Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
Figure Eight
Embeddable
Luzmo
Metabase
Looker
Vizzly
POWr
Sisense
Tableau
Build Remarkable Analytics Experiences. No more 'Build vs. Buy'. Embeddable is the embedded analytics tool where you own the front-end code and we handle everything else. Now you can build fully-bespoke, fast-loading charts and dashboards in your app without the engineering costs. Delight your customers, reduce engineering overheads, and deliver your dream experience, fast. Compatible with all major databases. Cloud & Self-hosted. Multi-tenancy. Open source component library + more
Pandas EmbeddablePandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.
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Embeddable's answer:
Software companies who care about the UX and loading speed of their customer-facing analytics.
Embeddable's answer:
Get the best of 'Build vs. Buy' in one stack-agnostic solution. Embeddable gives you full control over the frontend of your analytics experience, and handles the backend for you. No longer do you have to choose between a limited out-of-the-box solution, or building everything from scratch.
Embeddable's answer:
Embeddable is from the team behind Trevor.io -- a popular internal BI tool which also allows you to embed dashboards into your app. We realised embedding dashboards from a BI tool into your app wasn't the 'dream solution', and building analytics from scratch was super expensive... so we built Embeddable from the ground up to enable teams to deliver fully-bespoke, highly-performant analytics in their apps for their customers in 10% of the time.
Embeddable's answer:
Embeddable's answer:
If you want full control over the UX of your customer-facing analytics experience, but don't want to invest months of developer time on building and maintaining a fully-custom build -- OR -- if you're using an embedded analytics too already that loads slowly and doesn't look and feel like the rest of your platform.
Based on our record, Pandas seems to be a lot more popular than Embeddable. While we know about 231 links to Pandas, we've tracked only 2 mentions of Embeddable. 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.
Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / about 1 month ago
For early-career security practitioners (0-3 years). Start with Python literacy if you do not have it. The free Python Crash Course book and the pandas getting-started guide are enough to bootstrap. Then a hands-on applied course: GTK Cyber's Applied Data Science & AI for Cybersecurity and SANS SEC595 are both reasonable starting points. The goal at this stage is to be able to load a Zeek conn.log into a pandas... - Source: dev.to / about 2 months ago
Python and data engineering for security data. Pandas for ingesting Zeek, Sysmon, EDR, and SIEM exports. Timestamp normalization to UTC, join keys across heterogeneous sources, feature extraction from raw logs. Without this layer, the ML content downstream is theater. - Source: dev.to / about 2 months ago
Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / about 2 months ago
Pandas url is the most widely used library for data manipulation. - Source: dev.to / 2 months ago
Then comes data modeling. BI tools such as Embeddable need to know how different tables and fields relate to each other. Someone has to define what terms like โtop customerโ or โQ3 revenueโ actually mean. Without this, the AI won't know where to look or how to answer even basic questions. - Source: dev.to / about 1 year ago
Itโs still pretty new but build by an experienced team. Itโs commercial software though. https://embeddable.com/. - Source: Hacker News / over 2 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
Luzmo - From data to decisions, damn fast. Embed beautiful, easy-to-use dashboards in your SaaS product in days, not months.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...
OpenCV - OpenCV is the world's biggest computer vision library
Looker - Looker makes it easy for analysts to create and curate custom data experiencesโso everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
