10 Best Azure Data Engineer Tools in 2025

One of the most important responsibilities for experts in big data is configuring the cloud to store data and provide high availability. As a result, data engineers working with big data today require a basic grasp of cloud computing platforms and tools. Businesses can employ internal, public, or hybrid clouds depending on their data storage needs, including AWS, Azure, GCP, and other well-known cloud computing platforms. In this article, I have highlighted a handful of the popular Azure data engineering tools and services. Please browse through them. 

What Are Azure Data Engineer Tools?

Azure Data Engineer Tools encompass a set of services and tools within Microsoft Azure designed for data engineers to build, manage, and optimize data pipelines and analytics solutions. These tools help in various stages of data processing, storage, and analysis. Let’s read about them in the next section.

Benefits of Azure Data Engineer Tools 

Azure tools for Data Engineers offer several benefits for organizations and professionals involved in data engineering:

• Scalability: Azure data services can scale elastically to handle growing data volumes and workloads, ensuring that your data solutions remain performant as your needs expand.
• Data Quality: Azure Data Engineer Tools offer data transformation and cleansing capabilities, improving data quality and accuracy for downstream analytics and reporting.
• Automation: Azure services support automation through tools like Azure Data Factory and Azure Logic Apps, reducing manual intervention and improving operational efficiency.
• Machine Learning Integration: Organizations can easily integrate Azure Machine Learning for building predictive models and incorporating machine learning into data engineering workflows.
• Open Source Support: Many Azure services support popular open-source frameworks like Apache Spark, Kafka, and Hadoop, providing flexibility for data engineering tasks.
• Resource Monitoring: Azure offers extensive monitoring and logging capabilities, allowing organizations to track the performance and health of their data engineering pipelines and services.
• Hybrid and Multi-Cloud: Azure supports hybrid cloud deployments, allowing organizations to integrate on-premises data centers with Azure services.

Top 10 Azure Data Engineer Tools

I have compiled a list of the most useful Azure Data Engineer Tools here, please find them below.

1. Azure Data Factory

Azure Data Factory is a cloud ETL tool for scale-out serverless data integration and data transformation. It offers a code-free UI for simple authoring and single-pane management. Additionally, you can move and run current SSIS packages with full compatibility in ADF on Azure.

All of your data can be collected, analyzed, and processed in bulk with Microsoft Azure Data Factory (ADF), a fully managed serverless data integration solution. It makes it possible for all companies from all industries to utilize it for a variety of use cases, including data engineering, operational data integration, analytics, integrating data into data warehouses, and more. Obtaining the Data Engineer Azure certification is a great way to learn this important tool.

2. Microsoft Azure Databricks

Azure Databricks is a data analytics platform specifically designed for the Microsoft Azure cloud services ecosystem. It offers three distinct environments: SQL for Databricks, Databricks Machine Learning for data engineering, and data science.

One-click installation, quicker processes, and shared collaborative, interactive workspaces are all made possible for Azure users by the managed version of Databricks known as Azure Databricks. Data scientists, data engineers, and business analysts may collaborate more easily thanks to the Databricks platform. Azure Blob Storage, Data Lake Store, SQL Data Warehouse, and HDInsights are just a few of the computing and storage services that Azure offers.

3. Microsoft Azure Stream Analytics

For the simultaneous analysis and processing of large amounts of fast streaming data from various sources, Azure Stream Analytics is a real-time, sophisticated event-processing engine.

Information that has been retrieved from a variety of input sources, including devices, sensors, apps, and more, can be used to identify patterns and relationships. These patterns can be used to start workflows, trigger events, or create alerts, as well as to feed data into reporting tools or store altered data for later use. Data processing on IoT devices is made possible by Azure Stream Analytics, which is available on the Azure IoT Edge runtime.

4. Microsoft Azure Synapse Analytics

Microsoft offers Azure Synapse Analytics, a scalable cloud-based data warehousing solution. The Azure SQL data warehouse's newest version is this one.

By combining SQL's data warehouse, Spark's big data analytics capabilities, and data connection technologies to make it easier to move data between them and from outside data sources, it creates a cohesive environment. With ASA, we can quickly ingest, process, manage, and provide data for BI and machine learning purposes.

5. Microsoft Azure Data Lake Storage

 Azure Data Lake, which includes all the necessary infrastructure, enables data scientists, developers, and analysts to conveniently store data of any type and quantity. A single repository made available by Azure Data Lake storage allows for the upload of data of nearly infinite size by organizations. The store supports low-latency workloads and enables HDFS tools and applications to execute high-performance processing and analytics. To share data for collaboration, enterprise-grade security is offered in the store.

6. Microsoft Azure SQL Database

The SQL database is Microsoft's premier database offering. It is a general-purpose relational database that accepts relational data types like JSON, spatial, and XML. Every Azure SQL Database is entirely managed by the Azure platform, which also ensures zero data loss and a high level of data availability. Azure automatically manages operations like patching, backups, replication, failure detection, underlying potential hardware, software, or network failure, deploying bug fixes, failovers, database upgrades, and other maintenance-related tasks.

7. Microsoft Azure PostgreSQL Database

With the help of the service Azure Database for PostgreSQL, one of the most widely used open-source database servers in the market is made available on the Azure platform in a PaaS paradigm. The Single Server option has been the most often used method of deploying PostgreSQL on the Azure platform up to this point. With the help of this model, a PostgreSQL Server with normal settings for a transactional system may be deployed. This choice is appropriate in situations where the database server needs little customization. High availability, disaster recovery, managed storage, and other standard features are available with this option.

8. Azure Cosmos DB

A multi-model, globally distributed, low latency database called Cosmos Database (DB) is used to manage massive amounts of data. It is a cloud-based NoSQL database provided by Microsoft Azure as a PaaS (Platform as a Service). It is a highly dependable, high-throughput database that is frequently referred to as a serverless database. The Azure Document DB is housed in the universally accessible Cosmos database. Before learning this advanced tool, it is recommended to learn cloud computing for beginners. 

9. Microsoft Azure MariaDB

In addition to being directly integrated with Azure Web Apps, Azure Database for MariaDB also supports a number of other well-known open-source frameworks and languages, including WordPress and Drupal. It includes built-in monitoring and security, automatic backups, and automatic patching among the necessary database administration tools that are provided at no additional cost. With unrivalled security and round-the-clock monitoring, Azure Database for MariaDB connects you to the Microsoft global network of data centres.

10. Azure HDInsight

Azure HDInsight is a cloud-based service from Microsoft that is intended for the processing and analysis of significant amounts of streaming and historical data. HDInsight is also available to businesses as a fully managed, full-spectrum analytics service. The use of open-source frameworks including Apache Hadoop, Apache Spark, Apache Hive, Apache Kafka, Apache LLAP, Apache Storm, and Microsoft Machine Learning Server is made possible by HDInsight.

Additionally, it has capabilities that make data warehousing, data loading, data transformation, and data extraction simpler. The hottest technology developments like machine learning and the Internet of Things (IoT) are supported by HDInsight. To create unique big data applications and process enormous amounts of data, enterprises can use HDInsight.

How to Choose the Best Azure Data Engineer Tools?

Choosing the best Azure tools for Data Engineers depends on your specific project requirements and objectives. Here's a structured approach to help you make the right choices:

• Understand Your Project Needs: Begin by defining your project's data requirements, including data volume, variety, velocity, and specific use cases.
• Review Azure Data Services: Familiarize yourself with the various Azure data services available, such as Azure Data Factory, Databricks, Synapse Analytics, and others.
• Consider Data Sources and Formats: Assess the types of data sources you need to integrate, the data formats they use, and any transformations required.
• Evaluate Scalability and Performance: Determine if your project needs to handle large-scale data processing and whether performance is a critical factor.
• Data Processing and Transformation: Consider the data processing and transformation capabilities required for your project, including batch and real-time processing.
• Integration with Other Services: Evaluate how well the tools integrate with other Azure services, such as databases, machine learning, and analytics tools.
• Cost Analysis: Estimate the costs associated with using specific tools, including data storage, data movement, and compute resources.
• Security and Compliance: Ensure that the tools meet your organization's security and compliance requirements, including data encryption and access controls.
• Scalability and Flexibility: Assess whether the tools can scale as your data engineering needs grow and whether they provide flexibility in adapting to changing requirements.
• Ease of Use and Learning Curve: Consider the ease of learning and using the tools. Are they user-friendly, or do they require extensive training?
• Community and Support: Check for community support, documentation, and Azure support options for the selected tools.
• Pilot and Testing: Before committing fully, consider running pilot projects or tests with the selected tools to ensure they meet your expectations.
• Feedback from Peers: Seek feedback from colleagues or industry peers who have experience with Azure data engineering tools.
• Keep Abreast of Updates: Azure services continually evolve, so stay informed about new features and improvements that may benefit your project.
• Cost Monitoring: Regularly monitor your usage and costs to optimize resource allocation and avoid unexpected expenses.

Conclusion

For businesses of all sizes, data analysis and processing are becoming increasingly crucial. Businesses need to develop methods for processing and analyzing data rapidly and effectively as the volume of data created keeps increasing. A strong solution to this issue is provided by Azure data engineer tools, which offer enterprises a variety of tools and services that accelerate, intelligently, and effectively handle data. 

Businesses can handle, store, and analyze massive volumes of data with this collection of services and technologies from Microsoft Azure. With the use of these tools, businesses will be able to process data more quickly, effectively, and securely than ever before. Scalability, economy of scale, and security are just a few advantages provided by Azure Data Engineer Tools. I would recommend enrolling for One of the most important responsibilities for experts in big data is configuring the cloud to store data and provide high availability. As a result, data engineers working with big data today require a basic grasp of cloud computing platforms and tools. Businesses can employ internal, public, or hybrid clouds depending on their data storage needs, including AWS, Azure, GCP, and other well-known cloud computing platforms. In this article, I have highlighted a handful of the popular Azure data engineering tools and services. Please browse through them.