Best Data Management Software for Jupyter Notebook

Kubit

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.

33 Ratings

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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.io

104 Ratings

Starting Price: $0.005 per GB per hour

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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.)

8 Ratings

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Dagster

Dagster Labs

Dagster is a next-generation orchestration platform for the development, production, and observation of data assets. Unlike other data orchestration solutions, Dagster provides you with an end-to-end development lifecycle. Dagster gives you control over your disparate data tools and empowers you to build, test, deploy, run, and iterate on your data pipelines. It makes you and your data teams more productive, your operations more robust, and puts you in complete control of your data processes as you scale. Dagster brings a declarative approach to the engineering of data pipelines. Your team defines the data assets required, quickly assessing their status and resolving any discrepancies. An assets-based model is clearer than a tasks-based one and becomes a unifying abstraction across the whole workflow.

Starting Price: $0

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Stata

StataCorp LLC

Stata delivers everything you need for reproducible data analysis—powerful statistics, visualization, data manipulation, and automated reporting—all in one intuitive platform. Stata is fast and accurate. It is easy to learn through the extensive graphical interface yet completely programmable. With Stata's menus and dialogs, you get the best of both worlds. You can easily point and click or drag and drop your way to all of Stata's statistical, graphical, and data management features. Use Stata's intuitive command syntax to quickly execute commands. Whether you enter commands directly or use the menus and dialogs, you can create a log of all actions and their results to ensure the reproducibility and integrity of your analysis. Stata also has complete command-line scripting and programming facilities, including a full matrix programming language. You have access to everything you need to script your analysis or even to create new Stata commands.

Starting Price: $48.00/6-month/student

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Datameer

Datameer revolutionizes data transformation with a low-code approach, trusted by top global enterprises. Craft, transform, and publish data seamlessly with no code and SQL, simplifying complex data engineering tasks. Empower your data teams to make informed decisions confidently while saving costs and ensuring responsible self-service analytics. Speed up your analytics workflow by transforming datasets to answer ad-hoc questions and support operational dashboards. Empower everyone on your team with our SQL or Drag-and-Drop to transform your data in an intuitive and collaborative workspace. And best of all, everything happens in Snowflake. Datameer is designed and optimized for Snowflake to reduce data movement and increase platform adoption. Some of the problems Datameer solves: - Analytics is not accessible - Drowning in backlog - Long development

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Coginiti

Coginiti, the AI-enabled enterprise data workspace, empowers everyone to get consistent answers fast to any business question. Accelerating the analytic development lifecycle from development to certification, Coginiti makes it easy for you to search and find approved metrics for your use case. Coginiti integrates all the functionality you need to build, approve, version, and curate analytics across all business domains for reuse, all while adhering to your data governance policy and standards. Data and analytic teams in the insurance, financial services, healthcare, and retail/consumer package goods industries trust Coginiti’s collaborative data workspace to deliver value to their customers.

Starting Price: $189/user/year

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data.world

data.world is a fully managed service, born in the cloud, and optimized for modern data architectures. That means we handle all updates, migrations, and maintenance. Set up is fast and simple with a large and growing ecosystem of pre-built integrations including all of the major cloud data warehouses. When time-to-value is critical, your team needs to solve real business problems, not fight with hard-to-manage data software. data.world makes it easy for everyone, not just the "data people", to get clear, accurate, fast answers to any business question. Our cloud-native data catalog maps your siloed, distributed data to familiar and consistent business concepts, creating a unified body of knowledge anyone can find, understand, and use. In addition to our enterprise product, data.world is home to the world’s largest collaborative open data community. It’s where people team up on everything from social bot detection to award-winning data journalism.

Starting Price: $12 per month

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Azure Data Science Virtual Machines

Microsoft

DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.

Starting Price: $0.005

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neptune.ai

Neptune.ai is a machine learning operations (MLOps) platform designed to streamline the tracking, organizing, and sharing of experiments and model-building processes. It provides a comprehensive environment for data scientists and machine learning engineers to log, visualize, and compare model training runs, datasets, hyperparameters, and metrics in real-time. Neptune.ai integrates easily with popular machine learning libraries, enabling teams to efficiently manage both research and production workflows. With features that support collaboration, versioning, and experiment reproducibility, Neptune.ai enhances productivity and helps ensure that machine learning projects are transparent and well-documented across their lifecycle.

Starting Price: $49 per month

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Deep Lake

activeloop

Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.

Starting Price: $995 per month

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Kedro

Kedro is the foundation for clean data science code. It borrows concepts from software engineering and applies them to machine-learning projects. A Kedro project provides scaffolding for complex data and machine-learning pipelines. You spend less time on tedious "plumbing" and focus instead on solving new problems. Kedro standardizes how data science code is created and ensures teams collaborate to solve problems easily. Make a seamless transition from development to production with exploratory code that you can transition to reproducible, maintainable, and modular experiments. A series of lightweight data connectors is used to save and load data across many different file formats and file systems.

Starting Price: Free

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Datafi

Datafi provides a unified data platform for business teams. It integrates data siloes, it unifies data security and it enables self-service data workflows for the unique requirements of business users to easily find, use, and share the business information they need. Customers deploy Datafi to expand their organization’s data capabilities and empower more people to make fast and better data-driven decisions. With Datafi, data anywhere is easily accessible and meaningful for everyone. Know for sure how your data is accessed and how your data is used. Data-forward organizations know the value of enabling their data to drive new business outcomes, this starts with enabling data access in a simple and secure way. Novel uses of business data can drive new business outcomes and organizations that increase their data literacy are more likely to discover the data-driven insights that create new outcomes to better serve their customers.

Starting Price: $0.005 per query

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Forloop

Forloop is the no-code platform for external data automation. Go beyond your internal data limitations and access the latest market data to adapt faster, track market changes, and support price strategy. Get better insights with data outside of your company. With Forloop, you don’t have to make a compromise between a platform for prototyping and production-ready pipelines in the cloud of your choice. Access and extract data from non-API sources such as websites, maps, or 3rd party platforms. Get recommendations on how to clean, join, and aggregate data according to the best data science practices. Use no-code tools to clean, join, and transform data to model-ready format in an accelerated way with intelligent algorithms solving data quality issues. Our platform helped our users to increase their KPIs even by a factor of 10. Enhance decision-making and increase growth with new data. Forloop is a desktop app that you can download & try locally.

Starting Price: $29 per month

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MLJAR Studio

MLJAR

It's a desktop app with Jupyter Notebook and Python built in, installed with just one click. It includes interactive code snippets and an AI assistant to make coding faster and easier, perfect for data science projects. We manually hand crafted over 100 interactive code recipes that you can use in your Data Science projects. Code recipes detect packages available in the current environment. Install needed modules with 1-click, literally. You can create and interact with all variables available in your Python session. Interactive recipes speed-up your work. AI Assistant has access to your current Python session, variables and modules. Broad context makes it smart. Our AI Assistant was designed to solve data problems with Python programming language. It can help you with plots, data loading, data wrangling, Machine Learning and more. Use AI to quickly solve issues with code, just click Fix button. The AI assistant will analyze the error and propose the solution.

Starting Price: $20 per month

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Vanna.AI

Vanna.AI is an AI-powered platform designed to help users interact with their databases by asking questions in natural language. It enables both beginners and experts to quickly obtain insights from large datasets without needing to write complex SQL queries. Users simply ask a question, and Vanna automatically identifies the relevant tables and columns to retrieve the data needed. The platform integrates with popular databases like Snowflake, BigQuery, and Postgres and supports various front-end implementations such as Jupyter Notebooks, Slackbots, and web apps. Vanna's open source model allows for secure, self-hosted deployments and can continuously improve its performance as it learns from the user's interactions. It is ideal for businesses looking to democratize access to data insights and simplify the query process.

Starting Price: $25 per month

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GeoSpock

GeoSpock enables data fusion for the connected world with GeoSpock DB – the space-time analytics database. GeoSpock DB is a unique, cloud-native database optimised for querying for real-world use cases, able to fuse multiple sources of Internet of Things (IoT) data together to unlock its full value, whilst simultaneously reducing complexity and cost. GeoSpock DB enables efficient storage, data fusion, and rapid programmatic access to data, and allows you to run ANSI SQL queries and connect to analytics tools via JDBC/ODBC connectors. Users are able to perform analysis and share insights using familiar toolsets, with support for common BI tools (such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™), and Data Science and Machine Learning environments (including Python Notebooks and Apache Spark). The database can also be integrated with internal applications and web services – with compatibility for open-source and visualisation libraries such as Kepler and Cesium.js.

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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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Tengu

TENGU is a DataOps Orchestration Platform that works as a central workspace for data profiles of all levels. It provides data integration, extraction, transformation, loading all within it’s graph view UI in which you can intuitively monitor your data environment. By using the platform, business, analytics & data teams need fewer meetings and service tickets to collect data, and can start right away with the data relevant to furthering the company. The Platform offers a unique graph view in which every element is automatically generated with all available info based on metadata. While allowing you to perform all necessary actions from the same workspace. Enhance collaboration and efficiency, with the ability to quickly add and share comments, documentation, tags, groups. The platform enables anyone to get straight to the data with self-service. Thanks to the many automations and low to no-code functionalities and built-in assistant.

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IBM Watson Studio

IBM

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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Actian Avalanche

Actian

Actian Avalanche is a fully managed hybrid cloud data warehouse service designed from the ground up to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a fraction of the cost of alternative solutions. It is a true hybrid platform that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling you to migrate or offload applications and data to the cloud at your own pace. Actian Avalanche delivers the best price-performance in the industry outof-the-box without DBA tuning and optimization techniques. For the same cost as alternative solutions, you can benefit from substantially better performance or chose the same performance for significantly lower cost. For example, Avalanche provides up to 6x the price-performance advantage over Snowflake as measured by GigaOm’s TPC-H industry standard benchmark and even more against many of the appliance vendors.

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Warp 10

SenX

Warp 10 is a modular open source platform that collects, stores, and analyzes data from sensors. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 is both a time series database and a powerful analytics environment, allowing you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system.

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JetBrains DataSpell

JetBrains

Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.

Starting Price: $229

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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

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Hadoop

Apache Software Foundation

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page. Apache Hadoop 3.3.4 incorporates a number of significant enhancements over the previous major release line (hadoop-3.2).

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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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Kaggle

Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 19,000 public datasets and 200,000 public notebooks to conquer any analysis in no time.

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Molecula

Molecula is an enterprise feature store that simplifies, accelerates, and controls big data access to power machine-scale analytics and AI. Continuously extracting features, reducing the dimensionality of data at the source, and routing real-time feature changes into a central store enables millisecond queries, computation, and feature re-use across formats and locations without copying or moving raw data. The Molecula feature store provides data engineers, data scientists, and application developers a single access point to graduate from reporting and explaining with human-scale data to predicting and prescribing real-time business outcomes with all data. Enterprises spend a lot of money preparing, aggregating, and making numerous copies of their data for every project before they can make decisions with it. Molecula brings an entirely new paradigm for continuous, real-time data analysis to be used for all your mission-critical applications.

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Weights & Biases

Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence.

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Elucidata Polly

Elucidata

Harness the power of biomedical data with Polly. The Polly Platform helps to scale batch jobs, workflows, coding environments and visualization applications. Polly allows resource pooling and provides optimal resource allocation based on your usage requirements and makes use of spot instances whenever possible. All this leads to optimization, efficiency, faster response time and lower costs for the resources. Get access to a dashboard to monitor resource usage and cost real time and minimize overhead of resource management by your IT team. Version control is integral to Polly’s infrastructure. Polly ensures version control for your workflows and analyses through a combination of dockers and interactive notebooks. We have built a mechanism that allows the data, code and the environment co-exist. This coupled with data storage on the cloud and the ability to share projects ensures reproducibility of every analysis you perform.

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Best Data Management Software for Jupyter Notebook

Compare the Top Data Management Software that integrates with Jupyter Notebook as of October 2025

What is Data Management Software for Jupyter Notebook?

Kubit

Saturn Cloud

Google Colab

Dagster

Stata

Datameer

Coginiti

data.world

Azure Data Science Virtual Machines

neptune.ai

Deep Lake

Kedro

Datafi

Forloop

MLJAR Studio

Vanna.AI

GeoSpock

Google Cloud Datalab

Tengu

IBM Watson Studio

Actian Avalanche

Warp 10

JetBrains DataSpell

Chalk

Hadoop

Apache Spark

Kaggle

Molecula

Weights & Biases

Elucidata Polly

Best Data Management Software for Jupyter Notebook

Compare the Top Data Management Software that integrates with Jupyter Notebook as of October 2025

What is Data Management Software for Jupyter Notebook?

Kubit

Saturn Cloud

Google Colab

Dagster

Stata

Datameer

Coginiti

data.world

Azure Data Science Virtual Machines

neptune.ai

Deep Lake

Kedro

Datafi

Forloop

MLJAR Studio

Vanna.AI

GeoSpock

Google Cloud Datalab

Tengu

IBM Watson Studio

Actian Avalanche

Warp 10

JetBrains DataSpell

Chalk

Hadoop

Apache Spark

Kaggle

Molecula

Weights & Biases

Elucidata Polly

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