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No-code/low-code data infrastructure to enable do AI (Artificial Intelligence), ML (Machine Learning) and visualization at enterprise scale and quickly. The platform can be deployed: on-cloud or on-premises.
The platform helps connect your structured/unstructured and real time data sources, catalog, discover your data, create pipeline and run at scale, pre-built libraries take care of training the model based on your target variable and optimize, click on any data source and start to visualize with pre-built charts. Platform can be used for both historical data and real time data.
Helps save time, efforts, costs, resources spent on building data engineering infrastructure to do analytics. It helps being able to do AI and ML quickly.
Data engineering & analytics platform that captures the entire lifecycle of data science end-to-end, starting from data discovery phase until machine learning insights that are derived from these discovered data sources, including data preparation, data processing, data-warehouse/data-lake management.
It can capture and discover data from multiple different data sources. It builds a catalog which the users can browse through without necessarily having to actually go to the source and take a look at the data. It synchronizes data coming from various different sources, and put them all together with operators. It allows you to define features for your machine learning models. Visualize important features, generate new features. Model training choosing models from various libraries. AutoML capabilities can run multiple models in parallel. Built-in charting library enables visualize every single data point. Reports enable add multiple visualizations and share as links or pdfs. All data sources and datasets can be previewed in a tabular format. In case of monitoring real-time streams of data, the platform automatically detects the data source and provides switch to toggle between historic data or real-time data.
Some of the interesting aspects:
Ingest data from various data places (structured, unstructured, real-time).
Bring data from the cloud or even from on premise infrastructure.
We do not store customer’s data to perform analysis. We work on the Metadata.
Save about 70% of the time of the data analytics team
Major portion of data analytics team’s activities include: data integration, data engineering, data preparation, structuring the data, data transformation. These take almost 70% to 80% of the data analytics team’s time.
These are automated within the system.
Also Data Catalog and Lineage becomes easy for the teams and makes data discovery easy and efficient.
With your existing data analytics team you can achieve/do more in your analytics initiatives, as this platform will take out so much of effort/time.
Data Lifecycle Management
The end-to-end data lifecycle management platform drives cost savings by reducing redundancies and optimizing data workflows, while offering scalability to handle growing data needs seamlessly.
By automating processes and centralizing data operations, it boosts efficiency and enhances productivity, empowering teams to focus on delivering actionable insights faster.
View the following sections for details on interest areas:
Useful resources:
(1) Forbes magazine article: Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says
(2) Forbes magazine article: Building your Big Data Infrastructure: 4 key components every business needs to consider
(3) Analytics India Magazine article: Build vs Buy for Data Science Platforms
https://analyticsindiamag.com/build-vs-buy-for-data-science-platforms/
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