Business Software for Databricks

Astrato Analytics is a modern, warehouse-native business intelligence platform designed to help organizations create, embed, and share interactive dashboards and data-driven applications. It connects directly to leading cloud data warehouses such as Snowflake, BigQuery, Databricks, ClickHouse, Supabase, Amazon Redshift, PostgreSQL, and Dremio. By leveraging a zero-copy architecture, Astrato enables users to access and analyze data in real time without moving or duplicating it. Its live-query engine ensures that every dashboard and report reflects the most current data available. This eliminates the need for complex ETL processes or maintaining cached data layers. The platform also reduces operational overhead by simplifying data workflows and infrastructure requirements. Security is seamlessly managed, as Astrato inherits governance policies like row-level security and PII masking from the underlying data warehouse. This ensures compliance while minimizing manual configuration for IT teams. Users can build highly interactive and customizable analytics experiences directly on top of their existing data stack. The platform is designed to improve collaboration and data accessibility across teams. Overall, Astrato Analytics delivers a streamlined, real-time approach to business intelligence with strong performance and governance.

LayerLens serves as an autonomous platform dedicated to evaluating AI models, providing insights into their performance through verified benchmarks, prompt-specific outcomes, agentic comparisons, and audit-ready assessments across different vendors. This platform enables teams to conduct side-by-side comparisons of over 200 AI models, utilizing transparent benchmarks and consistent evaluation techniques focused on accuracy, latency, behavior, and practical application in real-world scenarios. Designed for comprehensive model analysis, LayerLens features Spaces that allow teams to organize benchmarks and evaluations, identify strengths in tasks, and monitor performance trends in relevant contexts. The platform also facilitates ongoing evaluations by continuously assessing model updates, prompt modifications, judge changes, and live traces, thereby empowering teams to identify issues like quality regressions, drift, silent failures, contamination, and policy concerns before they impact production. By prioritizing transparency and collaboration, LayerLens ensures that teams can make informed decisions about their AI model choices.

AccessOwl serves as a comprehensive tool for Access Governance and SaaS management, streamlining the process of managing employee access to various SaaS applications throughout their tenure, from onboarding to offboarding. Acting as the primary platform for overseeing SaaS access, it removes the confusion about who is responsible for specific tools and what approvals are necessary, while meticulously logging every application, user access, and the permissions utilized within the organization. By automating the processes of user account creation, access requests, approvals, and audits, along with detecting Shadow IT, AccessOwl enables teams to move away from spreadsheets and establish a reliable source of truth, significantly minimizing the chances of overlooking offboarding tasks. Furthermore, its integration with Slack allows employees to conveniently request access in the environment they already use, and HRIS integrations automate the onboarding and offboarding processes while keeping employee information such as job title, department, and manager up to date. Notably, AccessOwl has the capability to provision and revoke user access across a multitude of SaaS applications without the necessity for SCIM or SAML, ensuring flexibility and ease of use for organizations. This allows for a more efficient management of software access, ultimately enhancing security and compliance efforts.

Zingg is an open-source platform designed for Master Data Management, tailored for contemporary data warehousing needs. By leveraging machine learning for scalable entity resolution, it removes the reliance on costly and inflexible MDM solutions. Seamlessly integrated with Databricks, Microsoft Fabric, Snowflake, AWS, and GCP, Zingg empowers data teams to create a cohesive and reliable view of customers, suppliers, and products directly within their existing data environments. With a persistent Zingg ID, golden records are consistently upheld across various systems and data sources, ensuring data integrity and accessibility. Moreover, Zingg's innovative approach simplifies the management of master data, allowing organizations to focus on data-driven decision-making.

LakeSentry is a sophisticated software solution designed for cost monitoring and optimization specifically for teams utilizing Databricks. It seamlessly integrates with your Databricks environment, providing automatic tracking of expenditures across various areas such as workspaces, jobs, SQL warehouses, and individual users, enabling both platform and FinOps teams to understand their spending without the need for tedious manual checks. In addition to offering enhanced visibility, LakeSentry actively identifies cost anomalies and areas of idle expenditure, implementing optimization measures through a secure and gradual process that begins with shadow mode (where only recommendations are provided), advances to manual approval, and can ultimately culminate in fully automated execution if desired. Available as a Software-as-a-Service (SaaS) solution, LakeSentry includes a free tier along with flat-rate monthly subscriptions that come without additional charges per DBU or workspace. Key features include automatic cost attribution across various components of the Databricks ecosystem, real-time detection of anomalies and wasted resources, a multi-stage optimization process encompassing shadow, manual, and autopilot modes, and a native compatibility with Databricks, being aware of Unity Catalog usage and cluster lifecycle. The pricing structure is straightforward, allowing for unlimited workspaces under one flat rate, making it an accessible choice for teams looking to optimize their cloud expenditures. With these features, LakeSentry empowers organizations to manage their Databricks costs effectively while enhancing operational efficiency.

DataLumio simplifies the process of data extraction by enabling users to efficiently gather and arrange information from a variety of sources, including PDFs, spreadsheets, research papers, survey results, and datasets. With its AI-driven processing capabilities, the platform not only detects pertinent content but also organizes the information, making it ready for in-depth analysis and reporting. This innovative approach minimizes manual labor and enhances productivity for researchers, analysts, students, and organizations tasked with converting extensive data and documents into valuable insights. Ultimately, DataLumio empowers users to make informed decisions more quickly and effectively.

Jozu functions as an AI-driven platform focused on securing supply chains by validating artifacts prior to their execution, managing agent activities in real-time, and maintaining a record of all actions taken afterward. The Jozu Hub acts as a self-hosted repository for models, agents, MCP servers, and skills, ensuring that each artifact is consolidated with cryptographic signatures, attestations, thorough scanning, policy regulations, and audit trails. This platform's security analysis, tailored specifically for AI, addresses various threats including concealed executable code within model packages, compromised weights, data poisoning, prompt injection, insecure tools, and violations of licensing. Users can create policies once, which are then distributed as signed OCI artifacts, and these policies are enforced during the processes of pulling, promoting, admitting, or executing artifacts. Additionally, Jozu Agent Guard operates in conjunction with workloads across servers, desktops, edge devices, and isolated systems, implementing local filtering for prompts and input-output, access controls for tools, requirement for approvals, and enforcement of policies in real-time. Through this comprehensive approach, Jozu not only enhances security but also ensures a robust framework for managing and safeguarding AI-related artifacts throughout their lifecycle.

Cloudgov.ai serves as an intelligent AI-driven FinOps platform designed for ongoing management of costs and policy adherence across various environments, including cloud, multicloud, data systems, containers, and artificial intelligence. By integrating major platforms such as AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into a unified control panel, it enables teams to monitor expenses, allocation, policies, and associated risks in real time. Its Continuous Multicloud Observability feature links accounts, reviews past expenditures, categorizes costs based on region, account, and service, and projects future spending based on historical data. With AI-generated insights, the platform uncovers areas of waste and potential optimization, and its anomaly detection functionality alerts users to unexpected spikes in spending along with their financial implications. Furthermore, it provides ready-to-use Infrastructure as Code snippets for remediation, which allows engineering teams to implement suggested adjustments seamlessly, and integrates with Jira to convert insights and anomalies into actionable tasks for team members, thereby streamlining the workflow for cost management. Overall, Cloudgov.ai empowers organizations to maintain financial control while enhancing efficiency across their cloud operations.

Genie Code is an advanced AI tool designed specifically for data teams, providing the capability to analyze, construct, and manage intricate data workflows within the Databricks environment. This intelligent agent autonomously orchestrates and implements multi-step tasks while adjusting to the unique data and governance frameworks of an organization, boasting specialized skills in data engineering, data science, machine learning, and business intelligence. Leveraging the metadata, semantics, and governance of Unity Catalog, Genie Code can pinpoint authoritative tables, metrics, and assets, comprehend dependencies among various data and AI systems, and adhere to established access restrictions. In the realm of data science, it excels in locating and cleansing data, scrutinizing datasets, validating hypotheses, and producing easily shareable reports. For machine learning processes, it handles feature engineering, model training and assessment, deployment, endpoint setup, and fine-tuning performance. Additionally, data engineers can utilize natural language to streamline ETL processes, enhance query performance, and construct Spark Declarative Pipelines, making their workflows more efficient and user-friendly. Overall, Genie Code empowers data teams to work more effectively and innovate rapidly in their data-driven initiatives.

Databricks Genie One serves as an intelligent AI assistant designed for business users, offering a centralized platform to pose inquiries, analyze dashboards, utilize enterprise knowledge, and implement actions driven by AI that is rooted in organizational data. Users have the ability to inquire using everyday language and obtain contextually relevant responses that encompass both structured data from Databricks and unstructured knowledge sources, all without needing to write SQL queries or rely on an analyst's assistance. The Genie Ontology feature automatically constructs and enhances a dynamic knowledge graph that captures business terminology, entities, and key performance indicators, thereby providing insights and actions that reflect a unified business context. Additionally, Genie One streamlines workflows by automating tasks such as scheduling, tracking changes, drafting documents, storing reusable skills, and initiating updates across various platforms including email, Jira, and Slack. Furthermore, it accommodates custom Databricks Apps, AI/BI dashboards, boasts over 100 connectors, integrates with external sources like Google Drive and Salesforce, and employs MCP to incorporate Genie into a wider array of business applications. This comprehensive functionality empowers users to harness data more effectively, facilitating decision-making processes and enhancing productivity across the organization.

TabPFN-3.5 is an advanced foundation model specifically designed for achieving top-tier predictions on structured data, making it highly effective for a variety of tasks such as churn analysis, fraud detection, pricing strategies, demand forecasting, and risk assessment, thus enabling teams to utilize a single model for diverse applications. The model seamlessly processes data in its original form, adeptly managing issues like missing values, outliers, categorical data, multi-table datasets, free text features, and thousands of unique identifiers without requiring any encoding, while also accommodating numerous measurements per row. Users have the convenience of inputting raw data without the need for extensive feature engineering or preprocessing, allowing them to obtain high-quality, production-ready predictions immediately after the initial prediction call. Notably, TabPFN-3.5 generates predictions in a single forward pass, striking an optimal balance between accuracy and speed, and is optimized for quick inference, which is crucial for latency-sensitive predictive applications. Furthermore, it can efficiently handle large-scale datasets of up to one million rows natively and boasts a remarkable 20 times faster inference speed compared to its predecessors, making it a significant advancement in the field. This combination of efficiency, versatility, and performance positions TabPFN-3.5 as a powerful tool for data scientists and organizations seeking to leverage structured data effectively.

Gain insights into user interactions with your product and pinpoint areas of opportunity to guide your product strategy. Mode enables a single Stitch analyst to accomplish what typically requires an entire data team by offering rapid, adaptable, and collaborative tools. Create dashboards that track annual revenue and utilize chart visualizations to quickly spot anomalies. Develop well-crafted reports suitable for investors or facilitate collaboration by sharing your analyses with different teams. Integrate your complete technology ecosystem with Mode to uncover upstream problems and enhance overall performance. Accelerate cross-team workflows using APIs and webhooks. By analyzing user engagement, you can discover opportunity areas that help refine your product decisions. Additionally, utilize insights from marketing and product data to address vulnerabilities in your sales funnel, optimize landing-page efficiency, and anticipate churn before it occurs, ensuring proactive measures are in place.

Unravel Data is a powerful AI-native data observability and FinOps platform built for today’s complex enterprise data environments. It leverages intelligent Data Observability Agents to continuously monitor pipelines, workloads, and infrastructure for performance, reliability, and cost efficiency. Rather than just reporting issues, Unravel provides actionable insights that help teams resolve problems faster and prevent future incidents. The platform enables automated cost optimization, proactive troubleshooting, and performance tuning across the modern data stack. Unravel integrates seamlessly with existing tools and workflows, allowing teams to automate actions or maintain full control over decision-making. Purpose-built agents for FinOps, DataOps, and Data Engineering reduce firefighting, accelerate root cause analysis, and improve developer productivity. With native support for Databricks, Snowflake, and BigQuery, Unravel delivers deep, platform-specific visibility. Enterprises use Unravel to reduce cloud data costs, improve reliability, and scale operations confidently. Its agentic approach turns data observability into an active partner rather than a passive monitoring tool. Unravel empowers data teams to focus on innovation instead of constant issue resolution.

Astronomer is the driving force behind Apache Airflow, the de facto standard for expressing data flows as code. Airflow is downloaded more than 4 million times each month and is used by hundreds of thousands of teams around the world. For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Founded in 2018, Astronomer is a global remote-first company with hubs in Cincinnati, New York, San Francisco, and San Jose. Customers in more than 35 countries trust Astronomer as their partner for data orchestration.

USEReady is a data, analytics, and AI solutions firm headquartered in New York. With over a decade of experience, USEReady helps organizations transform data into actionable insights and achieve business goals. The company offers migration automation tools like STORM and MigratorIQ, along with Pixel Perfect for enhanced enterprise reporting. Plus, its two practices viz., Data Value, which focuses on modern data architectures and BI & AI initiatives, and Decision Intelligence, which empowers informed decisions and drives business outcomes through AI lend further credence to its focus on data-driven transformation. With a global team of 450+ experts and offices in the U.S., Canada, India, and Singapore, USEReady has served over 300 customers, including Fortune 500 companies across various industries. The company partners with industry leaders like Tableau, Salesforce, Snowflake, Starburst, and AWS, and has received multiple awards, including Tableau Partner of the Year.

Braze serves as a dynamic customer engagement platform that enhances messaging experiences via various channels like push notifications, email, and apps. Designed for the mobile-first landscape of today and the anticipated future of ambient computing, Braze distinguishes itself through its capability for real-time and continuous data streaming, effectively replacing outdated database systems that fall short in meeting the demands of today’s always-connected consumers. By fostering collaboration among data, technology, and teams, the Braze platform elevates marketing to a more genuine level, makes brands feel more relatable, and enhances customer satisfaction with each interaction. With a workforce of hundreds and venture backing, Braze operates offices in major cities including New York City, San Francisco, London, and Singapore. The company has garnered significant recognition, being ranked #85 on Forbes Cloud 100, #225 on Inc.'s 500 Fastest Growing Private Companies, #21 on the Deloitte Technology Fast 500 List, and has been acknowledged by The New York Times as part of the next generation of 'unicorn' startups, showcasing its rapid growth and innovation in the industry. This impressive recognition underscores Braze's commitment to redefining customer engagement in the digital age.

The ActionIQ Customer Data Platform empowers organizations to synchronize their personnel, technology, and procedures to create outstanding customer interactions at every point of engagement. To distinguish genuine CDPs from mere impostors, access ActionIQ's comprehensive guide, which will help you avoid months of tedious research and uncover the realities of the complex CDP market. In the current experience-driven economy, consumers anticipate that brands will recognize them and consistently provide authentic, valuable interactions. The ActionIQ CDP is designed to help large enterprises tackle persistent issues of customer data fragmentation, equipping them with the insights necessary to coordinate experiences seamlessly across all brand interactions. By developing an interconnected “smart hub” that consolidates data, organizations can empower their teams with immediate insights. This deep understanding of customers facilitates the delivery of trustworthy and profitable experiences at scale, ultimately enhancing brand loyalty and customer satisfaction. Moreover, embracing such a platform can significantly enhance your organization’s ability to respond to evolving customer needs swiftly and effectively.

Designed for accurate and scalable identity resolution, Amperity's platform fulfills the need for a comprehensive customer 360 foundation. By introducing an innovative method for large-scale identity resolution, discover how we have transformed the way brands utilize customer data effectively. Convert unrefined customer information into a driving force that enhances your organization across marketing, analytics, and IT functions. Eliminate the hassles of data manipulation and complex queries. Provide precise, impactful insights consistently throughout your organization. With speed, scalability, and security, our platform can easily manage extensive datasets while upholding user privacy. Our skilled teams collaborate with you to achieve up to a 10X return on your investment. This customer data platform is designed for the entire organization, catering to all users and integrating seamlessly with various tools. Our methodology clarifies disorganized data and allows you to leverage it quickly. With our industry-leading support and guidance, you can trust in our patented, AI-driven Customer 360 solution to maximize your data potential. In this way, organizations can stay ahead of their competitors while making data-driven decisions that foster growth.

Suplari brings spend analysis and procurement analytics into one platform for teams that buy at enterprise scale. Purchasing data is rarely in one place. It accumulates in finance systems, purchase order tools, card programs and contract repositories, each with its own vendor names and codes. Suplari reads all of it, resolves duplicate vendors, assigns every transaction to a category, and ties spending back to the agreements that govern it. Procurement gets a running view of where money is committed and where it actually goes, plus alerts when a supplier bills above an agreed rate, when buying bypasses a negotiated contract, when one division pays more than another for the same item, or when an agreement is about to renew on its own. Suplari also ships procurement AI agents that run analysis on a schedule, take questions in ordinary English, and stay with a savings idea until finance confirms the money landed. It layers on top of purchasing and ERP systems already in place.

FBerexia stands as a prominent global services provider, delivering an extensive array of solutions in consulting, digital innovation, and emerging technologies. Our mission is to create comprehensive and distinctive digital transformation services for our clients, tackling business challenges with a blend of innovation and profound industry expertise. We pride ourselves on offering a variety of cutting-edge solutions through interconnected services and products, along with software development that adds significant value. With a talented workforce of over 500 employees and subcontractors across the globe, we specialize in intelligent technologies such as Artificial Intelligence, Robotics, Digital Services, Blockchain, and Cloud Computing. Our operations span 12 countries across five continents, enabling us to serve clients in many additional locations. The projects we undertake cater to major key accounts across diverse sectors, underscoring our expertise and adaptability in the ever-evolving market landscape. Together, we strive to push the boundaries of technology and innovation to drive success for our partners.

Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.

Leverage your data effectively in real-time with Experience Platform, which serves as the backbone of Experience Cloud offerings, transforming both Adobe and non-Adobe data into comprehensive customer profiles that are continuously updated. This open system utilizes AI-driven insights to ensure you provide tailored experiences across all channels. By integrating data from various sources, you can enhance and create real-time customer profiles seamlessly. The standardized XDMs and data validation processes make your data more accessible and comprehensible. Start personalizing experiences immediately using these up-to-date customer profiles. Furthermore, our dynamic identity graphs allow for the activation of profiles across multiple devices and channels. Experience Platform boasts powerful application services that deliver unmatched functionality, enabling you to analyze customer interactions across channels from a single interface. You can also activate real-time customer profiles for immediate personalization and design individualized journeys that adapt to shifting customer behaviors, ensuring a responsive and engaging user experience. Ultimately, this leads to stronger customer relationships and improved business outcomes.

Syntasa introduces a revolutionary web application designed specifically for digital experts, allowing teams to seamlessly integrate first-party behavioral data with enterprise information. This self-service platform empowers users to create and implement tailored models, operate within a private cloud environment, and engage across multiple channels, enhancing the overall digital experience. With its user-friendly interface, Syntasa makes it easier than ever for digital professionals to harness the power of their data.

Revolutionize your omnichannel media effectiveness through the latest advancements in identity-centric marketing capabilities. TruAudience™ empowers marketing and measurement that are driven by data, offering a comprehensive suite of privacy-focused identity resolution, data enhancement, audience targeting, and sophisticated analytics solutions. Elevate your data strategy with a powerful, all-encompassing identity source that allows you to reach and interact with audiences effortlessly across every marketing channel. Continuously assess and enhance the effectiveness of your marketing expenditures to maximize returns. TruAudience integrates your data, teams, and marketing platforms via a unified identity graph, leading to improved results. This enables more precise audience targeting and personalized interactions across both paid and owned media. Optimize your media budget allocation, redefine audience targets, and refine creative and publisher placements to achieve your marketing goals more effectively. By leveraging these innovative tools, your marketing strategy can achieve unprecedented levels of efficiency and effectiveness.

IBM watsonx.data integration is an enterprise data integration platform built to help organizations deliver trusted, AI-ready data across complex environments. The solution provides a unified control plane that allows data engineers and analysts to integrate structured and unstructured data from multiple sources while managing pipelines from a single interface. Watsonx.data integration supports multiple integration styles including batch processing, real-time streaming, and data replication, enabling businesses to move and transform data based on their operational needs. The platform includes no-code, low-code, and pro-code interfaces that allow users of varying skill levels to design and manage pipelines. Built-in AI assistants enable natural language interactions, helping teams accelerate pipeline development and simplify complex tasks. Continuous pipeline monitoring and observability tools help teams identify and resolve data issues before they impact downstream systems. With support for hybrid and multi-cloud environments, watsonx.data integration allows organizations to process data wherever it resides while minimizing costly data movement. By simplifying pipeline design and supporting modern data architectures, the platform helps enterprises prepare high-quality data for analytics, AI, and machine learning workloads.