Auraa Alternatives

SCIKIQ Data Fabric makes enterprise data AI-ready, without rebuilding the data stack, In weeks and months or years SCIKIQ is an AI-native Data & Intelligence Platform that helps enterprises connect, contextualize, govern and activate their data for analytics, Generative AI and intelligent agents. Instead of adding another disconnected tool, SCIKIQ creates a unified intelligence layer across the technology you already use from SAP, Oracle and Salesforce to Snowflake, Databricks, cloud platforms, data lakes and enterprise applications. The result is trusted, contextualized and AI-ready enterprise data — in weeks, not years. What makes SCIKIQ Data Fbric different is its ability to bring the entire data-to-AI journey into one platform. Data integration, transformation, data quality, governance, catalog, lineage, semantic models, knowledge graphs, conversational analytics, AI/ML, data products and AI agents work together rather than as separate tools. At the heart of SCIKIQ is Contextual Intelligence. SCIKIQ connects technical metadata with business definitions, KPIs, ownership, relationships and rules so that people and AI understand what enterprise data actually means. This semantic foundation helps create more trusted analytics and better-grounded AI responses. Business users can simply ask questions of their enterprise data in natural language, explore KPIs and root causes, and receive contextual answers without depending on SQL or waiting for another report. For data and technology teams, SCIKIQ provides a governed foundation with 200+ connectors, active metadata, multi-hop lineage, data quality, role-based governance and multi-cloud support across AWS, Azure, GCP, hybrid and on-prem environments, and you don't have to rip and replace your existing investments. SCIKIQ works with your stack, not against it. SCIKIQ is already trusted in production by leading enterprises across the USA, India and UAE, including organizations such as American Express, London Stock Exchange Group, Landmark Group and EFS. Its solutions have also been delivered alongside global technology and consulting ecosystems including AWS, Microsoft Azure, Deloitte, EY, Infosys and Tech Mahindra. SCIKIQ has been recognized by Forrester, NASSCOM, YourStory, Inc42 and DataIQ, providing independent validation of its innovation in enterprise data and AI. If your enterprise already has data but is struggling to turn it into trusted AI, SCIKIQ is where that journey begins

FinOpsly is the Value Control™ platform for Cloud, Data, and AI economics. It helps enterprises move beyond cost visibility to actively control spend and business outcomes through explainable, policy-governed AI automation. Unlike reporting-only FinOps tools, FinOpsly unifies cloud (AWS, Azure, GCP), data (Snowflake, Databricks, BigQuery), and AI costs into a single system of action — enabling teams to plan spend before it happens, automate optimization safely, and prove value in weeks, not quarters. FinOpsly enables enterprises to: Map spend to business value across products, teams, customers, and workloads Explain cost drivers clearly with AI-generated context and root-cause analysis Automate optimization safely using policy-driven, explainable agents Prevent drift and overages before they impact budgets or performance

Your data, your insights—no third-party ownership or black-box analytics. Kubit is the leading Customer Journey Analytics platform for enterprises, enabling self-service insights, rapid decisions, and full transparency—without engineering dependencies or vendor lock-in. Unlike traditional tools, Kubit eliminates data silos, letting teams analyze customer behavior directly from Snowflake, BigQuery, or Databricks—no ETL or forced extraction needed. With built-in funnel, path, retention, and cohort analysis, Kubit empowers product teams with fast, exploratory analytics to detect anomalies, surface trends, and drive engagement—without compromise. Enterprises like Paramount, TelevisaUnivision, and Miro trust Kubit for its agility, reliability, and customer-first approach. Learn more at kubit.ai.

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.

Databricks Unity Catalog is the industry’s only unified and open governance solution for data and AI, built into the Databricks Data Intelligence Platform. With Unity Catalog, organizations can seamlessly govern both structured and unstructured data in any format, as well as machine learning models, notebooks, dashboards, and files across any cloud or platform. Data scientists, analysts, and engineers can securely discover, access, and collaborate on trusted data and AI assets across platforms, leveraging AI to boost productivity and unlock the full potential of the lakehouse environment. This unified and open approach to governance promotes interoperability and accelerates data and AI initiatives while simplifying regulatory compliance. Easily discover and classify both structured and unstructured data in any format, including machine learning models, notebooks, dashboards, and files across all cloud platforms.

DataWise is a purpose-built solution for SAP data modernization. It bridges SAP (ECC or S/4HANA) and the Databricks Lakehouse to transform siloed operational data into a trusted, analytics-ready foundation for real-time decisions and AI innovation. DataWise accelerates value with SAP-native connectors and prebuilt models for common modules (SD, MM, PM, Finance, Ariba, SuccessFactors). Automated ELT pipelines land data into Delta Lake, while MatchX AI-powered data quality engine performs cleansing, standardization, deduplication, and entity matching to raise data accuracy and completeness at scale. Governance is enforced end-to-end through Unity Catalog, fine-grained access controls, and lineage. Once standardized and governed, DataWise activates your SAP data across BI dashboards, machine-learning features, and event-driven workflows without disrupting core ERP.

LakeSentry is cost monitoring and optimization software for teams running Databricks. It connects to your Databricks environment and automatically attributes spend across workspaces, jobs, SQL warehouses, and users, so platform and FinOps teams see exactly where every dollar goes without manual investigation. Beyond visibility, LakeSentry continuously detects cost anomalies and idle waste, and applies optimization actions through a safe, progressive workflow — shadow mode (recommendations only), then manual approval, then optional fully automated execution. Delivered as SaaS with a free tier; paid plans are flat monthly, with no per-DBU surcharge and no per-workspace fees. Feature list: - Automatic cost attribution across workspaces, jobs, SQL warehouses, users - Real-time anomaly and idle-waste detection - Progressive optimization: shadow → manual → autopilot - Databricks-native: Unity Catalog usage and cluster lifecycle aware - Flat pricing, unlimited workspaces

Unravel is an AI-native data observability platform designed to help modern enterprises detect, resolve, and prevent data issues at scale. It uses intelligent, automated agents that work alongside data teams to surface insights, guide decisions, and reduce operational toil. Unravel brings data observability and FinOps together, enabling organizations to improve performance, ensure reliability, and optimize cloud data spending. The platform provides end-to-end visibility across pipelines, workloads, and infrastructure. With agent-driven actionability™, Unravel can take action on behalf of teams, integrate directly with existing tools, or recommend next-best actions. It supports major data platforms including Databricks, Snowflake, and Google Cloud BigQuery. By combining automation with human control, Unravel transforms data observability into a collaborative, always-on partner.

Genesis Computing provides an enterprise AI platform built around autonomous “AI data agents” that automate complex data engineering and analytics workflows across an organization’s existing technology stack. It introduces a new category of AI knowledge workers that operate as autonomous agents capable of executing full data workflows rather than simply suggesting code or analysis. These agents can research data sources, ingest and transform datasets, map raw data from source systems to structured analytical targets, generate and run data pipeline code, create documentation, perform testing, and monitor pipelines in production environments. By handling these tasks end-to-end, the platform reduces the manual workload typically required to build and maintain data pipelines and analytics infrastructure.

Talend Data Integration lets you connect and manage all your data, no matter where it lives. Use more than 1,000 connectors and components to connect virtually any data source with virtually any data environment, in the cloud or on premises. Easily develop and deploy reusable data pipelines with a drag-and-drop interface that’s 10 times faster than hand-coding. Talend has always supported scaling massive data sets to advanced data analytics or Spark platforms. We also partner with leading cloud service providers, data warehouses, and analytics platforms, including Amazon Web Services, Microsoft Azure, Google Cloud Platform, Snowflake, and Databricks. With Talend, data quality is embedded into every step of the data integration processes. Discover, highlight, and fix issues as data moves through your systems, before inconsistencies can disrupt or impact crucial decisions. Connect to data where it lives, use it where you need it.

Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

3X Code Conversion is an AI-augmented data engineering accelerator that converts legacy SQL and ETL code into modern cloud platform code. The tool supports migrations from Teradata, Oracle, Netezza, SQL Server, MySQL, PostgreSQL, and Redshift to Snowflake, Databricks, BigQuery, Microsoft Fabric, Synapse, and other modern data platforms. It can ingest SQL scripts, stored procedures, ETL jobs, BTEQ macros, FastLoad jobs, SSIS pipelines, and undocumented legacy codebases. 3X Code Conversion uses deep code understanding, complexity scoring, agentic conversion, automated refactoring, testing, validation, and developer action items. The platform generates converted code, validation reports, standardized formatting, migration summaries, documentation, and AI-assisted code reviews. Built for enterprise cloud migrations, 3X Code Conversion helps data teams move legacy code to modern platforms in hours instead of months.

FeatureByte is your AI data scientist streamlining the entire lifecycle so that what once took months now happens in hours. Deployed natively on Databricks, Snowflake, BigQuery, or Spark, it automates feature engineering, ideation, cataloging, custom UDFs (including transformer support), evaluation, selection, historical backfill, deployment, and serving (online or batch), all within a unified platform. FeatureByte’s GenAI‑inspired agents, data, domain, MLOps, and data science agents interactively guide teams through data acquisition, quality, feature generation, model creation, deployment orchestration, and continued monitoring. FeatureByte’s SDK and intuitive UI enable automated and semi‑automated feature ideation, customizable pipelines, cataloging, lineage tracking, approval flows, RBAC, alerts, and version control, empowering teams to build, refine, document, and serve features rapidly and reliably.

Lakeflow Designer is no-code data prep, ready for production. It helps teams prepare and transform data with AI-first authoring, directly on Databricks. Modern data teams can keep no-code data work on Databricks, preparing and transforming data in natural language without introducing new tools, rework, or governance gaps. Lakeflow Designer brings Lakeflow power with no-code simplicity, giving users the ease of visual data prep with the power, scale, and governance of the central data platform. With Genie Code, teams can generate and refine transformations with awareness of the data’s schema, lineage, and business context, improving accuracy while reducing manual effort. It provides a simple path to production by letting users prepare data directly where it lives, with transformations that can be used in production with Lakeflow Jobs without rebuilding or translating logic across tools.

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.

Astrato Analytics is a warehouse-native business intelligence platform that allows organizations to build, embed, and share interactive dashboards and data applications directly on cloud data warehouses. It integrates with major platforms such as Snowflake, BigQuery, Databricks, ClickHouse, Supabase, Amazon Redshift, PostgreSQL, and Dremio. The platform uses a zero-copy architecture, enabling real-time data access without the need for data extraction or duplication. This approach ensures that dashboards and reports always reflect the most up-to-date information. Astrato connects through a live-query engine, allowing seamless interaction with source data. It eliminates the complexity of managing data pipelines or cached datasets. Security is enhanced by inheriting policies like row-level access and data masking directly from the warehouse. Overall, it simplifies analytics while maintaining strong governance and real-time insights.

Informatica’s CLAIRE AI is an enterprise-grade, metadata-driven artificial intelligence engine embedded within the Intelligent Data Management Cloud that automates and accelerates data management tasks to deliver accurate, trusted, and AI-ready data at scale. CLAIRE uses deep metadata insight to reduce manual effort, democratize access to data, and streamline processes across integration, quality, governance, master data management, and observability, supporting autonomous workflows with AI agents, natural language interaction, and proactive recommendations. It powers capabilities such as CLAIRE Agents, which independently plan, reason, and solve complex data challenges like discovery, pipeline generation, quality remediation, and lineage tracking; CLAIRE GPT, a conversational interface that lets users ask questions in natural language to discover, analyze, and execute data tasks; and CLAIRE Copilot, an AI assistant that provides contextual guidance and suggestions.

Prophecy is an AI-powered data preparation and analysis platform that enables business users to transform raw data into actionable insights through natural language prompts. The platform uses specialized AI agents to automatically generate visual, low-code data workflows that users can inspect, refine, validate, and deploy without requiring programming expertise. Prophecy connects directly to cloud data platforms such as Databricks, Snowflake, and BigQuery, allowing organizations to prepare, analyze, and govern data at enterprise scale. The platform combines AI-generated data pipelines with visual workflow interfaces, making complex data transformations easier to understand and manage. Users can automate data preparation, perform advanced analysis, create visualizations, and deploy production-ready workflows while maintaining governance and transparency.

1Platform is a data engineering, analytics, and AI/ML platform that helps organizations unify, prepare, and analyze data to generate business-ready intelligence and automated insights; it blends advanced data engineering with generative AI, autonomous agents, and business intelligence capabilities so enterprises can move from raw data to measurable outcomes and strategic decisions. It supports end-to-end data workflows, including data orchestration, analytics plays, AI assistants (agentic and generative), and pre-built machine learning models that accelerate insights across sales, supply chain, finance, and operations while making data governance, integration, and readiness easier. Polestar’s ecosystem also includes tools for AI-led decision support and analytics dashboards, and it emphasizes scalable, cloud-ready infrastructure that connects with major hyperscalers and partners like Databricks to build an AI-ready data foundation.

DataNimbus is an AI-powered platform that streamlines payments and accelerates AI adoption through innovative, cost-efficient solutions. By seamlessly integrating with Databricks components like Spark, Unity Catalog, and ML Ops, DataNimbus enhances scalability, governance, and runtime operations. Its offerings include a visual designer, a marketplace for reusable connectors and machine learning blocks, and agile APIs, all designed to simplify workflows and drive data-driven innovation.

IBM watsonx.data integration is a data integration platform designed to help organizations transform raw data into AI-ready data at scale. The platform enables data teams to build, manage, and optimize data pipelines across multiple environments, including on-premises systems and hybrid or multi-cloud infrastructures. With a unified control plane, watsonx.data integration supports multiple integration styles such as batch processing, real-time streaming, and data replication within a single solution. The platform also offers no-code, low-code, and pro-code development options, allowing both technical and non-technical users to design and manage data pipelines efficiently. By simplifying data integration workflows and reducing reliance on multiple tools, watsonx.data integration helps organizations deliver reliable data for analytics and AI applications.

Superblocks is a platform that enables businesses to build AI-powered enterprise applications on their company data. It allows non-technical teams to generate apps quickly while IT maintains control over security and governance. The platform integrates with data sources like Snowflake, Databricks, AWS, and Azure. Superblocks ensures that all apps follow centralized authentication, access control, and auditing policies. It acts as a secure layer between AI apps and business systems. Teams can create internal tools without heavy engineering involvement. Overall, it combines AI app development with enterprise-grade governance.

Capital One Slingshot is a cloud data platform optimization and management solution that helps organizations simplify, optimize, and maximize their use of Snowflake and Databricks by providing enhanced visibility into financial and compute spend, continuous monitoring, dynamic rightsizing, and AI-driven recommendations to reduce waste and inefficiencies while improving performance. It delivers granular dashboards and reports tracking cost, usage, and performance trends, allocates costs to business units with custom tagging, and offers proactive alerts for credit consumption and cost spikes. Slingshot’s recommendation engine analyzes workloads to right-size warehouses, suggests schedule adjustments, and highlights inefficient queries with its Query Advisor to improve SQL performance. It supports automated optimization for Databricks jobs using machine learning models and enables federated management and governance with customizable workflows and controls.

Accelerating insights, eliminating barriers for data analysts. Intelligent data stack automation, get insights from your data 10x faster with AI. Pioneered at the Stanford AI lab and now available to your enterprise, intelligence for the modern data stack has arrived. Use natural language to get value from your messy, complex, and siloed data in minutes. Tell your data your desired output, and immediately generate code for execution. Customizable automation of complex data tasks that are specific to your organization and not captured by templated solutions. Empower anyone to securely automate data-intensive workflows on the modern data stack, free data engineers from an endless backlog of requests. Arrive at insights in minutes, not months. Uniquely designed for you, tuned for your organization’s needs. Integrated with upstream and downstream tools, Snowflake, Databricks, Redshift, BigQuery, and more coming, built on dbt.

Espresso AI is a data-warehouse optimization system built to reduce the compute and query costs of platforms like Snowflake and Databricks SQL by deploying machine-learning agents that manage scaling, scheduling, and query rewriting in real time. It layers three core agents; an autoscaling agent that predicts workload spikes and minimizes idle compute, a scheduling agent that routes queries dynamically across clusters to maximize utilization and significantly reduce idle time, and a query agent that rewrites SQL using large language models combined with formal verification to ensure equivalent results while improving efficiency. It offers fast deployment (minutes rather than months) and a pricing model tied to savings, so that if it does not reduce your bill, you don’t pay. By automating hundreds of thousands of optimization decisions per day, Espresso AI provides dramatic cost reductions while enabling engineering teams to focus on value-add features.

Alkemi’s flagship product, DataLab, is a secure AI-native workspace that connects directly to your company’s governed data from sources like Snowflake, BigQuery, Databricks, or simple CSV uploads and lets users ask questions in plain English to get instant, transparent answers, charts, and recommendations with no SQL or analysts required. DataLab indexes and analyzes your data inside a private, secure environment so every insight is traceable and verifiable, and your data never leaves your control, protecting intellectual property and governance. It bridges the gap between complex data stores and everyday decision-making by combining the clarity of business intelligence with conversational AI to reduce BI backlogs and speed decisions for marketing, finance, product, sales, operations, and more. DataLab also enables data providers to turn datasets into interactive, AI-ready experiences that buyers can explore securely without exposing raw data, and accelerating data discovery.

MetricSign monitors your entire data stack and detects incidents before your stakeholders do. Connect Power BI via Microsoft OAuth in 2 minutes. MetricSign immediately starts detecting refresh failures, slow datasets, and missed schedules — classifying each with the exact error code and a root cause hint. Beyond Power BI, MetricSign monitors Azure Data Factory, Databricks, dbt Cloud, dbt Core, and Microsoft Fabric. When an ADF pipeline fails and cascades into a Power BI refresh failure, you get one incident — not five separate alerts from five different tools. Key capabilities: - Refresh failure detection with 98+ error code classifications - End-to-end lineage: source → pipeline → dataset → report - Slow refresh and missed schedule detection - Alerts via email, Telegram, webhook - Free plan available — no credit card required

Sync Computing offers Gradient, an AI-powered compute optimization engine designed to enhance data infrastructure efficiency. By leveraging advanced machine learning algorithms developed at MIT, Gradient provides automated optimization for organizations running data workloads on cloud-based CPUs or GPUs. Users can achieve up to 50% cost savings on their Databricks compute expenses while consistently meeting runtime service level agreements (SLAs). Gradient's continuous monitoring and fine-tuning capabilities ensure optimal performance across complex data pipelines, adapting seamlessly to varying data sizes and workload patterns. The platform integrates with existing data tools and supports multiple cloud providers, offering a comprehensive solution for managing and optimizing data infrastructure.

Agile Data Engine is a comprehensive DataOps platform designed to streamline the development, deployment, and operation of cloud-based data warehouses. It integrates data modeling, transformations, continuous deployment, workflow orchestration, monitoring, and API connectivity within a single SaaS solution. The platform's metadata-driven approach automates SQL code generation and data load workflows, enhancing productivity and agility in data operations. Supporting multiple cloud database platforms, including Snowflake, Databricks SQL, Amazon Redshift, Microsoft Fabric (Warehouse), Azure Synapse SQL, Azure SQL Database, and Google BigQuery, Agile Data Engine offers flexibility in cloud environments. Its modular data product framework and out-of-the-box CI/CD pipelines facilitate seamless integration and continuous delivery, enabling data teams to adapt swiftly to changing business requirements. The platform also provides insights and statistics on data platform performance.

DataForge is the only framework to cover all three major data components of data development By combining unique elements within each component with an overall approach, DataForge creates the best foundation for any data design. DataForge Cloud (DFC) is a fully-featured data platform management service built around DataForge It translates the framework into automated developer workflows and provides tools to execute processing on platforms like Databricks and Snowflake. Through a combination of code structure and an event-based workflow engine, DFC fully automates the defining of required processing steps and dependency management. With standardized processing steps comes predictable infrastructure sizing. DFC leverages dynamic allocation and the best cluster/warehouse option at every step to ensure the best performance per cost possible.

Databao is an AI-powered agentic analytics platform designed to help organizations connect databases, BI tools, documents, and spreadsheets into a governed semantic layer that enables reliable natural language querying and analytics. The platform allows technical and business users to ask questions in plain language and receive accurate, reproducible answers without relying on manual dashboard creation, SQL writing, or ad-hoc analytics requests. Databao includes open-source tools such as Context Engine, Data Agent, and an Analytics CLI that work together to generate semantic context from enterprise data sources, automate SQL generation, query multiple datasets, clean and visualize data, and orchestrate conversational analytics workflows. The platform supports local deployment within an organization’s environment and integrates with large language models to reduce SQL hallucinations, improve query accuracy, and streamline data workflows.

Orchestra is a Unified Control Plane for Data and AI Operations, designed to help data teams build, deploy, and monitor workflows with ease. It offers a declarative framework that combines code and GUI, allowing users to implement workflows 10x faster and reduce maintenance time by 50%. With real-time metadata aggregation, Orchestra provides full-stack data observability, enabling proactive alerting and rapid recovery from pipeline failures. It integrates seamlessly with tools like dbt Core, dbt Cloud, Coalesce, Airbyte, Fivetran, Snowflake, BigQuery, Databricks, and more, ensuring compatibility with existing data stacks. Orchestra's modular architecture supports AWS, Azure, and GCP, making it a versatile solution for enterprises and scale-ups aiming to streamline their data operations and build trust in their AI initiatives.

Wayfinder is the all-in-one, SaaS big data platform for healthcare and life sciences, unifying data, analytics, and AI workloads into an ecosystem that accelerates the time to insights for the healthcare and life sciences industries. Wayfinder makes it possible to find granular insights from healthcare data faster. Built on the Databricks Lakehouse platform, Wayfinder delivers access to over 45 terabytes of de-identified, remastered claims data and meets the unique data and processing needs of the healthcare and life sciences industries at scale. With Wayfinder, you can analyze higher-quality claims data to identify rare patients, and target providers, build comprehensive patient journeys, and identify market trends, all with granular detail to inform strategies that drive differentiation and growth. Wayfinder is your big data infrastructure, so you can stop preparing and managing your data and start analyzing.

Zingg is an open source Master Data Management platform built for the modern data warehouse. It uses machine learning for entity resolution at scale — eliminating the need for expensive, rigid MDM tools. Native to Databricks, Microsoft Fabric, Snowflake, AWS, and GCP, Zingg helps data teams build a single, trusted view of customers, suppliers, and products — right where their data already lives. Golden records are maintained through a persistent Zingg ID across all your systems and sources.

Mitzu is an agentic analytics platform that runs your analytics directly on your data warehouse — no data copying, no reverse ETL, no SQL required. Its AI analytics agent answers business questions autonomously: it maps your schema, writes and executes queries on Snowflake, BigQuery, Redshift, Databricks, or ClickHouse, and returns explainable results with full SQL visibility. Teams get instant access to funnels, retention, cohorts, user journeys, and revenue metrics. Mitzu also proactively monitors KPIs and sends anomaly alerts via Slack or email. Available as SaaS, BYOC, or fully self-hosted. Free trial available at mitzu.io

Hackolade Studio is a powerful data modeling platform that supports a wide range of technologies including relational SQL and NoSQL databases, cloud data warehouses, APIs, streaming platforms, and data exchange formats. Designed for modern data architecture, it enables users to visually design, document, and evolve schemas across systems like Oracle, PostgreSQL, Databricks, Snowflake, MongoDB, Cassandra, DynamoDB, Neo4j, Kafka (with Confluent Schema Registry), OpenAPI, GraphQL, and more. Hackolade Studio offers forward and reverse engineering, schema versioning, model validation, and integration with metadata catalogs such as Unity Catalog and Collibra. It empowers data architects, engineers, and governance teams to collaborate on consistent, governed, and scalable data models. Whether building data products, managing API contracts, or ensuring regulatory compliance, Hackolade Studio streamlines the process in one unified interface.

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.

Zipher is an autonomous optimization platform specifically designed to improve the performance and cost efficiency of Databricks workloads by eliminating manual tuning and resource management and continuously adjusting clusters in real time. It uses proprietary machine learning models and the only Spark-aware scaler that actively learns and profiles workloads to adjust cluster resources, select optimal configurations for every job run, and dynamically tune settings like hardware, Spark configs, and availability zones to maximize efficiency and cut waste. Zipher continuously monitors evolving workloads to adapt configurations, optimize scheduling, and allocate shared compute resources to meet SLAs, while providing detailed cost visibility that breaks down Databricks and cloud provider costs so teams can identify key cost drivers. It integrates seamlessly with major cloud service providers including AWS, Azure, and Google Cloud and works with common orchestration and IaC tools.

Compass is an AI-powered, Slack-native data assistant that turns plain English questions into instant answers, summaries, charts, and insights powered by your actual warehouse data, so teams can make data-driven decisions without waiting on BI backlogs or building dashboards first. It connects directly to major data warehouses (Snowflake, BigQuery, Redshift, Postgres, AWS Athena, Databricks, and more), learns your schema and context, and generates governed, SQL-backed responses and visualizations in the tools your team already uses, all while keeping your data where it lives and under your control. Compass builds organizational context over time so answers become more accurate and relevant, supports collaboration through Slack threads, can schedule recurring analysis, and provides a shared repository of definitions and insights that help reduce analytical silos and reliance on specialized SQL users.

Cloudgov.ai is an agentic AI FinOps platform for continuous cost and policy governance across cloud, multicloud, data, container, and AI environments. It brings AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into one control plane, giving teams a live view of cost, allocation, policy, and risk. Continuous Multicloud Observability connects accounts, analyzes historical spending, filters costs by region, account, and service, and forecasts future spend from history. AI-driven insights identify waste and optimization opportunities, while anomaly detection highlights unexpected spending surges and their financial impact. Ready-to-use Infrastructure as Code remediation snippets help engineering teams apply recommended changes, and Jira integration turns insights and anomalies into assignable work.

An AI-powered SAP S/4HANA transformation accelerator that helps enterprises migrate, modernize, and optimize their ERP with speed, precision, and measurable business outcomes. Combining automation, process intelligence, and the SAP–Databricks partnership, Ascend ensures a smooth, value-driven migration journey from ECC to S/4HANA.

Assign and take action on KPI analysis, trends, and exceptions and ensure timely offline data collection with a configurable workflow and notification engine. Connect to any other third-party platform or data source, such as Databricks, Snowflake, Azure, SharePoint, Google Docs, AWS, business data sets, and more. Enrich KPIs with macroeconomic and market data, then link them to strategic objectives, business goals, risks, and even risk appetite levels to quickly visualize emerging issues and predict KPI performance. Our AI agent, Kaia™, can help you to analyze enrichment data sources and suggest recommendations for KPIs. Lumify360’s native Power BI integration empowers organizations to easily port visualizations and data sets into their internal Office applications and keep reports refreshed. And with Kaia, Lumify360’s integrated AI companion, all users can instantly get the insights they need or dig deeper into analytics without relying on data analysts or subject matter experts.

Impetus Technologies enables the Intelligent Enterprise™ with innovative data engineering, cloud, and enterprise AI services. Recognized as an AWS Advanced Consulting Partner, Elite Databricks Consulting Partner, Data & AI Solutions Microsoft Partner, and Elite Snowflake Services Partner, Impetus offers a comprehensive suite of cutting-edge IT services and solutions to drive innovation and transformation for businesses across various industries. With a proven track record with Fortune 500 clients, Impetus drives growth, enhances efficiency, and ensures a competitive edge through continuous innovation and flawless, zero-defect delivery.

SupportLogic delivers a Cognitive AI Cloud purpose-built for enterprise customer service and support. It ingests unstructured signals from tickets, chat, voice, and email, then uses AI to detect urgency, sentiment, product issues, and revenue risk in real time. Its layered architecture—data extraction, signal detection, and a context engine—powers ambient AI agents that automate tasks like case summarization, escalation prediction, routing, account health scoring, and coaching. SupportLogic integrates with Salesforce, Zendesk, Snowflake, and Slack, enhancing existing systems rather than replacing them. The platform helps CX, support, and IT teams act earlier, coach smarter, reduce escalations, and improve resolution times. Built for enterprise scale, it offers SOC 2 certification, GDPR/CCPA compliance, and secure data isolation. Customers like Salesforce, NICE, and Databricks use SupportLogic to boost CSAT, retention, and operational efficiency.

Data 360 is Salesforce’s next-generation data platform, evolving from Data Cloud to unify and activate enterprise data in real time. It connects fragmented data across systems into a single, trusted Customer 360 view without requiring data movement. Through Zero-Copy integrations, Data 360 works directly with platforms like Snowflake, Databricks, BigQuery, and AWS. The platform harmonizes structured and unstructured data to power personalized experiences and intelligent workflows. Built-in identity resolution and governance tools ensure data accuracy, compliance, and privacy. Data 360 enables real-time segmentation, predictive insights, and triggered automation across Salesforce and external applications. It serves as the data foundation for Agentforce, delivering context-rich intelligence to AI agents and business teams.

Bigeye is the data observability platform that helps teams measure, improve, and communicate data quality clearly at any scale. Every time a data quality issue causes an outage, the business loses trust in the data. Bigeye helps rebuild trust, starting with monitoring. Find missing and busted reporting data before executives see it in a dashboard. Get warned about issues in training data before models get retrained on it. Fix that uncomfortable feeling that most of the data is mostly right, most of the time. Pipeline job statuses don't tell the whole story. The best way to ensure data is fit for use, is to monitor the actual data. Tracking dataset-level freshness ensures pipelines are running on schedule, even when ETL orchestrators go down. Find out about changes to event names, region codes, product types, and other categorical data. Detect drops or spikes in row counts, nulls, and blank values to ensure everything is populating as expected.

Mavvrik is an AI and hybrid infrastructure cost management platform that gives finance, FinOps, IT, and engineering teams one control center for GenAI, autonomous agents, GPUs, cloud, on-premises systems, Kubernetes, data platforms, and SaaS. It unifies cost, usage, and telemetry signals from AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, creating a single source of truth across the technology stack. Teams can track every model call, agent interaction, GPU hour, workload, service, and resource, then allocate spending by customer, product, feature, project, application, environment, team, or cost center. Cost-to-serve and unit-economics analysis reveal margin drains, expensive workloads, and the true cost of delivering each offering. Real-time anomaly detection and alerts identify usage before it becomes a budget surprise, while predictive forecasting helps organizations model cloud, GPU, and AI expenses.

Redpanda is an enterprise data streaming platform designed to make AI agents safe, governed, and effective across all organizational data. Its Agentic Data Plane connects agents to data sources across cloud, on-prem, and hybrid environments without creating risk or chaos. Redpanda unifies live data streams and historical data into a single, queryable layer. Built-in governance ensures every agent action is authorized, logged, and auditable. The platform enables agents to retrieve exactly the data they need with full context. Redpanda records and replays all agent activity for transparency and debugging. It helps enterprises move from experimental AI to production-ready agentic systems.

nao is an AI-powered data IDE designed specifically for data teams, combining a code editor with native integration to your data warehouse so you can write, test, and maintain data-centric code with full context. It supports warehouses such as Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift. Once connected, nao replaces a traditional data-warehouse console by offering schema-aware SQL auto-completion, data previews, SQL worksheets, and the ability to switch easily between multiple warehouses. The core of nao is its AI agent, which has full awareness of your actual data schema, tables, columns, metadata, and your codebase or data-stack context. It can generate SQL queries or full data-transformation models (e.g., for dbt workflows), refactor code, add or update documentation, run data-quality checks and data-diff tests, and even surface insights or run exploratory analytics, all while respecting data structure and quality constraints.

Precisely Data Integrity Suite is a modular, interoperable cloud-based platform that provides a comprehensive set of services to ensure data is accurate, consistent, and enriched with meaningful context across an organization. It is designed as a unified solution that connects multiple data integrity capabilities, including data integration, data quality, data governance, data observability, geo addressing, spatial analytics, and data enrichment, all working together through a central Data Integrity Foundation. It enables businesses to break down data silos by building scalable data pipelines, monitoring data health to proactively detect anomalies, and governing data with visibility into lineage, policies, and relationships. It also enhances data usability by verifying, cleansing, and enriching datasets with additional contextual information, including location intelligence and curated external data sources, allowing organizations to uncover patterns.