Alternatives to Hopsworks
Compare Hopsworks alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Hopsworks in 2026. Compare features, ratings, user reviews, pricing, and more from Hopsworks 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. -
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TiMi
TIMi
With TIMi, companies can capitalize on their corporate data to develop new ideas and make critical business decisions faster and easier than ever before. The heart of TIMi’s Integrated Platform. TIMi’s ultimate real-time AUTO-ML engine. 3D VR segmentation and visualization. Unlimited self service business Intelligence. TIMi is several orders of magnitude faster than any other solution to do the 2 most important analytical tasks: the handling of datasets (data cleaning, feature engineering, creation of KPIs) and predictive modeling. TIMi is an “ethical solution”: no “lock-in” situation, just excellence. We guarantee you a work in all serenity and without unexpected extra costs. Thanks to an original & unique software infrastructure, TIMi is optimized to offer you the greatest flexibility for the exploration phase and the highest reliability during the production phase. TIMi is the ultimate “playground” that allows your analysts to test the craziest ideas! -
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Incorta
Incorta
Direct is the shortest path from data to insight. Incorta empowers everyone in your business with a true self-service data experience and breakthrough performance for better decisions and incredible results. What if you could bypass fragile ETL and expensive data warehouses, and deliver data projects in days, instead of weeks or months? Our direct approach to analytics delivers true self-service in the cloud or on-premises with agility and performance. Incorta is used by the world’s largest brands to succeed where other analytics solutions fail. Across multiple industries and lines of business, we boast connectors and pre-built solutions for your enterprise applications and technologies. Game-changing innovation and customer success happen through Incorta’s partners including Microsoft, AWS, eCapital, and Wipro. Explore or join our thriving partner ecosystem. -
5
Posit
Posit
Posit builds tools that help data scientists work more efficiently, collaborate seamlessly, and share insights securely across their organizations. Its Positron code editor provides the speed of an interactive console combined with the power to build, debug, and deploy data-science workflows in Python and R. Posit’s platform enables teams to scale open-source data science, offering enterprise-ready capabilities for publishing, sharing, and operationalizing applications. Companies rely on Posit’s secure infrastructure to host Shiny apps, dashboards, APIs, and analytical reports with confidence. Whether using open-source packages or cloud-based solutions, Posit supports reproducible, high-quality work at every stage of the data lifecycle. Trusted by millions of users—and more than half of the Fortune 100—Posit empowers professionals across industries to innovate with data. -
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Tecton
Tecton
Deploy machine learning applications to production in minutes, rather than months. Automate the transformation of raw data, generate training data sets, and serve features for online inference at scale. Save months of work by replacing bespoke data pipelines with robust pipelines that are created, orchestrated and maintained automatically. Increase your team’s efficiency by sharing features across the organization and standardize all of your machine learning data workflows in one platform. Serve features in production at extreme scale with the confidence that systems will always be up and running. Tecton meets strict security and compliance standards. Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure. -
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DATAGYM
eForce21
DATAGYM enables data scientists and machine learning experts to label images up to 10x faster. AI-assisted annotation tools reduce manual labeling effort, give you more time to finetune ML models and speed up your go to market of new products. Accelerate your computer vision projects by cutting down data preparation time up to 50%.Starting Price: $19.00/month/user -
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Deepnote
Deepnote
Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore, and analyze it with real-time collaboration and version control. Users can easily share project links with team collaborators, or with end-users to present polished assets. All of this is done through a powerful, browser-based UI that runs in the cloud. We built Deepnote because data scientists don't work alone. Features: - Sharing notebooks and projects via URL - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connectionStarting Price: Free -
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BIRD Analytics
Lightning Insights
BIRD Analytics is a blazingly fast high performance, full-stack data management, and analytics platform to generate insights using agile BI and AI/ ML models. It covers all the aspects - starting from data ingestion, transformation, wrangling, modeling, storing, analyze data in real-time, that too on petabyte-scale data. BIRD provides self-service capabilities with Google-type search and powerful ChatBot integration. We’ve compiled our resources to provide the answers you seek. From industry uses cases to blog articles, learn more about how BIRD addresses Big Data pain points. Now that you’ve discovered the value of BIRD, schedule a demo to see the platform in action and uncover how it can transform your distinct data. Utilize AI/ML technologies for greater agility & responsiveness in decision-making, cost reduction, and improving customer experiences. -
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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. -
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Robin.io
Robin.io
ROBIN is the industry’s first hyper-converged Kubernetes platform for big data, databases, and AI/ML. The platform provides a self-service App-store experience for the deployment of any application, anywhere – runs on-premises in your private data center or in public-cloud (AWS, Azure, GCP) environments. Hyper-converged Kubernetes is a software-defined application orchestration framework that combines containerized storage, networking, compute (Kubernetes), and the application management layer into a single system. Our approach extends Kubernetes for data-intensive applications such as Hortonworks, Cloudera, Elastic stack, RDBMS, NoSQL databases, and AI/ML apps. Facilitates simpler and faster roll-out of critical Enterprise IT and LoB initiatives, such as containerization, cloud-migration, cost-consolidation, and productivity improvement. Solves the fundamental challenges of running big data and databases in Kubernetes. -
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Google Cloud Datalab
Google
An easy-to-use interactive tool for data exploration, analysis, visualization, and machine learning. Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. It runs on Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks. Cloud Datalab is built on Jupyter (formerly IPython), which boasts a thriving ecosystem of modules and a robust knowledge base. Cloud Datalab enables analysis of your data on BigQuery, AI Platform, Compute Engine, and Cloud Storage using Python, SQL, and JavaScript (for BigQuery user-defined functions). Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data, and run training jobs on terabytes of data in AI Platform seamlessly. -
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Oracle Analytics Cloud
Oracle
Oracle Analytics is a complete platform for every analytics user role. AI and ML are embedded throughout the platform to accelerate productivity and power better business decisions. Choose either Oracle Analytics Cloud, our cloud native service, or our on-premises solution, Oracle Analytics Server, both of which help you avoid compromising security and governance. Oracle Analytic addresses all needs of business users from data to decision. Oracle Analytics can help you solve your business problems with built in data preparation and enrichment, no-code machine learning and industry leading data visualization.Starting Price: $16 User Per Month - Oracle An -
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Alteryx
Alteryx
Step into a new era of analytics with the Alteryx AI Platform. Empower your organization with automated data preparation, AI-powered analytics, and approachable machine learning — all with embedded governance and security. Welcome to the future of data-driven decisions for every user, every team, every step of the way. Empower your teams with an easy, intuitive user experience allowing everyone to create analytic solutions that improve productivity, efficiency, and the bottom line. Build an analytics culture with an end-to-end cloud analytics platform and transform data into insights with self-service data prep, machine learning, and AI-generated insights. Reduce risk and ensure your data is fully protected with the latest security standards and certifications. Connect to your data and applications with open API standards. -
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Kubeflow
Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready. -
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Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
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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. -
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Altair SLC
Altair
Many organizations have developed SAS language programs over the past 20 years that are vital to their operations. Altair SLC runs programs written in SAS language syntax without translation and without needing to license third-party products. Altair SLC reduces users’ capital costs and operating expenses thanks to its superb ability to handle high levels of throughput. Altair SLC's built-in SAS language compiler runs SAS language and SQL code, and utilizes Python and R compilers to run Python and R code and exchange SAS language datasets, Pandas, and R data frames. The software runs on IBM mainframes, in the cloud, and on servers and workstations running a variety of operating systems. It supports both remote job submission and the ability to exchange data between mainframe, cloud, and on-premises installations. -
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Scribble Data
Scribble Data
Scribble Data empowers organizations to enrich their raw data and easily transform it to enable reliable and fast decision-making for persistent business problems. Data-driven decision support for your business. A data-to-decision platform that helps you generate high-fidelity insights to automate decision-making. Solve your persistent business decision-making problems instantly with advanced analytics powered by machine learning. Rest easy and focus your energy on critical tasks, while Enrich does the heavy lifting to ensure the availability of reliable and trustworthy data for decision-making. Leverage customized data-driven workflows for easy consumption of data, and reduce your dependence on data science and machine learning engineering teams. Go from concept to operational data product in a few weeks, not months with feature engineering capabilities that can prepare high volume and high complexity data at scale. -
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BryteFlow
BryteFlow
BryteFlow builds the most efficient automated environments for analytics ever. It converts Amazon S3 into an awesome analytics platform by leveraging the AWS ecosystem intelligently to deliver data at lightning speeds. It complements AWS Lake Formation and automates the Modern Data Architecture providing performance and productivity. You can completely automate data ingestion with BryteFlow Ingest’s simple point-and-click interface while BryteFlow XL Ingest is great for the initial full ingest for very large datasets. No coding is needed! With BryteFlow Blend you can merge data from varied sources like Oracle, SQL Server, Salesforce and SAP etc. and transform it to make it ready for Analytics and Machine Learning. BryteFlow TruData reconciles the data at the destination with the source continually or at a frequency you select. If data is missing or incomplete you get an alert so you can fix the issue easily. -
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Dataiku
Dataiku
Dataiku is an advanced data science and machine learning platform designed to enable teams to build, deploy, and manage AI and analytics projects at scale. It empowers users, from data scientists to business analysts, to collaboratively create data pipelines, develop machine learning models, and prepare data using both visual and coding interfaces. Dataiku supports the entire AI lifecycle, offering tools for data preparation, model training, deployment, and monitoring. The platform also includes integrations for advanced capabilities like generative AI, helping organizations innovate and deploy AI solutions across industries. -
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Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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Saturn Cloud
Saturn Cloud
Saturn Cloud is an AI/ML platform available on every cloud. Data teams and engineers can build, scale, and deploy their AI/ML applications with any stack. Quickly spin up environments to test new ideas, then easily deploy them into production. Scale fast—from proof-of-concept to production-ready applications. Customers include NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. Get started for free at: saturncloud.ioStarting Price: $0.005 per GB per hour -
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RapidMiner
Altair
RapidMiner is reinventing enterprise AI so that anyone has the power to positively shape the future. We’re doing this by enabling ‘data loving’ people of all skill levels, across the enterprise, to rapidly create and operate AI solutions to drive immediate business impact. We offer an end-to-end platform that unifies data prep, machine learning, and model operations with a user experience that provides depth for data scientists and simplifies complex tasks for everyone else. Our Center of Excellence methodology and the RapidMiner Academy ensures customers are successful, no matter their experience or resource levels. Simplify operations, no matter how complex models are, or how they were created. Deploy, evaluate, compare, monitor, manage and swap any model. Solve your business issues faster with sharper insights and predictive models, no one understands the business problem like you do.Starting Price: Free -
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Databricks Data Intelligence Platform
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. -
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Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built. -
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Polyaxon
Polyaxon
A Platform for reproducible and scalable Machine Learning and Deep Learning applications. Learn more about the suite of features and products that underpin today's most innovative platform for managing data science workflows. Polyaxon provides an interactive workspace with notebooks, tensorboards, visualizations,and dashboards. Collaborate with the rest of your team, share and compare experiments and results. Reproducible results with a built-in version control for code and experiments. Deploy Polyaxon in the cloud, on-premises or in hybrid environments, including single laptop, container management platforms, or on Kubernetes. Spin up or down, add more nodes, add more GPUs, and expand storage. -
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MLlib
Apache Software Foundation
Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem. -
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Google Colab
Google
Google Colab is a free, hosted Jupyter Notebook service that provides cloud-based environments for machine learning, data science, and educational purposes. It offers no-setup, easy access to computational resources such as GPUs and TPUs, making it ideal for users working with data-intensive projects. Colab allows users to run Python code in an interactive, notebook-style environment, share and collaborate on projects, and access extensive pre-built resources for efficient experimentation and learning. Colab also now offers a Data Science Agent automating analysis, from understanding the data to delivering insights in a working Colab notebook (Sequences shortened. Results for illustrative purposes. Data Science Agent may make mistakes.) -
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Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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The biggest challenge to scaling AI-powered decision-making is unused data. IBM Cloud Pak® for Data is a unified platform that delivers a data fabric to connect and access siloed data on-premises or across multiple clouds without moving it. Simplify access to data by automatically discovering and curating it to deliver actionable knowledge assets to your users, while automating policy enforcement to safeguard use. Further accelerate insights with an integrated modern cloud data warehouse. Universally safeguard data usage with privacy and usage policy enforcement across all data. Use a modern, high-performance cloud data warehouse to achieve faster insights. Empower data scientists, developers and analysts with an integrated experience to build, deploy and manage trustworthy AI models on any cloud. Supercharge analytics with Netezza, a high-performance data warehouse.Starting Price: $699 per month
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Azure Notebooks
Microsoft
Develop and run code from anywhere with Jupyter notebooks on Azure. Get started for free. Get a better experience with a free Azure Subscription. Perfect for data scientists, developers, students, or anyone. Develop and run code in your browser regardless of industry or skillset. Supporting more languages than any other platform including Python 2, Python 3, R, and F#. Created by Microsoft Azure: Always accessible, always available from any browser, anywhere in the world. -
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Amazon EMR
Amazon
Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting. -
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Azure Synapse Analytics
Microsoft
Azure Synapse is Azure SQL Data Warehouse evolved. Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless or provisioned resources—at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs. -
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SynctacticAI
SynctacticAI Technology
Use cutting-edge data science tools to transform your business outcomes. SynctacticAI crafts a successful adventure out of your business by leveraging advanced data science tools, algorithms and systems to extract knowledge and insights from any structured and unstructured sets of data. Discover your data in any form – structure or unstructured and batch or real-time.Sync Discover is a key feature to discover a relevant piece of data and organizing the large pool of data in a systematic manner. Process your data at scale with Sync Data. Enabled with a simple navigation interface like drag and drop, you can smoothly configure your data pipelines and process data manually or at predetermined schedules. With the power of machine learning, the process of learning from data becomes effortless. Simply select the target variable, feature, and any of our pre-built models – rest is automatically taken care of by Sync Learn. -
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Semantix Data Platform (SDP)
Semantix
A Big Data Platform that generates intelligence and efficiency for your business with features that simplify the data journey. Create algorithms, Artificial Intelligence, Machine Learning and more for your business. With SDP you unify the entire data driven journey of your business from end to end, centralizing information and creating data-driven intelligence. Ingestion, engineering, science and data visualization in a single journey. Robust operations-ready and agnostic technology that facilitates data governance. Simple and intuitive Marketplace interface with ready-made algorithms and extensibility via APIs. The only Big Data platform to centralize and unify your entire business data journey. -
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NaturalText
NaturalText
NaturalText A.I. helps you get more out of your data. Discover relationships, create collections, and unveil hidden insights in documents and other text-based data. NaturalText A.I. uses novel artificial intelligence technology to uncover hidden relationships in data. The software uses various state-of-the-art methods to understand context, analyze patterns, and reveal insights—all in a human-readable way. Reveal insights hidden in your data. Finding everything hidden in your text data is a difficult, if not impossible, task. With traditional search, you can only locate information related to a document. NaturalText A.I., on the other hand, uncovers new information within millions of documents, including scientific papers and patents. Use NaturalText A.I. to reveal insights in the data you are currently missing.Starting Price: $5000.00 -
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Google Cloud Dataproc
Google
Dataproc makes open source data and analytics processing fast, easy, and more secure in the cloud. Build custom OSS clusters on custom machines faster. Whether you need extra memory for Presto or GPUs for Apache Spark machine learning, Dataproc can help accelerate your data and analytics processing by spinning up a purpose-built cluster in 90 seconds. Easy and affordable cluster management. With autoscaling, idle cluster deletion, per-second pricing, and more, Dataproc can help reduce the total cost of ownership of OSS so you can focus your time and resources elsewhere. Security built in by default. Encryption by default helps ensure no piece of data is unprotected. With JobsAPI and Component Gateway, you can define permissions for Cloud IAM clusters, without having to set up networking or gateway nodes. -
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kdb Insights
KX
kdb Insights is a cloud-native, high-performance analytics platform designed for real-time analysis of both streaming and historical data. It enables intelligent decision-making regardless of data volume or velocity, offering unmatched price and performance, and delivering analytics up to 100 times faster at 10% of the cost compared to other solutions. The platform supports interactive data visualization through real-time dashboards, facilitating instantaneous insights and decision-making. It also integrates machine learning models to predict, cluster, detect patterns, and score structured data, enhancing AI capabilities on time-series datasets. With supreme scalability, kdb Insights handles extensive real-time and historical data, proven at volumes of up to 110 terabytes per day. Its quick setup and simple data intake accelerate time-to-value, while native support for q, SQL, and Python, along with compatibility with other languages via RESTful APIs. -
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Paxata
Paxata
Paxata is a visually-dynamic, intuitive solution that enables business analysts to rapidly ingest, profile, and curate multiple raw datasets into consumable information in a self-service manner, greatly accelerating development of actionable business insights. In addition to empowering business analysts and SMEs, Paxata also provides a rich set of workload automation and embeddable data preparation capabilities to operationalize and deliver data preparation as a service within other applications. The Paxata Adaptive Information Platform (AIP) unifies data integration, data quality, semantic enrichment, re-use & collaboration, and also provides comprehensive data governance and audit capabilities with self-documenting data lineage. The Paxata AIP utilizes a native multi-tenant elastic cloud architecture and is the only modern information platform that is currently deployed as a multi-cloud hybrid information fabric. -
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Talend Data Fabric
Qlik
Talend Data Fabric’s suite of cloud services efficiently handles all your integration and integrity challenges — on-premises or in the cloud, any source, any endpoint. Deliver trusted data at the moment you need it — for every user, every time. Ingest and integrate data, applications, files, events and APIs from any source or endpoint to any location, on-premise and in the cloud, easier and faster with an intuitive interface and no coding. Embed quality into data management and guarantee ironclad regulatory compliance with a thoroughly collaborative, pervasive and cohesive approach to data governance. Make the most informed decisions based on high quality, trustworthy data derived from batch and real-time processing and bolstered with market-leading data cleaning and enrichment tools. Get more value from your data by making it available internally and externally. Extensive self-service capabilities make building APIs easy— improve customer engagement. -
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Informatica Data Engineering
Informatica
Ingest, prepare, and process data pipelines at scale for AI and analytics in the cloud. Informatica’s comprehensive data engineering portfolio provides everything you need to process and prepare big data engineering workloads to fuel AI and analytics: robust data integration, data quality, streaming, masking, and data preparation capabilities. Rapidly build intelligent data pipelines with CLAIRE®-powered automation, including automatic change data capture (CDC) Ingest thousands of databases and millions of files, and streaming events. Accelerate time-to-value ROI with self-service access to trusted, high-quality data. Get unbiased, real-world insights on Informatica data engineering solutions from peers you trust. Reference architectures for sustainable data engineering solutions. AI-powered data engineering in the cloud delivers the trusted, high quality data your analysts and data scientists need to transform business. -
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Oracle Big Data Preparation
Oracle
Oracle Big Data Preparation Cloud Service is a managed Platform as a Service (PaaS) cloud-based offering that enables you to rapidly ingest, repair, enrich, and publish large data sets with end-to-end visibility in an interactive environment. You can integrate your data with other Oracle Cloud Services, such as Oracle Business Intelligence Cloud Service, for down-stream analysis. Profile metrics and visualizations are important features of Oracle Big Data Preparation Cloud Service. When a data set is ingested, you have visual access to the profile results and summary of each column that was profiled, and the results of duplicate entity analysis completed on your entire data set. Visualize governance tasks on the service Home page with easily understood runtime metrics, data health reports, and alerts. Keep track of your transforms and ensure that files are processed correctly. See the entire data pipeline, from ingestion to enrichment and publishing. -
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Bluemetrix
Bluemetrix
Migrating data to the cloud is difficult. Let us simplify the process for you with Bluemetrix Data Manager (BDM). BDM automates the ingestion of complex data sources and ensures that as your data sources change dynamically, your pipelines configure automatically to match the new data sources. BDM provides the application of automation and processing of data at scale, in a secure modern environment, with smart GUI and API interfaces available. With data governance fully automated, it is now possible to streamline pipeline creation, while recording and storing all actions automatically in your catalogue as the pipeline executes. Easy to create templating, combined with smart scheduling options, enables Self Service capability for data consumers - both business and technical users. A free enterprise-grade data ingestion tool that will automate the ingestion of data smoothly and quickly from on-premise sources into the cloud, while also automating the creation and running of pipelines. -
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Zerve AI
Zerve AI
Zerve is the agentic data workspace designed for anyone who works with data, from solo analysts, data scientists, business users and teams alike. Zerve brings together exploration, advanced analysis, collaboration, and production deployment into a single AI-native environment, so that important data work doesn’t stall, break, or disappear. Zerve’s AI agents understand the full context of a project and actively help plan, build, debug, and iterate across multi-step analyses. Agents assist with tasks like cleaning and transforming data, identifying issues, and testing approaches, reducing the manual effort that slows teams down. This means working at a higher level of abstraction without being slowed by setup or syntax. Zerve can be used as SaaS, self-hosted, or even on-premise for highly regulated environments. Zerve is used by data professionals in companies such as BBC, QVC, Dun & Bradstreet, Airbus, and many others.Starting Price: $0 -
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Torch.AI Nexus
Torch.AI
Extract meaningful content from any type of data, any format, any system, any structure, in the cloud or on premises. Nexus leverages machine learning algorithms to process data instantly, before it’s stored anywhere. Secure connect your data sources and business systems, so your investments in infrastructure don’t go to waste. Nexus unlocks your proprietary data by fusing it with additional, public data sources—like social media and geography. Extract intelligence from your data in new and novel ways. Surface hidden context and correlations through a deeper, ontological understanding of your data. Composable microservices invoked as code, simplifying integration with existing data infrastructure. Securely provision and orchestrate multiple services at any scale. Rapid deployment provides your customers value within a matter of hours. -
47
Valohai
Valohai
Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.Starting Price: $560 per month -
48
Hex
Hex
Hex brings together the best of notebooks, BI, and docs into a seamless, collaborative UI. Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories. Your default landing page in Hex is the Projects page. You can quickly find projects you created, as well as those shared with you and your workspace. The outline provides an easy-to-browse overview of all the cells in a project's Logic View. Every cell in the outline lists the variables it defines, and cells that return a displayed output (chart cells, Input Parameters, markdown cells, etc.) display a preview of that output. You can click any cell in the outline to automatically jump to that position in the logic.Starting Price: $24 per user per month -
49
JupyterLab
Jupyter
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include, data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. -
50
OpenText Analytics Database is a high-performance, scalable analytics platform that enables organizations to analyze massive data sets quickly and cost-effectively. It supports real-time analytics and in-database machine learning to deliver actionable business insights. The platform can be deployed flexibly across hybrid, multi-cloud, and on-premises environments to optimize infrastructure and reduce total cost of ownership. Its massively parallel processing (MPP) architecture handles complex queries efficiently, regardless of data size. OpenText Analytics Database also features compatibility with data lakehouse architectures, supporting formats like Parquet and ORC. With built-in machine learning and broad language support, it empowers users from SQL experts to Python developers to derive predictive insights.
