stream Analytics
- Azure Databricks
- Azure Data Factory
- Azure Data Lake Storage Gen2
- Data modelling and visualization for reporting and analysis are supported by analytical data stores, which are filled with data produced by data engineering workloads. This process is accomplished using advanced visualization tools like Microsoft Power BI.
Azure Data Engineering Services used in Workloads
These are the most used services for implementing data engineering projects and solutions.
Azure Synapse Analytics
Azure Synapse Analytics is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems. Azure Synapse Analytics includes functionality for pipelines, data lakes, and relational data warehouses.
Azure Stream Analytics
Azure Stream Analytics is a user-friendly real-time analytics service optimized for critical workloads. You can develop a complete serverless streaming pipeline, going from inception to deployment in minutes using SQL.
Azure Databricks
Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open-source libraries. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and sci-kit-learn.
Azure Data Factory
Azure Data Factory provides a fully managed, serverless data integration service, Prepares data, constructs ETL and ELT processes, and orchestrates and monitors pipelines.
Azure Data Lake Storage Gen2
Azure Data Lake Storage Gen2 is a powerful platform dedicated to big data analytics, built on Azure Blob Storage. Azure Data Lake Storage Gen2 empowers organizations to build enterprise data lakes on Azure, handling massive data volumes while providing flexibility, security, and compatibility with big data analytics frameworks.
Conclusion
By understanding and applying these key concepts, data engineers can effectively manage and process data to support data-driven decision-making and real-time Enterprise Business solution models.
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