Best ETL Software for Azure Databricks

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

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 capacity

Dagster is a next-generation orchestration platform for the development, production, and observation of data assets. Unlike other data orchestration solutions, Dagster provides you with an end-to-end development lifecycle. Dagster gives you control over your disparate data tools and empowers you to build, test, deploy, run, and iterate on your data pipelines. It makes you and your data teams more productive, your operations more robust, and puts you in complete control of your data processes as you scale. Dagster brings a declarative approach to the engineering of data pipelines. Your team defines the data assets required, quickly assessing their status and resolving any discrepancies. An assets-based model is clearer than a tasks-based one and becomes a unifying abstraction across the whole workflow.

Uncover insights to supercharge sales growth using code-free, fully-automated data pipelines to data lakes or cloud warehouses. A flexible, standards-based platform to unify sales and marketing data for automating insights and smarter growth. Say goodbye to messy, expensive manual data downloads. Always know what you’ll pay and only pay for what you use. Fuel your tools with quick access to analytics-ready data. As certified developers, we only work with secure, official APIs. Get started quickly with data pipelines from popular sources. Pre-built, pre-transformed, and ready-to-go data pipelines. Unlock data from Amazon Vendor Central, Amazon Seller Central, Instagram Stories, Facebook, Amazon Advertising, Google Ads, and many others. Code-free data ingestion and transformation processes allow teams to realize value from their data quickly and cost-effectively. Data is always securely stored directly in a trusted, customer-owned data destination like Databricks, Amazon Redshift, etc.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

DataOps ETL Validator is the most comprehensive data validation and ETL testing automation tool. Comprehensive ETL/ELT validation tool to automate the testing of data migration and data warehouse projects with easy-to-use low-code, no-code component-based test creation and drag-and-drop user interface. ETL process involves extracting data from various sources, transforming it to fit operational needs, and loading it into a target database or data warehouse. ETL testing involves verifying the accuracy, integrity, and completeness of data as it moves through the ETL process to ensure it meets business rules and requirements. Automating ETL testing can be achieved using tools that automate data comparison, validation, and transformation tests, significantly speeding up the testing cycle and reducing manual labor. ETL Validator automates ETL testing by providing intuitive interfaces for creating test cases without extensive coding.

AnalyticsCreator allows you to build on an existing DWH and make extensions and adjustments. If a good foundation is available, it is easy to build on top of it. Additionally, AnalyticsCreator’s reverse engineering methodology enables you to take code from an existing DWH application and integrate it into AC. This way, even more layers/areas can be included in the automation and thus support the expected change process even more extensively. The extension of a manually developed DWH (i.e., with an ETL/ELT tool) can quickly consume time and resources. From our experience and various studies that can be found on the web, the following rule can be derived, the longer the lifecycle, the higher the costs rise. With AnalyticsCreator, you can design your data model for your analytical Power BI application and automatically generate a multi-tier data warehouse with the appropriate loading strategy. In the process, the business logic is mapped in one place in AnalyticsCreator.