Best Application Development Software for Azure Databricks
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Compare the Top Application Development Software that integrates with Azure Databricks as of July 2025
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This a list of Application Development software that integrates with Azure Databricks. Use the filters on the left to add additional filters for products that have integrations with Azure Databricks. View the products that work with Azure Databricks in the table below.
What is Application Development Software for Azure Databricks?
Application development software is a type of software used to create applications and software programs. It typically includes code editors, compilers, and debuggers that allow developers to write, compile, and debug code. It also includes libraries of pre-written code that developers can use to create more complex and powerful applications. Compare and read user reviews of the best Application Development software for Azure Databricks currently available using the table below. This list is updated regularly.
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1
QuerySurge
RTTS
QuerySurge leverages AI to automate the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Apps/ERPs with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Hadoop & NoSQL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise App/ERP Testing QuerySurge Features - Projects: Multi-project support - AI: automatically create datas validation tests based on data mappings - Smart Query Wizards: Create tests visually, without writing SQL - Data Quality at Speed: Automate the launch, execution, comparison & see results quickly - Test across 200+ platforms: Data Warehouses, Hadoop & NoSQL lakes, databases, flat files, XML, JSON, BI Reports - DevOps for Data & Continuous Testing: RESTful API with 60+ calls & integration with all mainstream solutions - Data Analytics & Data Intelligence: Analytics dashboard & reports -
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StarfishETL
StarfishETL
StarfishETL is an Integration Platform as a Service (iPaaS), and although “integration” is in the name, it’s capable of much more. An iPaaS lives in the cloud and can integrate different systems by using their APIs. This makes it adaptable beyond integration for migration, data governance, and data cleansing. Unlike traditional integration apps, StarfishETL provides low-code mapping and powerful scripting tools to manage, personalize, and manipulate data at scale. Features: - Drag and drop mapping - AI-powered connections - Purpose built integrations - Extensibility through scripting - Secure on-premises connections - Scalable data capacityStarting Price: 400/month -
3
Prophecy
Prophecy
Prophecy enables many more users - including visual ETL developers and Data Analysts. All you need to do is point-and-click and write a few SQL expressions to create your pipelines. As you use the Low-Code designer to build your workflows - you are developing high quality, readable code for Spark and Airflow that is committed to your Git. Prophecy gives you a gem builder - for you to quickly develop and rollout your own Frameworks. Examples are Data Quality, Encryption, new Sources and Targets that extend the built-in ones. Prophecy provides best practices and infrastructure as managed services – making your life and operations simple! With Prophecy, your workflows are high performance and use scale-out performance & scalability of the cloud.Starting Price: $299 per month -
4
KloudMate
KloudMate
Squash latencies, detect bottlenecks, and debug errors. Join a rapidly expanding community of businesses from around the world, that are achieving 20X value and ROI by adopting KloudMate, compared to any other observability platform. Quickly monitor crucial metrics, and dependencies, and detect anomalies through alarms and issue tracking. Instantly locate ‘break-points’ in your application development lifecycle, to proactively fix issues. View service maps for every component in your application, and uncover intricate interconnections and dependencies. Trace every request and operation, providing detailed visibility into execution paths and performance metrics. Whether it's multi-cloud, hybrid, or private architecture, access unified Infrastructure monitoring capabilities to monitor metrics and gather insights. Supercharge debugging speed and precision with a complete system view. Identify and resolve issues faster.Starting Price: $60 per month -
5
Horovod
Horovod
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.Starting Price: Free -
6
Datafold
Datafold
Prevent data outages by identifying and fixing data quality issues before they get into production. Go from 0 to 100% test coverage of your data pipelines in a day. Know the impact of each code change with automatic regression testing across billions of rows. Automate change management, improve data literacy, achieve compliance, and reduce incident response time. Don’t let data incidents take you by surprise. Be the first one to know with automated anomaly detection. Datafold’s easily adjustable ML model adapts to seasonality and trend patterns in your data to construct dynamic thresholds. Save hours spent on trying to understand data. Use the Data Catalog to find relevant datasets, fields, and explore distributions easily with an intuitive UI. Get interactive full-text search, data profiling, and consolidation of metadata in one place.
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