Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Peaka
Hasura
Polytomic
GraphQL
Supabase
Jet Admin
Basedash
Denodo
Peaka is a Zero-ETL Data Platform that enables you to build a data stack in minutes instead of months.
With Peaka, you can integrate relational and NoSQL databases, SaaS tools, and APIsโ all without a data warehouse or ETL processes.
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What makes Peaka unique is its capability to make data integration accessible to organizations like startups and SMBs that lack the resources to employ large data teams.
Peaka's answer:
Our primary audience comprises startups willing to pull in data from different sources without having to invest in a costly data stack or employ large data teams.
Peaka's answer:
Peaka simplifies data integration and brings your data together without complicated ETL processes. Once your data is consolidated, you can then automate repetitive work and draw insights that can inform your decision-making.
Peaka's answer:
Peaka leverages data virtualization technology to create a semantic layer over scattered data sources. This new layer allows users to query data from any source without any physical ETL processes.
Peaka's answer:
Popupsmart, OneWell, Hop, and Actioner are among Peaka's biggest customers.
Peaka's answer:
Peaka started its life as Code2 - a no-code platform for developing customer-facing web apps. Having discovered that customers first needed to bring their data together before creating apps, the company went on to focus on simplifying data integration for non-technical people. In line with this new vision, the company rebranded itself as Peaka in 2023.
Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 5 months ago
Numbers are useful, but sometimes itโs easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 8 months ago
We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 9 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 10 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 11 months ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Hasura - Hasura is an open platform to build scalable app backends, offering a built-in database, search, user-management and more.
NumPy - NumPy is the fundamental package for scientific computing with Python
Polytomic - The one platform to sync any data anywhere
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
GraphQL - GraphQL is a data query language and runtime to request and deliver data to mobile and web apps.
