Best Big Data Software for Python

Find and compare the best Big Data software for Python in 2024

Use the comparison tool below to compare the top Big Data software for Python on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Gigasheet Reviews

    Gigasheet

    Gigasheet

    $95 per month
    4 Ratings
    Gigasheet is the big data spreadsheet that requires no set up, training, database or coding skills. No SQL or Python code, no IT infrastructure required to explore big data. Big data answers are available to anyone, even if they're not data scientists. Best of all, your first 3GB are free! Gigasheet is used by thousands of people and teams to gain insights in minutes, rather than hours or days. Anyone who can use a spreadsheet can access Gigasheet's big data and analysis capabilities. Sharing and collaboration tools make distributing huge data sets a snap. Gigasheet integrates with more than 135 SaaS platforms and databases.
  • 2
    Row Zero Reviews

    Row Zero

    Row Zero

    $8/month/user
    Row Zero is the best spreadsheet for big data. Row Zero is similar to Excel and Google Sheets, but can handle 1+ billion rows, process data much faster, and connect live to your data warehouse and other data sources. Built-in connectors include Snowflake, Databricks, Redshift, Amazon S3, and Postgres. Row Zero spreadsheets are powerful enough to pull entire database tables into a spreadsheet, letting anyone build live pivot tables, charts, models, and metrics on data from your data warehouse. With Row Zero, you can easily open, edit, and share multi-GB files (CSV, parquet, txt, etc.) Row Zero also offers advanced security features and is cloud-based, empowering organizations to eliminate ungoverned CSV exports and locally stored spreadsheets from their org. Row Zero has all of the spreadsheet features you know and love, but was built for big data. If you know how to use Excel or Google Sheets, you can get started with ease. No training required.
  • 3
    Databricks Data Intelligence Platform Reviews
    The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.
  • 4
    DataWorks Reviews
    Alibaba Cloud launched DataWorks, a Big Data platform product. It offers Big Data development, data permission management and offline job scheduling. DataWorks is easy to use and does not require any special cluster setup or management. To create a workflow, drag and drop nodes. Online editing and debugging of code is possible. You can also ask other developers to join your project. Data integration, MaxCompute SQL and MaxCompute MS, machine learning, shell tasks, and MaxCompute MR are supported. To prevent service interruptions, task monitoring is supported. It sends alarms when errors are detected. It can run millions of tasks simultaneously and supports hourly, daily and weekly schedules. DataWorks is the best platform to build big data warehouses. It also offers comprehensive data warehousing and support services. DataWorks offers a complete solution for data aggregation and processing, as well as data governance and data services.
  • 5
    kdb Insights Reviews
    kdb Insights, a cloud native, high-performance analytics solution designed for real-time data analysis of streaming and historical data, is a platform that can be used to analyze both streams and historical information. It allows for intelligent decision making regardless of data volume and velocity. It offers unmatched performance and price, and delivers analytics up to 100-fold faster than other solutions. The platform allows interactive data visualization via real-time dashboards to facilitate instantaneous insight and decision-making. It also integrates machine-learning models to predict and cluster structured data, detect patterns, score it, and enhance AI capabilities for time-series datasets. kdb Insights is scalable enough to handle large volumes of real-time data and historical data. This has been proven with volumes up to 110 Terabytes per Day. Its simple data intake and quick setup accelerate time-to value. Native support for q SQL and Python is also available, as well as compatibility with other programming languages via RESTful interfaces.
  • 6
    Vaex Reviews
    Vaex.io aims to democratize the use of big data by making it available to everyone, on any device, at any scale. Your prototype is the solution to reducing development time by 80%. Create automatic pipelines for every model. Empower your data scientists. Turn any laptop into an enormous data processing powerhouse. No clusters or engineers required. We offer reliable and fast data-driven solutions. Our state-of-the art technology allows us to build and deploy machine-learning models faster than anyone else on the market. Transform your data scientists into big data engineers. We offer comprehensive training for your employees to enable you to fully utilize our technology. Memory mapping, a sophisticated Expression System, and fast Out-of-Core algorithms are combined. Visualize and explore large datasets and build machine-learning models on a single computer.
  • 7
    Polars Reviews
    Polars, which is aware of the data-wrangling habits of its users, exposes a complete Python interface, including all of the features necessary to manipulate DataFrames. This includes an expression language, which will allow you to write readable, performant code. Polars was written in Rust to provide the Rust ecosystem with a feature-complete DataFrame interface. Use it as either a DataFrame Library or as a query backend for your Data Models.
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