Azure Data Engineer

9 Key Azure Data Engineering Tools For Data Engineers

Explore nine of the best Azure data engineering tools and services on your Azure Data Engineer Learning Path - 

1. Microsoft Azure Data Factory

Microsoft Azure Data Factory (ADF) is a fully-managed, serverless data integration tool for acquiring, analyzing, and processing all of your data in bulk. It enables all businesses across all sectors to use it for various use cases, including data engineering, operational data integration, analytics, importing data into data warehouses, and more.

If you are willing to gain some hands-on experience with Azure Data Factory, you must explore this Azure Data Factory project for Azure Text Analytics to Deploy a Medical Search Engine.

2. Microsoft Azure Databricks

Azure Databricks is a managed version of Databricks that enables one-click installation, faster workflows, and shared collaborative, interactive workspaces for Azure users. The Databricks platform helps facilitate collaboration between data scientists, data engineers, and business analysts. Azure Databricks is closely linked to Azure's computation and storage resources, including Azure Blob Storage, Data Lake Store, SQL Data Warehouse, and HDInsights. 

If you are willing to gain some hands-on experience with Azure Databricks, you must explore this Databricks project to Analyse Yelp Dataset with Spark & Parquet Format on Azure Databricks.

3. Microsoft Azure Stream Analytics

Azure Stream Analytics is a real-time, complex event-processing engine designed to simultaneously analyze and process large amounts of fast streaming data from various sources. Azure Stream Analytics enables you to examine enormous data volumes that are impossible to analyze manually. Additionally, you can extract information from the data stream and keep an eye out for data trends and patterns in the relationship.

If you are willing to gain some hands-on experience with Azure Stream Analytics, you must explore this Azure project to Build Streaming Data Pipeline using Azure Stream Analytics.

Kickstart your data engineer career with end-to-end solved big data projects for beginners.

4. Microsoft Azure Synapse Analytics

Microsoft Azure Synapse Analytics is an integrated platform solution that brings together the capability of data warehousing, data connectors, ETL pipelines, analytics tools, big data scalability, visualization, and dashboards. Organizations can efficiently process the data for warehousing and analytical purposes using Synapse Pipelines' ETL and data integration capabilities from several sources.

If you are willing to gain some hands-on experience with Azure Synapse Analytics, you must explore Building Data Pipelines in Azure with Azure Synapse Analytics.

5. Microsoft Azure Data Lake Storage

Data scientists, developers, and analysts can easily store data of any type and size using Azure Data Lake, which provides all the necessary infrastructure. Organizations can upload data of practically limitless magnitude to a single repository offered by Azure Data Lake storage. The store supports low-latency workloads and facilitates high-performance processing and analytics from HDFS applications and tools. Enterprise-grade security is available in the store to share data for collaboration.

6. Microsoft Azure SQL Database

Azure SQL Database, a member of the Azure SQL family, is a relational database service that is continually updated and fully managed for the cloud. You can use the simplicity and adaptability of a multi-model database that scales to meet the demand to create your next application. With native support for services like Azure Functions and Azure App Service, you can streamline development and build applications locally or in the cloud on popular platforms and frameworks. 

If you want to gain some hands-on experience with Azure SQL DB, you must explore this Azure SQL project to Learn Real-Time Data Ingestion with Azure Purview.

7. Microsoft Azure PostgreSQL Database

Instead of focusing on database management, the Azure PostgreSQL Database strongly emphasizes application innovation. It enables simple migration to a fully managed open-source database and supports the most recent PostgreSQL releases. The service-level agreement (SLA) for Azure Database for PostgreSQL includes:

Superior security.

Performance optimization enabled by AI.

Uptime guarantees of up to 99.99 percent.

8. Microsoft Azure MariaDB

Along with having direct integration with Azure Web Apps, Azure Database for MariaDB supports various other popular open-source frameworks and languages, such as WordPress and Drupal. It offers the necessary database administration features at no additional expense, including built-in monitoring and security, automatic backups, and automatic patching. Azure Database for MariaDB connects you to the Microsoft worldwide network of data centers with unmatched security and 24*7 monitoring.

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9. Microsoft Azure Cosmos DB

Azure Cosmos DB allows you to develop high-performance applications of any size or scope with a fully-managed and serverless distributed database that supports PostgreSQL, MongoDB, and Apache Cassandra. You can gain automatic and immediate scalability with single-digit millisecond reads and writes and 99.999 percent availability for NoSQL data. It enables you to use the most recent versions, tools, and extensions to launch and scale applications utilizing distributed PostgreSQL.

If you are willing to gain some hands-on experience with Azure Cosmos DB, you must explore Azure Stream Analytics for Real-Time Cab Service Monitoring project.

If you wish to gain more working experience with these Azure data engineering tools, check out the Microsoft Azure projects in the ProjectPro repository that will serve the right purpose. You can also access free guided project preview videos that give you an overview of the project workflow.