Alternatives to Iterative
Compare Iterative alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Iterative in 2026. Compare features, ratings, user reviews, pricing, and more from Iterative competitors and alternatives in order to make an informed decision for your business.
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Teradata VantageCloud
Teradata
Teradata VantageCloud: The complete cloud analytics and data platform for AI. Teradata VantageCloud is an enterprise-grade, cloud-native data and analytics platform that unifies data management, advanced analytics, and AI/ML capabilities in a single environment. Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems. -
2
Google Cloud BigQuery
Google
BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven. Gemini in BigQuery offers AI-driven tools for assistance and collaboration, such as code suggestions, visual data preparation, and smart recommendations designed to boost efficiency and reduce costs. BigQuery delivers an integrated platform featuring SQL, a notebook, and a natural language-based canvas interface, catering to data professionals with varying coding expertise. This unified workspace streamlines the entire analytics process. -
3
dbt
dbt Labs
dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow. Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence. -
4
AnalyticsCreator
AnalyticsCreator
AnalyticsCreator is a metadata-driven data warehouse automation application for teams working in the Microsoft data ecosystem. It enables data engineers to design, generate, and maintain production-ready data products across Microsoft SQL Server, Azure Data Factory, and Microsoft Fabric. By using centralized metadata, AnalyticsCreator generates ELT pipelines, dimensional models, historization logic, and analytical models in a consistent, version-controlled way. This reduces manual implementation effort and tool sprawl while ensuring transparency through built-in lineage tracking and clear visibility into data dependencies and change impact. With CI/CD integration via Azure DevOps and GitHub, plus support for custom SQL, AnalyticsCreator helps data teams scale delivery, enforce standards, and maintain control as complexity grows. -
5
Looker
Google
Looker, Google Cloud’s business intelligence platform, enables you to chat with your data. Organizations turn to Looker for self-service and governed BI, to build custom applications with trusted metrics, or to bring Looker modeling to their existing environment. The result is improved data engineering efficiency and true business transformation. Looker is reinventing business intelligence for the modern company. Looker works the way the web does: browser-based, its unique modeling language lets any employee leverage the work of your best data analysts. Operating 100% in-database, Looker capitalizes on the newest, fastest analytic databases—to get real results, in real time. -
6
Fivetran
Fivetran
Fivetran is a leading data integration platform that centralizes an organization’s data from various sources to enable modern data infrastructure and drive innovation. It offers over 700 fully managed connectors to move data automatically, reliably, and securely from SaaS applications, databases, ERPs, and files to data warehouses and lakes. The platform supports real-time data syncs and scalable pipelines that fit evolving business needs. Trusted by global enterprises like Dropbox, JetBlue, and Pfizer, Fivetran helps accelerate analytics, AI workflows, and cloud migrations. It features robust security certifications including SOC 1 & 2, GDPR, HIPAA, and ISO 27001. Fivetran provides an easy-to-use, customizable platform that reduces engineering time and enables faster insights. -
7
Composable DataOps Platform
Composable Analytics
Composable is an enterprise-grade DataOps platform built for business users that want to architect data intelligence solutions and deliver operational data-driven products leveraging disparate data sources, live feeds, and event data regardless of the format or structure of the data. With a modern, intuitive dataflow visual designer, built-in services to facilitate data engineering, and a composable architecture that enables abstraction and integration of any software or analytical approach, Composable is the leading integrated development environment to discover, manage, transform and analyze enterprise data.Starting Price: $8/hr - pay-as-you-go -
8
Dremio
Dremio
Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable. -
9
Vaex
Vaex
At Vaex.io we aim to democratize big data and make it available to anyone, on any machine, at any scale. Cut development time by 80%, your prototype is your solution. Create automatic pipelines for any model. Empower your data scientists. Turn any laptop into a big data powerhouse, no clusters, no engineers. We provide reliable and fast data driven solutions. With our state-of-the-art technology we build and deploy machine learning models faster than anyone on the market. Turn your data scientist into big data engineers. We provide comprehensive training of your employees, enabling you to take full advantage of our technology. Combines memory mapping, a sophisticated expression system, and fast out-of-core algorithms. Efficiently visualize and explore big datasets, and build machine learning models on a single machine. -
10
datuum.ai
Datuum
AI-powered data integration tool that helps streamline the process of customer data onboarding. It allows for easy and fast automated data integration from various sources without coding, reducing preparation time to just a few minutes. With Datuum, organizations can efficiently extract, ingest, transform, migrate, and establish a single source of truth for their data, while integrating it into their existing data storage. Datuum is a no-code product and can reduce up to 80% of the time spent on data-related tasks, freeing up time for organizations to focus on generating insights and improving the customer experience. With over 40 years of experience in data management and operations, we at Datuum have incorporated our expertise into the core of our product, addressing the key challenges faced by data engineers and managers and ensuring that the platform is user-friendly, even for non-technical specialists. -
11
DataLakeHouse.io
DataLakeHouse.io
DataLakeHouse.io (DLH.io) Data Sync provides replication and synchronization of operational systems (on-premise and cloud-based SaaS) data into destinations of their choosing, primarily Cloud Data Warehouses. Built for marketing teams and really any data team at any size organization, DLH.io enables business cases for building single source of truth data repositories, such as dimensional data warehouses, data vault 2.0, and other machine learning workloads. Use cases are technical and functional including: ELT, ETL, Data Warehouse, Pipeline, Analytics, AI & Machine Learning, Data, Marketing, Sales, Retail, FinTech, Restaurant, Manufacturing, Public Sector, and more. DataLakeHouse.io is on a mission to orchestrate data for every organization particularly those desiring to become data-driven, or those that are continuing their data driven strategy journey. DataLakeHouse.io (aka DLH.io) enables hundreds of companies to managed their cloud data warehousing and analytics solutions.Starting Price: $99 -
12
Delta Lake
Delta Lake
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. Data lakes typically have multiple data pipelines reading and writing data concurrently, and data engineers have to go through a tedious process to ensure data integrity, due to the lack of transactions. Delta Lake brings ACID transactions to your data lakes. It provides serializability, the strongest level of isolation level. Learn more at Diving into Delta Lake: Unpacking the Transaction Log. In big data, even the metadata itself can be "big data". Delta Lake treats metadata just like data, leveraging Spark's distributed processing power to handle all its metadata. As a result, Delta Lake can handle petabyte-scale tables with billions of partitions and files at ease. Delta Lake provides snapshots of data enabling developers to access and revert to earlier versions of data for audits, rollbacks or to reproduce experiments. -
13
Feast
Tecton
Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution. -
14
Cloudflare R2
Cloudflare
Cloudflare R2 is a global object storage service that allows developers to store large amounts of unstructured data without the costly egress bandwidth fees associated with typical cloud storage services. It supports multiple scenarios, including storage for cloud-native applications, web content, podcast episodes, data lakes, and outputs for large batch processes such as machine learning model artifacts or datasets. R2 offers features like location hints to optimize data access, CORS configuration for interacting with objects, public buckets to expose contents directly to the Internet, and bucket-scoped tokens for granular access control. It integrates with Cloudflare Workers, enabling developers to perform authentication, route requests, and deploy edge functions across a network of over 330 data centers. Additionally, R2 supports Apache Iceberg through its data catalog, transforming object storage into a fully functional data warehouse without management overhead.Starting Price: $0.015 per GB -
15
ClearML
ClearML
ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.Starting Price: $15 -
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Data Lakes on AWS
Amazon
Many Amazon Web Services (AWS) customers require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is a new and increasingly popular way to store and analyze data because it allows companies to manage multiple data types from a wide variety of sources, and store this data, structured and unstructured, in a centralized repository. The AWS Cloud provides many of the building blocks required to help customers implement a secure, flexible, and cost-effective data lake. These include AWS managed services that help ingest, store, find, process, and analyze both structured and unstructured data. To support our customers as they build data lakes, AWS offers the data lake solution, which is an automated reference implementation that deploys a highly available, cost-effective data lake architecture on the AWS Cloud along with a user-friendly console for searching and requesting datasets. -
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NAVIK AI Platform
Absolutdata Analytics
An Advanced Analytics Software Platform That Helps Sales, Marketing, Technology, and Operations Leaders Make Great Business Decisions Based on Powerful Data-Driven Insights. Addresses the breadth of AI needs across data infrastructure, data engineering and data analytics. UI, workflows and proprietary algorithms are tuned to the unique needs of each client. Components are modular enabling custom configurations. Supports, augments and automates decision making. Elimination of human biases drives better business outcomes. The AI adoption rate is unprecedented. To stay competitive, leading companies need a rapid implementation strategy that scales. To create scalable business impact, combine these four distinct capabilities. -
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Microsoft Fabric
Microsoft
Reshape how everyone accesses, manages, and acts on data and insights by connecting every data source and analytics service together—on a single, AI-powered platform. All your data. All your teams. All in one place. Establish an open and lake-centric hub that helps data engineers connect and curate data from different sources—eliminating sprawl and creating custom views for everyone. Accelerate analysis by developing AI models on a single foundation without data movement—reducing the time data scientists need to deliver value. Innovate faster by helping every person in your organization act on insights from within Microsoft 365 apps, such as Microsoft Excel and Microsoft Teams. Responsibly connect people and data using an open and scalable solution that gives data stewards additional control with built-in security, governance, and compliance.Starting Price: $156.334/month/2CU -
19
Archon Data Store
Platform 3 Solutions
Archon Data Store is a next-generation enterprise data archiving platform designed to help organizations manage rapid data growth, reduce legacy application costs, and meet global compliance standards. Built on a modern Lakehouse architecture, Archon Data Store unifies data lakes and data warehouses to deliver secure, scalable, and analytics-ready archival storage. The platform supports on-premise, cloud, and hybrid deployments with AES-256 encryption, audit trails, metadata governance, and role-based access control. Archon Data Store offers intelligent storage tiering, high-performance querying, and seamless integration with BI tools. It enables efficient application decommissioning, cloud migration, and digital modernization while transforming archived data into a strategic asset. With Archon Data Store, organizations can ensure long-term compliance, optimize storage costs, and unlock AI-driven insights from historical data. -
20
Databricks
Databricks
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. -
21
Innodata
Innodata
We Make Data for the World's Most Valuable Companies Innodata solves your toughest data engineering challenges using artificial intelligence and human expertise. Innodata provides the services and solutions you need to harness digital data at scale and drive digital disruption in your industry. We securely and efficiently collect & label your most complex and sensitive data, delivering near-100% accurate ground truth for AI and ML models. Our easy-to-use API ingests your unstructured data (such as contracts and medical records) and generates normalized, schema-compliant structured XML for your downstream applications and analytics. We ensure that your mission-critical databases are accurate and always up-to-date. -
22
Chalk
Chalk
Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.Starting Price: Free -
23
Alibaba Cloud Data Lake Formation
Alibaba Cloud
A data lake is a centralized repository used for big data and AI computing. It allows you to store structured and unstructured data at any scale. Data Lake Formation (DLF) is a key component of the cloud-native data lake framework. DLF provides an easy way to build a cloud-native data lake. It seamlessly integrates with a variety of compute engines and allows you to manage the metadata in data lakes in a centralized manner and control enterprise-class permissions. Systematically collects structured, semi-structured, and unstructured data and supports massive data storage. Uses an architecture that separates computing from storage. You can plan resources on demand at low costs. This improves data processing efficiency to meet the rapidly changing business requirements. DLF can automatically discover and collect metadata from multiple engines and manage the metadata in a centralized manner to solve the data silo issues. -
24
QFlow.ai
QFlow.ai
The machine learning platform that unifies data, orchestrates intelligent behavior across revenue-generating teams, and delivers out-of-the-box attribution & actionable analytics. QFlow.ai processes the gigabytes of data that your Salesforce.com instance is collecting in its activity table. We normalize, trend, and analyze sales effort to help you generate more opportunities and win more deals. QFlow.ai uses data engineering to break down outbound activity reporting based on a crucial factor: whether or not they were productive. It also automatically surfaces critical metrics like average days from first activity to opp creation and average days from opp creation to close. Sales Effort data can be filtered by team or by an individual to understand sales activity, and productivity trends over time.Starting Price: $699 per month -
25
SelectDB
SelectDB
SelectDB is a modern data warehouse based on Apache Doris, which supports rapid query analysis on large-scale real-time data. From Clickhouse to Apache Doris, to achieve the separation of the lake warehouse and upgrade to the lake warehouse. The fast-hand OLAP system carries nearly 1 billion query requests every day to provide data services for multiple scenes. Due to the problems of storage redundancy, resource seizure, complicated governance, and difficulty in querying and adjustment, the original lake warehouse separation architecture was decided to introduce Apache Doris lake warehouse, combined with Doris's materialized view rewriting ability and automated services, to achieve high-performance data query and flexible data governance. Write real-time data in seconds, and synchronize flow data from databases and data streams. Data storage engine for real-time update, real-time addition, and real-time pre-polymerization.Starting Price: $0.22 per hour -
26
Stardog
Stardog Union
With ready access to the richest flexible semantic layer, explainable AI, and reusable data modeling, data engineers and scientists can be 95% more productive — create and expand semantic data models, understand any data interrelationship, and run federated queries to speed time to insight. Stardog offers the most advanced graph data virtualization and high-performance graph database — up to 57x better price/performance — to connect any data lakehouse, warehouse or enterprise data source without moving or copying data. Scale use cases and users at lower infrastructure cost. Stardog’s inference engine intelligently applies expert knowledge dynamically at query time to uncover hidden patterns or unexpected insights in relationships that enable better data-informed decisions and business outcomes.Starting Price: $0 -
27
SiaSearch
SiaSearch
We want ML engineers to worry less about data engineering and focus on what they love, building better models in less time. Our product is a powerful framework that makes it 10x easier and faster for developers to explore, understand and share visual data at scale. Automatically create custom interval attributes using pre-trained extractors or any other model. Visualize data and analyze model performance using custom attributes combined with all common KPIs. Use custom attributes to query, find rare edge cases and curate new training data across your whole data lake. Easily save, edit, version, comment and share frames, sequences or objects with colleagues or 3rd parties. SiaSearch, a data management platform that automatically extracts frame-level, contextual metadata and utilizes it for fast data exploration, selection and evaluation. Automating these tasks with metadata can more than double engineering productivity and remove the bottleneck to building industrial AI. -
28
Azure Data Lake
Microsoft
Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. It removes the complexities of ingesting and storing all of your data while making it faster to get up and running with batch, streaming, and interactive analytics. Azure Data Lake works with existing IT investments for identity, management, and security for simplified data management and governance. It also integrates seamlessly with operational stores and data warehouses so you can extend current data applications. We’ve drawn on the experience of working with enterprise customers and running some of the largest scale processing and analytics in the world for Microsoft businesses like Office 365, Xbox Live, Azure, Windows, Bing, and Skype. Azure Data Lake solves many of the productivity and scalability challenges that prevent you from maximizing the -
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Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
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Qubole
Qubole
Qubole is a simple, open, and secure Data Lake Platform for machine learning, streaming, and ad-hoc analytics. Our platform provides end-to-end services that reduce the time and effort required to run Data pipelines, Streaming Analytics, and Machine Learning workloads on any cloud. No other platform offers the openness and data workload flexibility of Qubole while lowering cloud data lake costs by over 50 percent. Qubole delivers faster access to petabytes of secure, reliable and trusted datasets of structured and unstructured data for Analytics and Machine Learning. Users conduct ETL, analytics, and AI/ML workloads efficiently in end-to-end fashion across best-of-breed open source engines, multiple formats, libraries, and languages adapted to data volume, variety, SLAs and organizational policies. -
31
biGENIUS
biGENIUS AG
biGENIUS automates the entire lifecycle of analytical data management solutions (e.g. data warehouses, data lakes, data marts, real-time analytics, etc.) and thus providing the foundation for turning your data into business as fast and cost-efficient as possible. Save time, efforts and costs to build and maintain your data analytics solutions. Integrate new ideas and data into your data analytics solutions easily. Benefit from new technologies thanks to the metadata-driven approach. Advancing digitalization challenges traditional data warehouse (DWH) and business intelligence systems to leverage an increasing wealth of data. To accommodate today’s business decision making, analytical data management is required to integrate new data sources, support new data formats as well as technologies and deliver effective solutions faster than ever before, ideally with limited resources.Starting Price: 833CHF/seat/month -
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ELCA Smart Data Lake Builder
ELCA Group
Classical Data Lakes are often reduced to basic but cheap raw data storage, neglecting significant aspects like transformation, data quality and security. These topics are left to data scientists, who end up spending up to 80% of their time acquiring, understanding and cleaning data before they can start using their core competencies. In addition, classical Data Lakes are often implemented by separate departments using different standards and tools, which makes it harder to implement comprehensive analytical use cases. Smart Data Lakes solve these various issues by providing architectural and methodical guidelines, together with an efficient tool to build a strong high-quality data foundation. Smart Data Lakes are at the core of any modern analytics platform. Their structure easily integrates prevalent Data Science tools and open source technologies, as well as AI and ML. Their storage is cheap and scalable, supporting both unstructured data and complex data structures.Starting Price: Free -
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Bobsled
Bobsled
Deliver data into your consumer’s cloud data lake or warehouse—without ever leaving your own. Connect Bobsled to your source, pick the bucket or warehouse where you want the data to go—and Bobsled handles the rest. No accounts to manage or pipelines to build.Bobsled is built on each platform’s sharing protocol to bring data providers the security and ease of modern sharing without the pain and complexity of multi-cloud management. Data integration accounts for 70% of the time teams spend with external datasets. Empower your customers to get to analysis faster by sharing ready-to-query data directly into the platforms where they work. Track and manage every share from one interface. Initiate shares, automate transfers, resolve errors, and monitor usage. -
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AI Verse
AI Verse
When real-life data capture is challenging, we generate diverse, fully labeled image datasets. Our procedural technology ensures the highest quality, unbiased, labeled synthetic datasets that will improve your computer vision model’s accuracy. AI Verse empowers users with full control over scene parameters, ensuring you can fine-tune the environments for unlimited image generation, giving you an edge in the competitive landscape of computer vision development. -
35
SmartGit
syntevo
SmartGit supports GitHub, Bitbucket, GitLab and Azure DevOps. SmartGit assists Git newbies as well as it makes experienced developers more productive. SmartGit has the same intuitive user interface on Windows, macOS and Linux: - graphical merge and commit history - drag and drop commit reordering, merging or rebase - fast, even for larger repositories Use your SmartGit license on as many machines and operating systems you like. SmartGit comes with special integrations for GitHub, Azure DevOps, BitBucket (as well BitBucket Server) and GitLab to create and resolve Pull Requests and Review Comments. Of course, you can use SmartGit like any other Git client with your own Git repositories or other hosting providers.Starting Price: $59 per year -
36
Scraawl
Scraawl
Scraawl is a suite of data analytics tools designed to empower you to gain more from your data. Whether your problem set focuses on publicly available data, images and video, unstructured text, or all of the above, Scraawl has powerful tools to enhance your analyses. Scraawl leverages state-of-the-art artificial intelligence and machine learning techniques to provide actionable insights through analytics. Our team is a multi-disciplinary group of developers, researchers, and data scientists dedicated to bringing cutting edge analytics to users. Scraawl SocL® is an enterprise-level, easy-to-use, web-based PAI listening and analytics tool. Scraawl SocL® searches, analyzes, and visualizes online conversations and news data, providing a user with a detailed 360-degree analysis. -
37
Aquarium
Aquarium
Aquarium's embedding technology surfaces the biggest problems in your model performance and finds the right data to solve them. Unlock the power of neural network embeddings without worrying about maintaining infrastructure or debugging embedding models. Automatically find the most critical patterns of model failures in your dataset. Understand the long tail of edge cases and triage which issues to solve first. Trawl through massive unlabeled datasets to find edge-case scenarios. Bootstrap new classes with a handful of examples using few-shot learning technology. The more data you have, the more value we offer. Aquarium reliably scales to datasets containing hundreds of millions of data points. Aquarium offers solutions engineering resources, customer success syncs, and user training to help customers get value. We also offer an anonymous mode for organizations who want to use Aquarium without exposing any sensitive data.Starting Price: $1,250 per month -
38
Accern
Accern
The Accern No-Code NLP Platform empowers domain experts and business analysts to extract the most accurate insights from massive streams of unstructured data–including news, social media, industry reports and internal documents—within minutes. Accern offers pre-built AI/ML/NLP solutions to minimize time to value and maximize ROI for equity research, credit risk, M&A activity, ESG performance, insurance claims, fraud prevention, sanctions monitoring and more. Recognized as the first No-Code NLP platform and industry leader with the highest accuracy scores, Accern also enables data scientists to customize end-to-end AI/ML/NLP workflows with BYO datasets, taxonomies, models and pre-integrated dashboards and DSML platforms. In production at companies like Allianz, William Blair and Mizuho Bank, Accern accelerates innovation by enhancing existing models and enriching BI dashboards. -
39
Learn how CloudWorx for Intergraph Smart 3D connects to the point cloud and enables users to make the hybrid between the existing plant and newly modeled parts. Intergraph Smart® Laser Data Engineer provides state-of-the-art point cloud rendering performance in CloudWorx for Intergraph Smart 3D users via the JetStream point cloud engine. With its instant loading and persistent full rendering of the point cloud during user actions – regardless of the size of the dataset – Smart Laser Data Engineer delivers ultimate fidelity to the user. JetStream’s centralized data storage and administrative architecture – while serving the high-performance point cloud to users – also provides an easy-to-use project environment, making data distribution, user access control, backups and other IT functions easy and effective, saving time and money.
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40
SplineCloud
SplineCloud
SplineCloud is an open knowledge management platform designed to facilitate the discovery, formalization, and exchange of structured and reusable knowledge in science and engineering. It enables users to organize data into structured repositories, making it findable and accessible. The platform offers tools such as an online plot digitizer for extracting data from graphs and an interactive curve fitting tool that allows users to define functional relationships in datasets using smooth spline functions. Users can also reuse datasets and relations in their models and calculations by accessing them directly through the SplineCloud API or by utilizing open source client libraries for Python and MATLAB. The platform supports the development of reusable engineering and analytical applications, aiming to reduce redundancy in design processes, preserve expert knowledge, and facilitate better decision-making. -
41
Grooper
BIS
Grooper was built from the ground up by BIS, a company with 35 years of continuous experience developing and delivering new technology. Grooper is an intelligent document processing and digital data integration solution that empowers organizations to extract meaningful information from paper/electronic documents and other forms of unstructured data. The platform combines patented and sophisticated image processing, capture technology, machine learning, natural language processing, and optical character recognition to enrich and embed human comprehension into data. By tackling tough challenges that other systems cannot resolve, Grooper has become the foundation for many industry-first solutions in healthcare, financial services, oil and gas, education, and government. -
42
Kodex
Kodex
Privacy engineering is an emerging field that has intersections with data engineering, information security, software development, and privacy law. Its goal is to ensure that personal data is stored and processed in a legally compliant way that respects and protects the privacy of the individuals this data belongs in the best possible way. Security engineering is on one hand a requirement for privacy engineering but also an independent discipline that aims to guarantee the secure processing and storage of sensitive data in general. If your organization processes data that is either sensitive or personal (or both), you need privacy & security engineering. This is especially true if you do your own data engineering or data science. -
43
Hugging Face
Hugging Face
Hugging Face is a leading platform for AI and machine learning, offering a vast hub for models, datasets, and tools for natural language processing (NLP) and beyond. The platform supports a wide range of applications, from text, image, and audio to 3D data analysis. Hugging Face fosters collaboration among researchers, developers, and companies by providing open-source tools like Transformers, Diffusers, and Tokenizers. It enables users to build, share, and access pre-trained models, accelerating AI development for a variety of industries.Starting Price: $9 per month -
44
Lentiq
Lentiq
Lentiq is a collaborative data lake as a service environment that’s built to enable small teams to do big things. Quickly run data science, machine learning and data analysis at scale in the cloud of your choice. With Lentiq, your teams can ingest data in real time and then process, clean and share it. From there, Lentiq makes it possible to build, train and share models internally. Simply put, data teams can collaborate with Lentiq and innovate with no restrictions. Data lakes are storage and processing environments, which provide ML, ETL, schema-on-read querying capabilities and so much more. Are you working on some data science magic? You definitely need a data lake. In the Post-Hadoop era, the big, centralized data lake is a thing of the past. With Lentiq, we use data pools, which are multi-cloud, interconnected mini-data lakes. They work together to give you a stable, secure and fast data science environment. -
45
Datactics
Datactics
Profile, cleanse, match and deduplicate data in drag-and-drop rules studio. Lo-code UI means no programming skill required, putting power in the hands of subject matter experts. Add AI & machine learning to your existing data management processes In order to reduce manual effort and increase accuracy, providing full transparency on machine-led decisions with human-in-the-loop. Offering award-winning data quality and matching capabilities across multiple industries, our self-service solutions are rapidly configured within weeks with specialist assistance available from Datactics data engineers. With Datactics you can easily measure data to regulatory & industry standards, fix breaches in bulk and push into reporting tools, with full visibility and audit trail for Chief Risk Officers. Augment data matching into Legal Entity Masters for Client Lifecycle Management. -
46
DataSentics
DataSentics
Making data science & machine learning have a real impact on organizations. We are an AI product studio, a group of 100 experienced data scientists and data engineers with a combination of experience both from the agile world of digital start-ups as well as major international corporations. We don’t end with nice slides and dashboards. The result that counts is an automated data solution in production integrated inside a real process. We do not report clickers but data scientists and data engineers. We have a strong focus on productionalizing data science solutions in the cloud with high standards of CI and automation. Building the greatest concentration of the smartest and most creative data scientists and engineers by being the most exciting and fulfilling place for them to work in Central Europe. Giving them the freedom to use our critical mass of expertise to find and iterate on the most promising data-driven opportunities, both for our clients and our own products. -
47
Bodo.ai
Bodo.ai
Bodo’s powerful compute engine and parallel computing approach provides efficient execution and effective scalability even for 10,000+ cores and PBs of data. Bodo enables faster development and easier maintenance for data science, data engineering and ML workloads with standard Python APIs like Pandas. Avoid frequent failures with bare-metal native code execution and catch errors before they appear in production with end-to-end compilation. Experiment faster with large datasets on your laptop with the simplicity that only Python can provide. Write production-ready code without the hassle of refactoring for scaling on large infrastructure! -
48
Querona
YouNeedIT
We make BI & Big Data analytics work easier and faster. Our goal is to empower business users and make always-busy business and heavily loaded BI specialists less dependent on each other when solving data-driven business problems. If you have ever experienced a lack of data you needed, time to consuming report generation or long queue to your BI expert, consider Querona. Querona uses a built-in Big Data engine to handle growing data volumes. Repeatable queries can be cached or calculated in advance. Optimization needs less effort as Querona automatically suggests query improvements. Querona empowers business analysts and data scientists by putting self-service in their hands. They can easily discover and prototype data models, add new data sources, experiment with query optimization and dig in raw data. Less IT is needed. Now users can get live data no matter where it is stored. If databases are too busy to be queried live, Querona will cache the data. -
49
The Autonomous Data Engine
Infoworks
There is a consistent “buzz” today about how leading companies are harnessing big data for competitive advantage. Your organization is striving to become one of those market-leading companies. However, the reality is that over 80% of big data projects fail to deploy to production because project implementation is a complex, resource-intensive effort that takes months or even years. The technology is complicated, and the people who have the necessary skills are either extremely expensive or impossible to find. Automates the complete data workflow from source to consumption. Automates migration of data and workloads from legacy Data Warehouse systems to big data platforms. Automates orchestration and management of complex data pipelines in production. Alternative approaches such as stitching together multiple point solutions or custom development are expensive, inflexible, time-consuming and require specialized skills to assemble and maintain. -
50
Outerbounds
Outerbounds
Design and develop data-intensive projects with human-friendly, open-source Metaflow. Run, scale, and deploy them reliably on the fully managed Outerbounds platform. One platform for all your ML and data science projects. Access data securely from your existing data warehouses. Compute with a cluster optimized for scale and cost. 24/7 managed orchestration for production workflows. Use results to power any application. Give your data scientists superpowers, approved by your engineers. Outerbounds Platform allows data scientists to develop rapidly, experiment at scale, and deploy to production confidently. All within the outer bounds of policies and processes defined by your engineers, running on your cloud account, fully managed by us. Security is in our DNA, not at the perimeter. The platform adapts to your policies and compliance requirements through multiple layers of security. Centralized auth, a strict permission boundary, and granular task execution roles.
