Best Data Management Software for Python - Page 4
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Compare the Top Data Management Software that integrates with Python as of November 2025 - Page 4
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This a list of Data Management software that integrates with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
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1
DataCebo Synthetic Data Vault (SDV)
DataCebo
The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables, or sequential tables. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights. Control data processing to improve the quality of synthetic data, choose from different types of anonymization, and define business rules in the form of logical constraints. Use synthetic data in place of real data for added protection, or use it in addition to your real data as an enhancement. The SDV is an overall ecosystem for synthetic data models, benchmarks, and metrics.Starting Price: Free -
2
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 -
3
Zerve AI
Zerve AI
Merging the best of a notebook and an IDE into one integrated coding environment, experts can explore their data and write stable code at the same time with fully automated cloud infrastructure. Zerve’s data science development environment gives data science and ML teams a unified space to explore, collaborate, build, and deploy data science & AI projects like never before. Zerve offers true language interoperability, meaning that as well as being able to use Python, R, SQL, or Markdown all in the same canvas, users can connect these code blocks to each other. No more long-running code blocks or containers, with Zerve enjoying unlimited parallelization at any stage of the development journey. Analysis artifacts are automatically serialized, versioned, stored, and preserved for later use, meaning easily changing a step in the data flow without needing to rerun any preceding steps. Fine-grained selection of compute resources and extra memory for complex data transformation. -
4
Pathway
Pathway
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes. -
5
Onehouse
Onehouse
The only fully managed cloud data lakehouse designed to ingest from all your data sources in minutes and support all your query engines at scale, for a fraction of the cost. Ingest from databases and event streams at TB-scale in near real-time, with the simplicity of fully managed pipelines. Query your data with any engine, and support all your use cases including BI, real-time analytics, and AI/ML. Cut your costs by 50% or more compared to cloud data warehouses and ETL tools with simple usage-based pricing. Deploy in minutes without engineering overhead with a fully managed, highly optimized cloud service. Unify your data in a single source of truth and eliminate the need to copy data across data warehouses and lakes. Use the right table format for the job, with omnidirectional interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Quickly configure managed pipelines for database CDC and streaming ingestion. -
6
Handinger
Handinger
You don't need to know how to code, just call an HTTP endpoint to extract data. Ideal for training LLM models or storing content in your second brain. Good for training visual models or fetching web thumbnails. Extract information from a website (image, title, description). Perfect for extracting specific content from websites. Fetch the content from a website and convert it to Markdown. Removes irrelevant content but may also eliminate some important information. Take a screenshot of a website and return the image URL. Extract the most common metadata from a website and return the JSON. Fetch the content from a website and return the HTML. There's a rate limit, but it's quite generous, 1,000 requests per minute. This allows you to extract data rapidly while ensuring the service remains fair and reliable for all users. It's just an HTTP endpoint, so you can use it without any coding.Starting Price: $0.0005 per URL -
7
DiscoLike
DiscoLike
Step up your product’s capabilities with a modern company data platform. We identify all business sites and their subsidiaries, extract text from key pages, and build the largest company LLM embedding database on the market. Our prospects repeatedly test us at 98.5% accuracy and 98% coverage. Leverage our data with our natural language search and segmentation technology. The company directory is a foundational part of many products. Ours begins with SSL certificates, ensuring unmatched accuracy and coverage, with no dead, obsolete, or parked domains. Non-English sites are translated first, allowing for truly global coverage. The same certificates provide us with additional exclusive data points, accurate company start dates, business size, and growth patterns, including private and international companies. The shift towards higher quality and more relevant business site content is driven by AI’s ability to analyze large datasets and understand context. -
8
Substrate
Substrate
Substrate is the platform for agentic AI. Elegant abstractions and high-performance components, optimized models, vector database, code interpreter, and model router. Substrate is the only compute engine designed to run multi-step AI workloads. Describe your task by connecting components and let Substrate run it as fast as possible. We analyze your workload as a directed acyclic graph and optimize the graph, for example, merging nodes that can be run in a batch. The Substrate inference engine automatically schedules your workflow graph with optimized parallelism, reducing the complexity of chaining multiple inference APIs. No more async programming, just connect nodes and let Substrate parallelize your workload. Our infrastructure guarantees your entire workload runs in the same cluster, often on the same machine. You won’t spend fractions of a second per task on unnecessary data roundtrips and cross-region HTTP transport.Starting Price: $30 per month -
9
DataChain
iterative.ai
DataChain connects unstructured data in cloud storage with AI models and APIs, enabling instant data insights by leveraging foundational models and API calls to quickly understand your unstructured files in storage. Its Pythonic stack accelerates development tenfold by switching to Python-based data wrangling without SQL data islands. DataChain ensures dataset versioning, guaranteeing traceability and full reproducibility for every dataset to streamline team collaboration and ensure data integrity. It allows you to analyze your data where it lives, keeping raw data in storage (S3, GCP, Azure, or local) while storing metadata in inefficient data warehouses. DataChain offers tools and integrations that are cloud-agnostic for both storage and computing. With DataChain, you can query your unstructured multi-modal data, apply intelligent AI filters to curate data for training and snapshot your unstructured data, the code for data selection, and any stored or computed metadata.Starting Price: Free -
10
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. -
11
Tensorlake
Tensorlake
Tensorlake is the AI data cloud that reliably transforms data from unstructured sources into ingestion-ready formats for AI applications. It seamlessly converts documents, images, and slides into structured JSON or markdown chunks, ready for retrieval and analysis by LLMs. The document ingestion APIs parse any file type, from hand-written notes to PDFs to complex spreadsheets, performing post-processing steps like chunking and preserving the reading order and layout of the documents. Tensorlake's serverless workflows enable lightning-fast, end-to-end data processing, allowing users to build and deploy fully managed Workflow APIs in Python that scale down to zero when idle and scale up when processing data. It supports processing millions of documents at once, maintaining context and relationships between various data formats, and offers secure, role-based access control for effective team collaboration.Starting Price: $0.01 per page -
12
Orchestra
Orchestra
Orchestra is a Unified Control Plane for Data and AI Operations, designed to help data teams build, deploy, and monitor workflows with ease. It offers a declarative framework that combines code and GUI, allowing users to implement workflows 10x faster and reduce maintenance time by 50%. With real-time metadata aggregation, Orchestra provides full-stack data observability, enabling proactive alerting and rapid recovery from pipeline failures. It integrates seamlessly with tools like dbt Core, dbt Cloud, Coalesce, Airbyte, Fivetran, Snowflake, BigQuery, Databricks, and more, ensuring compatibility with existing data stacks. Orchestra's modular architecture supports AWS, Azure, and GCP, making it a versatile solution for enterprises and scale-ups aiming to streamline their data operations and build trust in their AI initiatives. -
13
FeatureByte
FeatureByte
FeatureByte is your AI data scientist streamlining the entire lifecycle so that what once took months now happens in hours. Deployed natively on Databricks, Snowflake, BigQuery, or Spark, it automates feature engineering, ideation, cataloging, custom UDFs (including transformer support), evaluation, selection, historical backfill, deployment, and serving (online or batch), all within a unified platform. FeatureByte’s GenAI‑inspired agents, data, domain, MLOps, and data science agents interactively guide teams through data acquisition, quality, feature generation, model creation, deployment orchestration, and continued monitoring. FeatureByte’s SDK and intuitive UI enable automated and semi‑automated feature ideation, customizable pipelines, cataloging, lineage tracking, approval flows, RBAC, alerts, and version control, empowering teams to build, refine, document, and serve features rapidly and reliably. -
14
Serply
Serply
Serply.io is a developer-focused API platform that provides real-time, CAPTCHA-free Google Search Engine Results Page (SERP) data in JSON format. Designed for applications requiring precise search information, it delivers results in under 300 milliseconds. The API supports advanced queries across various Google services, allowing for tailored data retrieval. Serply.io ensures accurate location-based results by utilizing geolocated, encrypted parameters and routing requests through proximate servers. Developers can integrate the API using multiple programming languages such as Python, JavaScript, Ruby, and Go. It boasts a four-year track record with a 100% service level, offering responsive customer support and comprehensive documentation to assist users in implementation. Also, Serply.io provides open source tools like Serply Notifications, enabling users to schedule and receive notifications for specific search queries.Starting Price: $49 per month -
15
CData Connect AI
CData
CData’s AI offering is centered on Connect AI and associated AI-driven connectivity capabilities, which provide live, governed access to enterprise data without moving it off source systems. Connect AI is built as a managed Model Context Protocol (MCP) platform that lets AI assistants, agents, copilots, and embedded AI applications directly query over 300 data sources, such as CRM, ERP, databases, APIs, with a full understanding of data semantics and relationships. It enforces source system authentication, respects existing role-based permissions, and ensures that AI actions (reads and writes) follow governance and audit rules. The system supports query pushdown, parallel paging, bulk read/write operations, streaming mode for large datasets, and cross-source reasoning via a unified semantic layer. In addition, CData’s “Talk to your Data” engine integrates with its Virtuality product to allow conversational access to BI insights and reports. -
16
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. -
17
Forsta
Forsta
The most powerful, flexible, connected, and most reliable experience & research tech platform. Forsta transcends methodological and data silos. All human experience is here. If it’s insightful, it’s measurable. Use customizable surveys to seek insight from any audience, from small teams to global communities. Take the data you need from any touchpoint or channel. Forsta comes packed with the tools to bring you better data and deeper insights. So you can push your business forward. If it’s insightful, it’s measurable. Use customizable surveys or moderated online conversations to seek insight from any audience – from small teams to global communities. Take the data you need from any touchpoint or channel. Bring all your data onto a single platform. So you can see the stories behind the statistics. Use advanced analytics tools to search, sort and filter in whatever way gets you to the answers you need. -
18
IBM Databand
IBM
Monitor your data health and pipeline performance. Gain unified visibility for pipelines running on cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. An observability platform purpose built for Data Engineers. Data engineering is only getting more challenging as demands from business stakeholders grow. Databand can help you catch up. More pipelines, more complexity. Data engineers are working with more complex infrastructure than ever and pushing higher speeds of release. It’s harder to understand why a process has failed, why it’s running late, and how changes affect the quality of data outputs. Data consumers are frustrated with inconsistent results, model performance, and delays in data delivery. Not knowing exactly what data is being delivered, or precisely where failures are coming from, leads to persistent lack of trust. Pipeline logs, errors, and data quality metrics are captured and stored in independent, isolated systems. -
19
Scrapy
Scrapy
Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. Built-in support for selecting and extracting data from HTML/XML sources using extended CSS selectors and XPath expressions, with helper methods to extract using regular expressions. Built-in support for generating feed exports in multiple formats (JSON, CSV, XML) and storing them in multiple backends (FTP, S3, local filesystem). Robust encoding support and auto-detection, for dealing with foreign, non-standard and broken encoding declarations. -
20
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. -
21
Zepl
Zepl
Sync, search and manage all the work across your data science team. Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. Use fine-grained access controls to share your work. Allow others have read, edit, and run access as well as enable collaboration and distribution. All notebooks are auto-saved and versioned. You can name, manage and roll back all versions through an easy-to-use interface, and export seamlessly into Github. -
22
Bitfount
Bitfount
Bitfount is a platform for distributed data science. We power deep data collaborations without data sharing. Distributed data science sends algorithms to data, instead of the other way around. Set up a federated privacy-preserving analytics and machine learning network in minutes, and let your team focus on insights and innovation instead of bureaucracy. Your data team has the skills to solve your biggest challenges and innovate, but they are held back by barriers to data access. Is complex data pipeline infrastructure messing with your plans? Are compliance processes taking too long? Bitfount has a better way to unleash your data experts. Connect siloed and multi-cloud datasets while preserving privacy and respecting commercial sensitivity. No expensive, time-consuming data lift-and-shift. Usage-based access controls to ensure teams only perform the analysis you want, on the data you want. Transfer management of access controls to the teams who control the data. -
23
Seaborn
Seaborn
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery to see some of the things that you can do with seaborn, and then check out the tutorials or API reference to find out how. To see the code or report a bug, please visit the GitHub repository. General support questions are most at home on StackOverflow, which has a dedicated channel for seaborn. -
24
MakerSuite
Google
MakerSuite is a tool that simplifies this workflow. With MakerSuite, you’ll be able to iterate on prompts, augment your dataset with synthetic data, and easily tune custom models. When you’re ready to move to code, MakerSuite will let you export your prompt as code in your favorite languages and frameworks, like Python and Node.js. -
25
Avanzai
Avanzai
Avanzai helps accelerate your financial data analysis by letting you use natural language to output production-ready Python code. Avanzai speeds up financial data analysis for both beginners and experts using plain English. Plot times series data, equity index members, and even stock performance data using natural prompts. Skip the boring parts of financial analysis by leveraging AI to generate code with relevant Python packages already installed. Further edit the code if you wish, once you're ready copy and paste the code into your local environment and get straight to business. Leverage commonly used Python packages for quant analysis such as Pandas, Numpy, etc using plain English. Take financial analysis to the next level, quickly pull fundamental data and calculate the performance of nearly all US stocks. Enhance your investment decisions with accurate and up-to-date information. Avanzai empowers you to write the same Python code that quants use to analyze complex financial data. -
26
Quadratic
Quadratic
Quadratic enables your team to work together on data analysis to deliver faster results. You already know how to use a spreadsheet, but you’ve never had this much power. Quadratic speaks Formulas and Python (SQL & JavaScript coming soon). Use the language you and your team already know. Single-line formulas are hard to read. In Quadratic you can expand your recipes to as many lines as you need. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. The last line of code is returned to the spreadsheet. Raw values, 1/2D arrays, and Pandas DataFrames are supported by default. Pull or fetch data from an external API, and it updates automatically in Quadratic's cells. Navigate with ease, zoom out for the big picture, and zoom in to focus on the details. Arrange and navigate your data how it makes sense in your head, not how a tool forces you to do it. -
27
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. -
28
Polars
Polars
Knowing of data wrangling habits, Polars exposes a complete Python API, including the full set of features to manipulate DataFrames using an expression language that will empower you to create readable and performant code. Polars is written in Rust, uncompromising in its choices to provide a feature-complete DataFrame API to the Rust ecosystem. Use it as a DataFrame library or as a query engine backend for your data models. -
29
Kestra
Kestra
Kestra is an open-source, event-driven orchestrator that simplifies data operations and improves collaboration between engineers and business users. By bringing Infrastructure as Code best practices to data pipelines, Kestra allows you to build reliable workflows and manage them with confidence. Thanks to the declarative YAML interface for defining orchestration logic, everyone who benefits from analytics can participate in the data pipeline creation process. The UI automatically adjusts the YAML definition any time you make changes to a workflow from the UI or via an API call. Therefore, the orchestration logic is defined declaratively in code, even if some workflow components are modified in other ways. -
30
SuperDuperDB
SuperDuperDB
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands.
