Best Data Management Software for Azure Databricks

Based on your interests, history, and role, you will receive personalized and recommended results. QuaerisAI provides near-real-time data access for all data. QuaerisAI enhances your data and document workload with AI. To increase knowledge sharing and track performance, teams can share insights and pinboards. Our advanced AI engine transforms your inquiry to a database-ready language within micro-seconds. Data is nothing without context, just like life. Our cognitive AI engine interprets search terms, interests, roles, and past history to provide ranks results that allow further exploration. You can easily add filters to search results to dig into the details and explore relevant questions.

Enhance your data warehouse (DWH) development process by leveraging automation to design and produce intricate data models, such as dimensional, data mart, and data vault frameworks. This automated approach significantly shortens the time to realize value by optimizing workflows, which leads to greater accuracy and consistency in your data. With AnalyticsCreator, you can easily connect your data to various platforms including MS Fabric, Power BI, Snowflake, Tableau, and Azure Synapse, among others. The tool features built-in transformations and historization functions that allow for effective management of historical data, including support for Slowly Changing Dimensions (SCD) types, thereby improving data governance and operational performance. Facilitate collaboration and streamline your team's efforts with advanced version control capabilities and automated documentation processes, which help minimize development time. This enables quicker prototyping, schema evolution, and effective metadata management, fostering a more responsive approach to data management.

QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed

Kyvos is a semantic data lakehouse designed to speed up every BI and AI initiative, offering lightning-fast analytics at an infinite scale with maximum cost efficiency and the lowest possible carbon footprint. The platform provides high-performance storage for both structured and unstructured data, ensuring trusted data for AI applications. It is built to scale seamlessly, making it an ideal solution for enterprises aiming to maximize their data’s potential. Kyvos is infrastructure-agnostic, which means it fits perfectly into any modern data or AI stack, whether deployed on-premises or in the cloud. Leading companies rely on Kyvos as a unified source for cost-effective, high-performance analytics that foster deep, meaningful insights and context-aware AI application development. By leveraging Kyvos, organizations can break through data barriers, accelerate decision-making, and enhance their AI-driven initiatives. The platform's flexibility allows businesses to create a scalable foundation for a range of data-driven solutions.

StarfishETL is a Cloud iPaaS solution, which gives it the unique ability to connect virtually any kind of solution to any other kind of solution as long as both of those applications have an API. This gives StarfishETL customers ultimate control over their data projects, with the ability to build more unique and scalable data connections.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

You can quickly find answers with the flexible visual query builder. You can access data via your browser or phone and analyze it anywhere you are. No SQL, data scientist, or desktop required. You can learn from your team mates and search for any questions within your organization with shared questions. @mentions, push notifications and shared chat allow you to bring the right people in the conversation and make data actionable. You can easily copy and modify shared questions, export data and change the way charts are displayed so you don't just see someone else's analysis but make it yours. External sharing can be turned on to allow access to data tables and partners outside your domain. In just two clicks, you can access the underlying data tables. Smart typeaheads make it easy to run custom SQL.

Dasera is a Data Security Posture Management (DSPM) solution that provides comprehensive security and governance for structured and unstructured data across cloud and on-premises environments. It uniquely monitors data-in-use, offering continuous visibility and automated remediation to prevent data breaches at every data lifecycle stage. Dasera facilitates continuous risk detection and mitigation, ensuring seamless integration and regulation compliance. With a deep understanding of data infrastructure, attributes, users, and usage, Dasera empowers organizations to pursue a secure, data-driven growth strategy, minimizing risks and maximizing value in the digital era.

Connecting every data source with analytics services on a single AI-powered platform will transform how people access, manage, and act on data and insights. All your data. All your teams. All your teams in one place. Create an open, lake-centric hub to help data engineers connect data from various sources and curate it. This will eliminate sprawl and create custom views for all. Accelerate analysis through the development of AI models without moving data. This reduces the time needed by data scientists to deliver value. Microsoft Teams, Microsoft Excel, and Microsoft Teams are all great tools to help your team innovate faster. Connect people and data responsibly with an open, scalable solution. This solution gives data stewards more control, thanks to its built-in security, compliance, and governance.

Hackolade is the pioneer for data modeling of NoSQL and multi-model databases, providing a comprehensive suite of data modeling tools for various NoSQL databases and APIs. Hackolade is the only data modeling tool for MongoDB, Neo4j, Cassandra, ArangoDB, BigQuery, Couchbase, Cosmos DB, Databricks, DocumentDB, DynamoDB, Elasticsearch, EventBridge Schema Registry, Glue Data Catalog, HBase, Hive, Firebase/Firestore, JanusGraph, MariaDB, MarkLogic, MySQL, Oracle, PostgreSQL, Redshift, ScyllaDB, Snowflake, SQL Server, Synapse, TinkerPop, YugabyteDB, etc. It also applies its visual design to Avro, JSON Schema, Parquet, Protobuf, Swagger and OpenAPI, and is rapidly adding new targets for its physical data modeling engine. The software is user-friendly and simple to use yet provides powerful visuals and graphic data modeling to smooth the onboarding of NoSQL technology. Its software tools help functional analysts, designers, architects, and DBAs involved with NoSQL technology achieve greater transparency and control, resulting in reduced development time, increased application quality, and lower execution risks across the enterprise.

Prophecy expands accessibility for a wider range of users, including visual ETL developers and data analysts, by allowing them to easily create pipelines through a user-friendly point-and-click interface combined with a few SQL expressions. While utilizing the Low-Code designer to construct workflows, you simultaneously generate high-quality, easily readable code for Spark and Airflow, which is then seamlessly integrated into your Git repository. The platform comes equipped with a gem builder, enabling rapid development and deployment of custom frameworks, such as those for data quality, encryption, and additional sources and targets that enhance the existing capabilities. Furthermore, Prophecy ensures that best practices and essential infrastructure are offered as managed services, simplifying your daily operations and overall experience. With Prophecy, you can achieve high-performance workflows that leverage the cloud's scalability and performance capabilities, ensuring that your projects run efficiently and effectively. This powerful combination of features makes it an invaluable tool for modern data workflows.

DQOps is a data quality monitoring platform for data teams that helps detect and address quality issues before they impact your business. Track data quality KPIs on data quality dashboards and reach a 100% data quality score. DQOps helps monitor data warehouses and data lakes on the most popular data platforms. DQOps offers a built-in list of predefined data quality checks verifying key data quality dimensions. The extensibility of the platform allows you to modify existing checks or add custom, business-specific checks as needed. The DQOps platform easily integrates with DevOps environments and allows data quality definitions to be stored in a source repository along with the data pipeline code.

Discover how to enhance sales growth effortlessly by utilizing automated data pipelines that connect seamlessly to data lakes or cloud storage solutions without the need for coding. This adaptable platform adheres to industry standards, enabling the integration of sales and marketing data to generate automated insights for more intelligent expansion. Eliminate the hassle and costs associated with cumbersome manual data downloads. You’ll always have a clear understanding of your expenses, only paying for the services you actually use. Empower your tools with rapid access to data that is ready for analytics. Our certified developers prioritize security by exclusively working with official APIs. You can quickly initiate data pipelines sourced from widely-used platforms. With pre-built, pre-transformed pipelines at your disposal, you can unlock crucial data from sources like Amazon Vendor Central, Amazon Seller Central, Instagram Stories, Facebook, Amazon Advertising, Google Ads, and more. The processes for data ingestion and transformation require no coding, allowing teams to swiftly and affordably harness the full potential of their data. Your information is consistently safeguarded and securely stored in a reliable, customer-controlled data destination such as Databricks or Amazon Redshift, ensuring peace of mind as you manage your data assets. This streamlined approach not only saves time but also enhances overall operational efficiency.

A streaming database is specifically designed to efficiently ingest, store, process, and analyze large volumes of data streams. This advanced data infrastructure integrates messaging, stream processing, and storage to enable real-time value extraction from your data. It continuously handles vast amounts of data generated by diverse sources, including sensors from IoT devices. Data streams are securely stored in a dedicated distributed streaming data storage cluster that can manage millions of streams. By subscribing to topics in HStreamDB, users can access and consume data streams in real-time at speeds comparable to Kafka. The system also allows for permanent storage of data streams, enabling users to replay and analyze them whenever needed. With a familiar SQL syntax, you can process these data streams based on event-time, similar to querying data in a traditional relational database. This functionality enables users to filter, transform, aggregate, and even join multiple streams seamlessly, enhancing the overall data analysis experience. Ultimately, the integration of these features ensures that organizations can leverage their data effectively and make timely decisions.

Kedro serves as a robust framework for establishing clean data science practices. By integrating principles from software engineering, it enhances the efficiency of machine-learning initiatives. Within a Kedro project, you will find a structured approach to managing intricate data workflows and machine-learning pipelines. This allows you to minimize the time spent on cumbersome implementation tasks and concentrate on addressing innovative challenges. Kedro also standardizes the creation of data science code, fostering effective collaboration among team members in problem-solving endeavors. Transitioning smoothly from development to production becomes effortless with exploratory code that can evolve into reproducible, maintainable, and modular experiments. Additionally, Kedro features a set of lightweight data connectors designed to facilitate the saving and loading of data across various file formats and storage systems, making data management more versatile and user-friendly. Ultimately, this framework empowers data scientists to work more effectively and with greater confidence in their projects.

Tabular is an innovative open table storage solution designed by the same team behind Apache Iceberg, allowing seamless integration with various computing engines and frameworks. By leveraging this technology, users can significantly reduce both query times and storage expenses, achieving savings of up to 50%. It centralizes the enforcement of role-based access control (RBAC) policies, ensuring data security is consistently maintained. The platform is compatible with multiple query engines and frameworks, such as Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python, offering extensive flexibility. With features like intelligent compaction and clustering, as well as other automated data services, Tabular further enhances efficiency by minimizing storage costs and speeding up query performance. It allows for unified data access at various levels, whether at the database or table. Additionally, managing RBAC controls is straightforward, ensuring that security measures are not only consistent but also easily auditable. Tabular excels in usability, providing robust ingestion capabilities and performance, all while maintaining effective RBAC management. Ultimately, it empowers users to select from a variety of top-tier compute engines, each tailored to their specific strengths, while also enabling precise privilege assignments at the database, table, or even column level. This combination of features makes Tabular a powerful tool for modern data management.

Developing and overseeing data policies can often be a tedious and frustrating process. However, with PACE by STRM, you gain a powerful tool to ensure the secure usage of data. You can implement data policies through code, no matter where they are situated, eliminating lengthy delays and expensive meetings in the process. Say hello to your new open-source data security engine that transforms the way you manage data governance. Data policies extend beyond merely controlling access; they are essential for deriving meaningful insights from data while maintaining appropriate safeguards. PACE empowers you to collaborate on the rationale and timing, while automating the execution through code. With PACE, you have the ability to programmatically define and enforce data policies across various platforms. This tool can seamlessly integrate into your existing data platform and catalog, taking advantage of the inherent capabilities of your current stack. By automating policy application across critical data platforms and catalogs, PACE simplifies your governance efforts significantly. This innovative solution streamlines the creation and execution of policies, allowing you to centralize oversight while decentralizing implementation. Additionally, it simplifies compliance with auditing requirements by enabling you to demonstrate how controls are executed effectively. Ultimately, PACE revolutionizes data governance, making it more efficient and user-friendly.

Transmit only the modified data to the target location to eliminate latency issues and minimize resource consumption. Change data capture (CDC) serves as an effective strategy for synchronizing information efficiently. Utilizing log-based replication offers a seamless method for real-time data duplication without hindering the performance of the primary database. You can establish the complete solution swiftly, requiring no ongoing pipeline management. This allows your data teams to focus on more valuable initiatives. Implementing Artie is a straightforward process that involves just a few easy steps. Artie takes care of backfilling historical records and will consistently relay new modifications to the designated table as they happen. The system guarantees data consistency and exceptional reliability. Should an outage occur, Artie uses offsets in Kafka to resume operations from the last point, ensuring high data integrity while eliminating the need for complete re-synchronization. This robust approach not only streamlines data management but also enhances overall operational efficiency.

Our platform allows businesses to use data, including its application in advanced analysis, machine learning and AI, to do great things without worrying that customers, employees or intellectual property are at risk. The Protegrity Data Protection Platform does more than just protect data. It also classifies and discovers data, while protecting it. It is impossible to protect data you don't already know about. Our platform first categorizes data, allowing users the ability to classify the type of data that is most commonly in the public domain. Once those classifications are established, the platform uses machine learning algorithms to find that type of data. The platform uses classification and discovery to find the data that must be protected. The platform protects data behind many operational systems that are essential to business operations. It also provides privacy options such as tokenizing, encryption, and privacy methods.

Transferring data to the cloud can be a challenging task. However, with Bluemetrix Data Manager (BDM), we can make this transition much easier for you. BDM streamlines the ingestion of intricate data sources and adapts your pipelines automatically as your data sources evolve. It leverages automation for large-scale data processing in a secure, contemporary environment, offering user-friendly GUI and API interfaces. With comprehensive data governance automated, you can efficiently develop pipelines while simultaneously documenting and archiving all actions in your catalogue during pipeline execution. The tool's intuitive templating and intelligent scheduling capabilities empower both business and technical users with Self Service options for data consumption. This enterprise-level data ingestion solution is offered free of charge, facilitating quick and seamless automation of data transfer from on-premise locations to the cloud, while also managing the creation and execution of pipelines effortlessly. In essence, BDM not only simplifies the migration process but also enhances operational efficiency across your organization.

The toolkit to build interactive, fully customized analytics experiences into your apps. Embeddable believes that you shouldn't be forced to choose between buying or building your analytics solution. While creating charts, graphs and dashboards is an expensive, ongoing commitment, the out-of-the box solutions do not deliver the user experience that you desire for your clients. Welcome to the world of limitless creativity where you can create analytics experiences that are truly remarkable, and surpass your customers' expectations. Create your perfect experience using best-in class open source libraries. Embeddable displays data in your app using a secure read only transaction, regardless of whether your data is stored in a central data warehouse or distributed across multiple microservices. Let your imagination run wild and create the analytics solution you want, without compromising.

Experience robust data engineering processes free from the challenges of infrastructure management. By utilizing straightforward, modular Python, you can define intricate streaming, scheduling, and data backfill pipelines with ease. Transition from traditional ETL methods and access your data instantly, regardless of its complexity. Seamlessly blend deep learning and large language models with structured business datasets to enhance decision-making. Improve forecasting accuracy using up-to-date information, eliminate the costs associated with vendor data pre-fetching, and conduct timely queries for online predictions. Test your ideas in Jupyter notebooks before moving them to a live environment. Avoid discrepancies between training and serving data while developing new workflows in mere milliseconds. Monitor all of your data operations in real-time to effortlessly track usage and maintain data integrity. Have full visibility into everything you've processed and the ability to replay data as needed. Easily integrate with existing tools and deploy on your infrastructure, while setting and enforcing withdrawal limits with tailored hold periods. With such capabilities, you can not only enhance productivity but also ensure streamlined operations across your data ecosystem.

DataOps ETL Validator stands out as an all-encompassing tool for automating data validation and ETL testing. It serves as an efficient ETL/ELT validation solution that streamlines the testing processes of data migration and data warehouse initiatives, featuring a user-friendly, low-code, no-code interface with component-based test creation and a convenient drag-and-drop functionality. The ETL process comprises extracting data from diverse sources, applying transformations to meet operational requirements, and subsequently loading the data into a designated database or data warehouse. Testing within the ETL framework requires thorough verification of the data's accuracy, integrity, and completeness as it transitions through the various stages of the ETL pipeline to ensure compliance with business rules and specifications. By employing automation tools for ETL testing, organizations can facilitate data comparison, validation, and transformation tests, which not only accelerates the testing process but also minimizes the need for manual intervention. The ETL Validator enhances this automated testing by offering user-friendly interfaces for the effortless creation of test cases, thereby allowing teams to focus more on strategy and analysis rather than technical intricacies. In doing so, it empowers organizations to achieve higher levels of data quality and operational efficiency.

Generate data products swiftly from various sources without the need to relocate the data, making them accessible to everyone while retaining full oversight. Deliver impactful experiences that reveal value, while simultaneously enhancing your data mesh through effortless sharing, discovery, and governance across various domains. Encourage collaboration and speed up innovation by providing unified access to top-notch data products. Offer regulated access to AI models for every user, ensuring control over how data interacts with AI to protect intellectual property. Streamline AI workflows to quickly incorporate and refine new functionalities. Access and create data products directly from Snowflake without the hassle of data movement. Enjoy the simplicity of maximizing your data's potential, making it accessible for analysis and eliminating the necessity for centralized infrastructure and tools. Data products seamlessly integrate with various tools to uphold governance and expedite results, fostering a more efficient data environment. Thus, the approach not only enhances collaboration but also empowers users to leverage data more effectively.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.