Top 10 Essential Azure Data Engineering Services for Data Engineers
Introduction to Data Engineering in Azure
Data engineering is the backbone of data-driven decision-making, focusing on the collection, transformation, and storage of vast amounts of data. In this era, where information reigns supreme, cloud computing has become essential. It offers unparalleled scalability and efficiency in storing massive datasets and performing complex data processing tasks. Among the leading cloud service providers AWS, Azure, and GCP data engineers rely on these platforms’ robust infrastructures to harness data’s potential. In this article I will delve into the top services offered by Azure, crucial for data engineers in architecting and managing modern data ecosystems.
10. Azure Storage
Azure offers a diverse array of data storage services, catering to the varied needs of modern data management. Azure storage is divided into four main categories they are Blob storage, File storage, Table storage, and Queue storage.
Blob Storage
- Stands for Binary Large Object
- Storing unstructured data (Text, Images, Videos, and so on..)
- Unlimited storage services.
- A massively scalable object store for text and binary data. Also includes support for big data analytics through Data Lake Storage Gen2.
- Use cases: When your application wants to support streaming and random access scenarios, when you want your data to be accessible from anywhere, and when to build an enterprise data lake on Azure and perform big data analytics.
File Storage
- Managed file shares for cloud or on-premises deployments.
- Enables you to set up highly available network file shares that can be accessed by using the industry standard Server Message Block (SMB) protocol, Network File System (NFS) protocol, and Azure Files REST API.
- Use cases: When you want to store development and debugging tools that need to be accessed from many virtual machines When you want to replace or supplement on-premises file servers or NAS devices.
Queue Storage
- A messaging store for reliable messaging between application components.
- Stores data in FIFO rule.
Table Storage
- A NoSQL store for schemaless storage of structured data.
- NoSQL key-value storage.
- No fixed schema.
09. Azure SQL DB
Azure SQL Database is a fully managed relational database service offered by Microsoft Azure. It’s designed to host SQL databases in the cloud without the hassle of managing infrastructure. This service ensures high availability, scalability, and robust security features for your data.
Azure SQL Database provides three deployment options:
- Single is a fully managed, isolated database.
- Elastic Pool is a collection of single databases with a shared set of resources.
- A managed Instance is a fully managed instance of the SQL server.
SQL Database offers a lot of available features like Automatic backups, Point-in-time restores, Active geo-replication, Auto-failover groups, and Zone-redundant databases.
08. Azure Cosmos DB
Azure Cosmos DB is a globally distributed, multi-model database service provided by Microsoft Azure. It’s designed to handle vast amounts of structured and unstructured data, allowing developers to build highly responsive and scalable applications. Applies to NoSQL, MongoDB, Cassandra, Gremlin, Table, and PostgreSQL. Azure Cosmos DB can simplify and expedite your development by being the single AI database for your applications. Azure Cosmos DB is a fully managed NoSQL and relational database for modern app development including; AI, digital commerce, Internet of Things, booking management, and other types of solutions.
Key Benefits
- Guaranteed speed at any scale
- Simplified application development
- Mission-critical ready
- Fully managed and cost-effective
07. Azure HDInsight
Azure HDInsight is a fully managed cloud service from Microsoft Azure that offers Apache Hadoop and Apache Spark clusters. It provides a scalable, reliable, and high-performance environment for processing large volumes of data. HDInsight is a cloud distribution of Hadoop components. HDInsight lets you run Apache Spark, Apache Hive, Apache Kafka, Apache HBase, and more in the cloud.
06. Azure Data Lake Storage
Azure Data Lake Storage is a highly scalable and secure cloud storage service from Microsoft Azure designed specifically for big data analytics. It enables users to store and analyze large amounts of structured and unstructured data at any scale. Azure Data Lake Storage Gen2 is an evolution of Data Lake Storage Gen1 built on top of Blob Storage. A data lake is a single, centralized repository where you can store all your data, both structured and unstructured. Data Lake Storage Gen2 includes the following capabilities Hadoop-compatible access, Hierarchical directory structure, Optimized cost and performance, Finer grain security model, and Massive scalability.
05. Azure Stream Analytics
Azure Stream Analytics is a fully managed (PaaS) offering on Azure. Azure Stream Analytics is a real-time stream processing and analytics service designed to handle high-volume data with sub-millisecond latency.
Key Features
- Real-time processing — Analyze data streams as they occur and empower businesses to gain valuable insights, detect anomalies, and trigger actions swiftly, making it a valuable tool for real-time analytics and decision-making.
- Scalability
- Ease of use — A few clicks is enough to connect multiple sources and sinks, and create an end-to-end pipeline. To ingest streaming data into Azure Stream Analytics we can use Azure Event Hub and Azure IoT Hub as well as historical data from Azure Blob Storage. In the same way, we can route the output from stream jobs into Azure Blob storage, Azure SQL Database, Azure Data Lake Store, and Azure Cosmos DB. It offers a no-code editor.
- Versatility — Low-latency dashboarding, Streaming analytics, Real-time alerting, Geospatial analytics, Predictive maintenance, and Clickstream analytics.
04. Azure Machine Learning
Azure ML is a cloud-based service provided by Azure to empower data scientists and developers to build, deploy, and manage high-quality machine learning models faster and with confidence.
The workspace contains all related assets related to ML such as Compute, Storage, Data, Scripts, Notebooks, Experiments, Metrics, Pipelines, and Models. It seamlessly integrates with other Azure services, enabling users to deploy models as web services or containers, monitor model performance, and implement continuous integration/continuous deployment (CI/CD) pipelines for efficient model updates.
Key Features
- Potential to auto-train and auto-tune a model.
- The model trained locally can be deployed.
03. Azure Data Factory
1. Azure Data Factory — Code-free ETL as a service that provides, data ingestion, Control flow, data flow, schedule, and monitor.
2. Integration Runtime — Provides the compute infrastructure that is used by ADF to run data flow and do data transformation. Three types of IR are available they are Azure, Self-hosted, and Azure-SSIS.
3. Triggers — Used to automatically run the pipeline during a certain frequency, scheduled time, or when some specific event happened. Types of triggers are Schedule triggers, Tumbling Window triggers, and Event-based triggers.
4. Mapping Data Flow — Visually designed data transformations in ADF.
5. Azure Key Vault — Used to store secure secrets.
ADF is the cloud-based ETL and data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data at scale. Azure Data Factory is composed of the following key components: Pipelines, Activities, Datasets, Linked services, Data Flows and Integration Runtimes.
02. Azure Synapse Analytics
Commonly known as Azure SQL Data Warehouse it’s a cloud-based analytic service provided by Azure. Supports both structured and unstructured data sources from all different data sources and allows users to process large amounts of data for analytical workloads. It supports massive parallel processing (MPP) architecture, enabling high-performance querying and processing of large datasets.
01. Azure Data Bricks
Azure Data Bricks is a unified analytics engine for big data processing. Azure Databricks is a fast, collaborative, and Apache Spark-based analytics platform offered by Microsoft Azure. It’s designed to simplify big data analytics and AI workflows by providing an interactive workspace for data engineers, data scientists, and analysts to collaborate and perform data processing, exploration, and machine learning tasks. It supports multiple languages such as Scala, Python, R, Java, and SQL. Azure Databricks offers three environments SQL, Data science and engineering, and Machine learning. The platform leverages Apache Spark’s distributed computing power, allowing users to scale resources dynamically based on workload requirements. It supports real-time analytics, batch processing, and ETL (Extract, Transform, Load) operations, providing versatility in data processing tasks.
Resources
- https://learn.microsoft.com/en-us/azure/storage/common/storage-introduction
- https://learn.microsoft.com/en-us/azure/azure-sql/database/sql-database-paas-overview?view=azuresql
- https://azure.microsoft.com/en-us/products/cosmos-db
- https://learn.microsoft.com/en-us/azure/hdinsight/
- https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction
- https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-introduction
- https://towardsdatascience.com/azure-machine-learning-service-part-1-an-introduction-739620d1127b
- https://k21academy.com/microsoft-azure/dp-100/azure-machine-learning-service-workflow-for-beginners/
- https://learn.microsoft.com/en-us/azure/synapse-analytics/overview-what-is
10. https://k21academy.com/microsoft-azure/data-engineer/azure-databricks/
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