Business Software for Databricks

Keras is an API tailored for human users rather than machines. It adheres to optimal practices for alleviating cognitive strain by providing consistent and straightforward APIs, reducing the number of necessary actions for typical tasks, and delivering clear and actionable error messages. Additionally, it boasts comprehensive documentation alongside developer guides. Keras is recognized as the most utilized deep learning framework among the top five winning teams on Kaggle, showcasing its popularity and effectiveness. By simplifying the process of conducting new experiments, Keras enables users to implement more innovative ideas at a quicker pace than their competitors, which is a crucial advantage for success. Built upon TensorFlow 2.0, Keras serves as a robust framework capable of scaling across large GPU clusters or entire TPU pods with ease. Utilizing the full deployment potential of the TensorFlow platform is not just feasible; it is remarkably straightforward. You have the ability to export Keras models to JavaScript for direct browser execution, transform them to TF Lite for use on iOS, Android, and embedded devices, and seamlessly serve Keras models through a web API. This versatility makes Keras an invaluable tool for developers looking to maximize their machine learning capabilities.

The Alation Agentic Data Intelligence Platform is designed to transform how enterprises manage, govern, and use data for AI and analytics. It combines search, cataloging, governance, lineage, and analytics into one unified solution, turning metadata into actionable insights. AI-powered agents automate critical tasks like documentation, data quality monitoring, and product creation, freeing teams from repetitive manual work. Its Active Metadata Graph and workflow automation capabilities ensure that data remains accurate, consistent, and trustworthy across systems. With 120+ pre-built connectors, including integrations with AWS, Snowflake, Salesforce, and Databricks, Alation integrates seamlessly into enterprise ecosystems. The platform enables organizations to govern AI responsibly, ensuring compliance, transparency, and ethical use of data. Enterprises benefit from improved self-service analytics, faster data-driven decisions, and a stronger data culture. With industry leaders like Salesforce and 40% of the Fortune 100 relying on it, Alation is proven to help businesses unlock the value of their data.

Alibaba Cloud, a subsidiary of Alibaba Group (NYSE: BABA), offers a wide range of global cloud computing solutions designed to enhance the online operations of our international clientele while also supporting Alibaba Group's e-commerce infrastructure. In a significant move, Alibaba Cloud was named the official Cloud Services Partner for the International Olympic Committee in January 2017. Committed to advancing the latest cloud technologies and robust security measures, we strive to fulfill our mission of simplifying global business interactions for everyone. Serving both large enterprises and small startups, as well as individual developers and public organizations, Alibaba Cloud extends its services across more than 200 countries and regions worldwide. Our dedication to innovation and customer satisfaction sets us apart in the cloud computing landscape.

Supply chain security and developer productivity are both based on simplified dependency lifecycle management. Endor Labs aids security and development teams by safely maximising software reuse. With a better selection process, you can reduce the number of dependencies and eliminate unused dependencies. To protect against software supply chain attacks, identify the most critical vulnerabilities and use dozens leading indicators of risk. You can get out of dependency hell quicker by identifying and fixing bugs and security issues in the dependency chain. Dev and security teams will see an increase in productivity. Endor Labs allows organizations to focus on delivering value-adding code by maximising software reuse and minimizing false positives. You can see every repos in your dependency network. Who uses what and who is dependent on whom?

LangChain provides a comprehensive framework that empowers developers to build and scale intelligent applications using large language models (LLMs). By integrating data and APIs, LangChain enables context-aware applications that can perform reasoning tasks. The suite includes LangGraph, a tool for orchestrating complex workflows, and LangSmith, a platform for monitoring and optimizing LLM-driven agents. LangChain supports the full lifecycle of LLM applications, offering tools to handle everything from initial design and deployment to post-launch performance management. Its flexibility makes it an ideal solution for businesses looking to enhance their applications with AI-powered reasoning and automation.

Apache Kafka® is a robust, open-source platform designed for distributed streaming. It can scale production environments to accommodate up to a thousand brokers, handling trillions of messages daily and managing petabytes of data with hundreds of thousands of partitions. The system allows for elastic growth and reduction of both storage and processing capabilities. Furthermore, it enables efficient cluster expansion across availability zones or facilitates the interconnection of distinct clusters across various geographic locations. Users can process event streams through features such as joins, aggregations, filters, transformations, and more, all while utilizing event-time and exactly-once processing guarantees. Kafka's built-in Connect interface seamlessly integrates with a wide range of event sources and sinks, including Postgres, JMS, Elasticsearch, AWS S3, among others. Additionally, developers can read, write, and manipulate event streams using a diverse selection of programming languages, enhancing the platform's versatility and accessibility. This extensive support for various integrations and programming environments makes Kafka a powerful tool for modern data architectures.

Equip your data professionals with advanced contextual learning agents that enable immediate insights from intricate structured data. Your data transcends mere numbers; it tells a story. Our advanced contextual learning system processes, enriches, and integrates your diverse, multi-source data, converting disjointed facts into a unified graph. From multi-cloud data warehouses to sophisticated data lineage tracking, observe the comprehensive narrative unfold in real-time. Acquire insights that adapt alongside your data, facilitating informed decisions free from distractions. Synchronize every data role into a cohesive workflow through agent-assisted collaboration. Analysts, engineers, managers, and artificial intelligence collaborate seamlessly, dismantling barriers with agent-driven processes that clarify even the most intricate analytics challenges. Our agents promote an effortless information exchange among teams, significantly reducing the time needed for insights and enhancing team effectiveness. Together, unleash the complete potential of your data. By fostering collaboration and streamlining processes, your organization can thrive in an increasingly data-driven landscape.

ER/Studio Data Architect is an enterprise data modeling solution that helps organizations design, document, and manage data architecture across modern platforms. It enables data architects and database professionals to create conceptual, logical, and physical data models that connect business meaning with technical implementation. By defining entities, relationships, and standards before systems are built, ER/Studio helps ensure consistent definitions, accurate reporting, and reliable analytics. A core capability of ER/Studio Data Architect is logical data modeling, which defines business concepts independently of technology. Logical models act as a semantic foundation for the organization, helping teams align on the meaning of key entities such as customers, products, and transactions. This approach reduces ambiguity, prevents semantic drift across systems, and improves the reliability of analytics and AI initiatives. The platform provides powerful forward and reverse engineering capabilities. Architects can generate database schemas from models or reverse engineer existing databases to document and analyze current structures. Schema compare and merge tools detect differences between versions and generate scripts to apply updates efficiently. ER/Studio Data Architect supports major platforms including SQL Server, Oracle, PostgreSQL, Snowflake, Databricks, and JSON-based systems. Automation features such as macros, data lineage, and impact analysis help teams understand dependencies and reduce manual work. The platform also includes ERbert, an AI-powered data modeling assistant that can generate logical models from natural language prompts, accelerating model creation while maintaining structured data architecture.

Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud. Key Features: Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines. Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance. Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects. Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.

HighByte Intelligence Hub is an Industrial DataOps software solution designed specifically for industrial data modeling, delivery, and governance. The Intelligence Hub helps mid-size to large industrial companies accelerate and scale the use of operational data throughout the enterprise by contextualizing, standardizing, and securing this valuable information. Run the software at the Edge to merge and model real-time, transactional, and time-series data into a single payload and deliver contextualized, correlated information to all the applications that require it. Accelerate analytics and other Industry 4.0 use cases with a digital infrastructure solution built for scale.

ER/Studio is an enterprise data modeling and architecture solution that helps organizations structure, align, and govern data across complex, distributed environments, including data warehouses, lakehouses, data mesh frameworks, and data vault architectures. It bridges business intent and technical execution through integrated conceptual, logical, and physical modeling, enabling teams to move from strategy to implementation with clarity and control. The result is a consistent architectural foundation that supports analytics, AI initiatives, modernization, regulatory requirements, and operational systems. Collaboration is built into the platform through a centralized, multi-user repository and the web-based Team Server portal. The repository manages version control, role-based permissions, and parallel development so teams can work concurrently while preserving model integrity and full audit history. Team Server extends visibility beyond architects, allowing business and technical stakeholders to review models, explore definitions, and contribute feedback through a browser interface. ER/Studio reinforces governance by embedding standardized definitions, business glossaries, and data dictionaries directly within technical models. Impact analysis provides insight into downstream dependencies before changes are implemented, helping reduce risk and improve coordination. Integrations with Microsoft Purview and Collibra extend metadata into broader governance ecosystems, strengthening lineage tracking, documentation accuracy, and compliance oversight. Available in Standard, Professional, and Enterprise editions, ER/Studio scales from focused modeling teams to enterprise-wide data architecture programs with advanced collaboration and governance requirements.

Ketch is the AI Privacy Company. Built by the founders who led GDPR compliance for Salesforce Marketing Cloud, Ketch is permissioning infrastructure that helps businesses collect and use people's data responsibly — respecting consent and preferences, meeting privacy requirements, and ensuring all data is AI-ready. The problem Ketch solves is real and growing. AI turns data into a compounding competitive advantage while simultaneously expanding the surface area of risk. Marketing tools assume permission is handled elsewhere. Privacy tools are compliance-first, not growth-first. The result: enterprises are forced into a false choice between data growth and data governance. Ketch closes that gap. The platform operates across three layers. First, discovery — see everything. Ketch observes what data exists, where it flows, how it's collected, and whether consent is actually being honored. Second, permissioning — control everything. Consent, rights, policy, and intended use are enforced in real time across systems. Third, activation — use everything, safely. Permissioned data powers AI pipelines, advertising, and personalization without regulatory or reputational risk. Core capabilities include consent management with 400+ no-code configurations, automated data subject rights, front-end data observability via Data Sentry, server-side permission storage via Permission Vault, and AI governance via AI Sentry — which monitors, enforces, and audits every AI interaction against consent and rights signals. 3,500+ businesses trust Ketch. 67.2 billion consent transactions processed per month. Rated #1 Consent Management Platform on Gartner Peer Insights (4.8/5) and a G2 Grid Leader for Enterprise. Used by Chipotle, Forbes, Paramount, Hasbro, Equifax and more.

Hackolade Studio is a comprehensive data modeling platform built for today’s complex and hybrid data ecosystems. Originally developed to address the lack of visual design tools for NoSQL databases, Hackolade has evolved into a multi-model solution that supports the broadest range of data technologies in the industry. The platform enables agile, iterative schema design and governance for both structured and semi-structured data, making it ideal for organizations working across traditional RDBMS, modern data warehouses, NoSQL stores, and streaming systems. Hackolade supports technologies such as Oracle, PostgreSQL, BigQuery, Databricks, Redshift, Snowflake, MongoDB, Cassandra, DynamoDB, Neo4j, Kafka (with Confluent Schema Registry), OpenAPI, GraphQL, and more. Beyond databases, Hackolade Studio offers robust capabilities for API modeling, supporting OpenAPI (Swagger) and GraphQL, as well as native modeling for data exchange formats like JSON Schema, Avro, Protobuf, Parquet, and YAML. It also integrates with metadata and data governance platforms like Unity Catalog and Collibra, making it a powerful enabler for organizations focused on data quality, lineage, and compliance. Key features include reverse and forward engineering, schema versioning, data type mapping, and team collaboration tools. Whether you're building data products, managing data contracts, or migrating between systems, Hackolade Studio provides a unified interface for modeling, documenting, and evolving your schemas. Hackolade is trusted by enterprises across finance, retail, healthcare, and telecom to align data architecture with real-world delivery. It’s an essential tool for teams implementing data mesh, data fabric, microservices, or API-first strategies.

Discover fresh perspectives, respond to a myriad of inquiries, and enhance your decision-making capabilities. With the simplicity of initiating your next project at the click of a button, no question will slip through your fingers. You can commence by forking any project from your colleagues or tapping into Observable’s extensive community of visualization and analysis professionals. Integrate some of Observable’s visualization tools and UI elements, or incorporate custom code utilizing any JavaScript library of your choice. The creative possibilities are limitless; construct projects in the way that suits you best. Engage in discussions by leaving comments and proposing edits, or dive into spontaneous live coding sessions with your colleagues. Gain insights from experts in our community forum. While viewers have the ability to access a private notebook shared with them, they cannot alter it. Editors, however, can make modifications, collaborate on the notebook, and manage its access. Nonetheless, viewers have the option to fork a notebook and create their own editable version. Additionally, you can export your code as native JavaScript modules, allowing them to function seamlessly anywhere with the open-source Observable runtime, thus broadening your project’s reach and versatility.

Select the tools that best suit your needs, and we will handle everything else. Create an ideal CI/CD stack tailored to your organization's objectives without the worry of vendor lock-in. By eliminating the need for manual scripts and complex toolchain automation, your engineers can concentrate on your main business activities. Our pipeline workflows utilize a declarative approach, allowing you to prioritize essential tasks over the methods used to achieve them, covering aspects such as software builds, security assessments, unit testing, and deployment processes. With the help of Blueprints, you can troubleshoot any issues directly within Opsera, thanks to a detailed console output for each step of your pipeline's execution. Gain a holistic view of your CI/CD journey with extensive software delivery analytics, tracking metrics like Lead Time, Change Failure Rate, Deployment Frequency, and Time to Restore. Additionally, benefit from contextualized logs that facilitate quicker resolutions while enhancing auditing and compliance measures, ensuring that your operations remain efficient and transparent. This streamlined approach not only promotes better productivity but also empowers teams to innovate more freely.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

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.

In order to get a complete view of customer/account-level journeys, understand attribution, and uncover cross-functional business insights, event data is often duplicated and exported from product analytics solutions to the data warehouse. This creates inconsistent data between two platforms: siloed product analytics solutions and SQL/BI tools running on the data warehouse. Adding to the challenge, BI tools are not even designed to explore and derive insights from event data. NetSpring offers a single self-service tool for product analytics with BI-style ad hoc visual exploration, working directly off the data warehouse as the single source of truth. Key Benefits: - GTM Teams: Self-serve answers to the next business question without worrying about data availability - Data & Analytics Teams: Support GTM teams with governed, self-service tooling - C-Suite: Leverage the data warehouse (source of truth) for consistent results and to avoid data duplication, reverse ETL, and security issues Key Capabilities: - Self-Service: Rich library of behavioral analytics templates - Analytical Power of BI: Self-guided ad hoc visual exploration - Warehouse-Native: Rich business context with no data duplication

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

RTILA X is a local-first web automation engine for developers and power users. Built on Tauri, it runs entirely on your hardware — no cloud dependency, no telemetry, no per-execution billing. The AI Assistant generates complete automations from plain English using local GGUF models (including RTILA Lite 1.5 9B, needs only 4–6GB RAM) or any OpenRouter model (DeepSeek, Claude, Gemini, GPT). Failed scripts are analyzed, rewritten, and retried automatically — up to 3 self-correction passes. Network API Interception passively captures XHR/fetch traffic and auto-generates extraction steps from JSON payloads, skipping brittle CSS/XPath selectors entirely. Stealth stack: cubic Bezier mouse paths with human velocity profiles, proxy rotation with fingerprint/timezone alignment, isolated browser profiles, 2Captcha integration. Checkpoint & resume retries only failed URLs. Ships with an MCP server, API/webhook triggers, Telegram daemon, scheduler, and 30+ integrations. Unique: compile automations into standalone executables you can white-label and sell. Free tier; $149 lifetime.

Weaviate is an open-source vector database built for the AI era, giving teams one platform for vector search, retrieval-augmented generation, and agent memory. Store data objects together with embeddings from your preferred machine learning models and scale effortlessly to billions of objects. Import your own vectors or rely on Weaviate's built-in vectorization, then search across vector, keyword, and hybrid methods to get highly relevant results, even when filters are applied. By connecting to today's leading large language models, Weaviate helps you build grounded search and question-answering over your own data. The platform reaches well beyond storage: its Query Agent translates plain-language questions into accurate queries with citations, Engram delivers managed long-term memory for AI agents, and Weaviate Embeddings removes the work of running your own embedding pipeline. Available as self-hosted open source or fully managed Weaviate Cloud across AWS, GCP, and Azure, backed by SOC 2 Type II, native multi-tenancy, replication, and role-based access control. From semantic search to recommendation to fully agentic applications, Weaviate is the foundation to ship AI products faster.

Production database operations often require developers to move quickly while DBAs and security teams maintain control. Bytebase provides a shared platform built around three capabilities: Govern Database Changes — route schema and data updates through consistent review, approval, SQL validation, and rollout workflows before they reach production. Control Database Access — provide developers and AI agents with temporary, policy-based access while masking sensitive data and avoiding shared database credentials. Preserve an Audit Trail — record database queries, access requests, approvals, and changes so teams can investigate activity and demonstrate accountability. Bytebase supports both UI-driven and GitOps workflows, helping engineering teams standardize database operations without replacing their existing development tools.

As enterprise data explodes and is scattered across multiple systems, the oversight of privacy, data security and governance has become a very difficult task. Businesses are exposed to significant risks, including data breaches, privacy suits, and penalties. It takes months to find data privacy risks within an organization. A team of data engineers is involved in the effort. Data breaches and privacy legislation are forcing companies to better understand who has access to data and how it is used. Enterprise data is complex. Even if a team works for months to isolate data privacy risks, they may not be able to quickly find ways to reduce them.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.