What i s Pirllabs?
Pirllabs is a No-code platform to rapidly build scalable, secure enterprise-grade apps and analytics with integrated AI/ML. PIRL can be deployed on industry-standard open-source stacks on the cloud or on-premises.
How Customers are using it
Some applications developed and deployed using Pirllabs.
Pirllabs Modules
Pirllabs platform has a scalable database as the back bone with a data access control layer on top of it.The development platform (SDK) is used to develop applications and can be deployed on the Run time engine (RTE). Various web and mobile applications run on the run time engine. Users can develop various modules and applications and market them using the market place.
Do You Want to Develop
Web applications usually have a back end application server, a database server, business logic scripted in some language like php or python with embedded sql queries etc. With scaleData, users can build, deploy and manage the entire web application without writing a single line of code. Web applications can include static pages with predefined content, dynamic pages for data input as well as data output with charts and graphs etc. Users can develop the data input forms and web pages using a drag and drop interface. Various charts and graphs can be configured using a no code interface.
Some users will prefer to have their own web front end developed using various tools or scripts as application front ends are very application and user specific. Users can use their own web front end and use scaleData to support the back end of the application. They can use scaledata to provide their APIs, database, data analytics and scalability and security requirements.
Some application require large volumes of data to be processed and results extracted. These processes could be statistical, AI/ML or data ingestion in nature. scaledata can be used to develop such applications for large volume data ingestion, storage, analysis and results extraction. The extracted results can be stored in database tables and users can choose to read and represent this curated data using their existing user interfaces and applications.
While scaledata can be used for developing and deploying web applications, mobile apps will require different set of content to be shown in a mobile screen form factor. Even though the application may be responsive, a mobile screen is too small for certain type of content to be presented effectively. scaledata provides capability for users to select different representations of the same data to be presented to the same user on a mobile phone app differently compared to a web browser on a large screen computer.
While data analytics provide the ability to analyze data at rest (in a database), there is tremendous value to be extracted by analyzing data while the data is being ingested into the system in near real time. Stream analytics enables users to analyze data on a per stream basis while maintaining states as well as the ability to notify real time events.
Usually, no code environments allow users to select their own choice of databases. But, for application performance and scalability, database is the most critical part of the system. Database scalability requires users to be able to run multiple database server farms and be able to transparently handle such scaleout capabilities. scaleData data’s shard manager layers automatically enables users to shard the data and store it across multiple tables, databases as well as server farms in a transparent manner and be able to access them.
Typical no code environments are good at allowing users to create business logic for data ingestion while they depend on external tools to do data analytics. Value of data being ingested is only as good as the analytics you perform on the data and the insights extracted from the data. In our experience, data analytics keep changing periodically as users want to extract more and more insights from the same set of data. scaledata’s data analytics SDK and run time engine allows users to perform various kind of streaming analytics as well as analysis on data at rest using statistical as well as AI methods.
Today, most of the data analytics using AI is heavily dependent upon various scripts and back end libraries. It is very difficult for a normal human being to even understand how one can use AI. scaleData has simplified the AI interface in such a manner that users can use AI functions exactly the same way as statistical functions. Machine learnt models are stored as functions so that users can use them in their everyday reports.
Our experience of machine learning shows that it is a continuous process of training the models and extracting improved accuracy and efficiency. scaleData provides the ability for users to setup training pipelines such that models can be trained on cumulative sets of data or models on a per site set of data. The trained models can be applied on a cumulative basis or on a per site basis. Training can be set using a pure drag and drop interface without any scriptin.
When data is ingested into the system, it needs to be cleaned, filtered, outliers discarded, insights extracted in near real time as well as stored in proper formats. Appropriate notifications and escalations need to be initiated. Such a set up to achieve the above is termed as a data pipeline. scaleData provides the ability for a user to set up data pipelines with a pure drag and drop interface. The whole pipeline can be stored as an API and can be called from an external system. scaleData supports both client and server nodes and these can be used to wither PUSH or PULL data as input to pipes.
When data is ingested into the system, it needs to be cleaned, filtered, outliers discarded, insights extracted in near real time as well as stored in proper formats. Appropriate notifications and escalations need to be initiated. Such a set up to achieve the above is termed as a data fabric. scaleData provides the ability for a user to set up data fabrics with a pure drag and drop interface. The whole chain can be stored as an API and can be called from an external system. scaleData supports both client and server nodes and these can be used to wither PUSH or PULL data as input to pipes.
Users use various kinds of AI models as well as statistical functions as part of the data analysis chain. scaleData supports both AI as well as statistical functions using similar interfaces so that users can mix and match both these algorithms as part of their data analysis.
Sending real time PUSH notification to users through mobile APP adds tremendous value in certain types of applications. Most no code platforms in the world expects the users of the platform to tie up with third party push notification servers. scaleData provides an inbuilt PUSH notification service as part of the platform. Users can use the standard mobile APP or use the scaledata PUSH notification client library to avail the PUSH notification services as part of their application.
Web applications usually have a back end application server, a database server, business logic scripted in some language like php or python with embedded sql queries etc. With scaleData, users can build, deploy and manage the entire web application without writing a single line of code. Web applications can include static pages with predefined content, dynamic pages for data input as well as data output with charts and graphs etc. Users can develop the data input forms and web pages using a drag and drop interface. Various charts and graphs can be configured using a no code interface.
Some users will prefer to have their own web front end developed using various tools or scripts as application front ends are very application and user specific. Users can use their own web front end and use scaleData to support the back end of the application. They can use scaledata to provide their APIs, database, data analytics and scalability and security requirements.
Some application require large volumes of data to be processed and results extracted. These processes could be statistical, AI/ML or data ingestion in nature. scaledata can be used to develop such applications for large volume data ingestion, storage, analysis and results extraction. The extracted results can be stored in database tables and users can choose to read and represent this curated data using their existing user interfaces and applications.
While scaledata can be used for developing and deploying web applications, mobile apps will require different set of content to be shown in a mobile screen form factor. Even though the application may be responsive, a mobile screen is too small for certain type of content to be presented effectively. scaledata provides capability for users to select different representations of the same data to be presented to the same user on a mobile phone app differently compared to a web browser on a large screen computer.
While data analytics provide the ability to analyze data at rest (in a database), there is tremendous value to be extracted by analyzing data while the data is being ingested into the system in near real time. Stream analytics enables users to analyze data on a per stream basis while maintaining states as well as the ability to notify real time events.
Usually, no code environments allow users to select their own choice of databases. But, for application performance and scalability, database is the most critical part of the system. Database scalability requires users to be able to run multiple database server farms and be able to transparently handle such scaleout capabilities. scaleData data’s shard manager layers automatically enables users to shard the data and store it across multiple tables, databases as well as server farms in a transparent manner and be able to access them.
Typical no code environments are good at allowing users to create business logic for data ingestion while they depend on external tools to do data analytics. Value of data being ingested is only as good as the analytics you perform on the data and the insights extracted from the data. In our experience, data analytics keep changing periodically as users want to extract more and more insights from the same set of data. scaledata’s data analytics SDK and run time engine allows users to perform various kind of streaming analytics as well as analysis on data at rest using statistical as well as AI methods.
Today, most of the data analytics using AI is heavily dependent upon various scripts and back end libraries. It is very difficult for a normal human being to even understand how one can use AI. scaleData has simplified the AI interface in such a manner that users can use AI functions exactly the same way as statistical functions. Machine learnt models are stored as functions so that users can use them in their everyday reports.
Our experience of machine learning shows that it is a continuous process of training the models and extracting improved accuracy and efficiency. scaleData provides the ability for users to setup training pipelines such that models can be trained on cumulative sets of data or models on a per site set of data. The trained models can be applied on a cumulative basis or on a per site basis. Training can be set using a pure drag and drop interface without any scripting.
When data is ingested into the system, it needs to be cleaned, filtered, outliers discarded, insights extracted in near real time as well as stored in proper formats. Appropriate notifications and escalations need to be initiated. Such a set up to achieve the above is termed as a data pipeline. scaleData provides the ability for a user to set up data pipelines with a pure drag and drop interface. The whole pipeline can be stored as an API and can be called from an external system. scaleData supports both client and server nodes and these can be used to wither PUSH or PULL data as input to pipes.
When data is ingested into the system, it needs to be cleaned, filtered, outliers discarded, insights extracted in near real time as well as stored in proper formats. Appropriate notifications and escalations need to be initiated. Such a set up to achieve the above is termed as a data fabric. scaleData provides the ability for a user to set up data fabrics with a pure drag and drop interface. The whole chain can be stored as an API and can be called from an external system. scaleData supports both client and server nodes and these can be used to wither PUSH or PULL data as input to pipes.
Users use various kinds of AI models as well as statistical functions as part of the data analysis chain. scaleData supports both AI as well as statistical functions using similar interfaces so that users can mix and match both these algorithms as part of their data analysis.
Sending real time PUSH notification to users through mobile APP adds tremendous value in certain types of applications. Most no code platforms in the world expects the users of the platform to tie up with third party push notification servers. scaleData provides an inbuilt PUSH notification service as part of the platform. Users can use the standard mobile APP or use the scaledata PUSH notification client library to avail the PUSH notification services as part of their application.
Example Application Design Using Pirllabs
Build Your Data Pipline in Minutes With No-Code Interface
Are You Facing Any Of These Challenges?
- Takes too long build dashboards
- Takes too long to filter, clean and analyze data
- Too many data sources to contend with
- Too much dependence on data scientists
- Spending all my time on getting to correct report as opposed to analyzing data
- Scalability issues and takes too much processing time to generate reports
- Once reports are created, cannot easily based on operational feedbadk
- Not able to ingest data faster into the system
- Too many input data formats
- Takes too long idea to reality
- Too many reports and I dont know where the report is and I am new
- Want to run some reports across some selectd sites only
- How do I run the same report across thousands of sites
- Need an asset management system that maintains all the assets data at site to enable proper data analytics
- Want to analyze data while it is coming into the system (real streaming analytics)
- Want a system to ingest data, analyze in realtime, store data in a scalable manner and create dashboards rapidly
- Want a system that can analyze data and create visualizations that can be incorporated as part of customer portal
- Want a system that can help you run summary analysis at the back end for easy visualizations
Pirllabs is Your Answer
- Specialized for streamming data analysis & time series data.
- Statistical AI/ML functions
- Parallel processing in the backend
- Ability to scheduled analytics
Benefits
Pirllabs Features
Convert your raw data into useful business insights with a no-code drag and drop platform.
The solution can be deployed on premises or in the cloud
Scalability
Highly scalable with inbuilt parallel processing, multi application server support, iteration, scheduling and an inbuilt shard manager for storage scalability
Drag and Drop
No need to write any code. If you have custom scripts, you can integrate them using Lamda function capability
User Experience
Designed for simple User experience
Security
Follows industry standard security practices
Data in Real-Time
Analyze stream data in combination with already stored configuration information. Buffers, sessions, windows, real time gauges add power to streaming analytics
Dashboards
Multiple templates, ability to integrate with your custom web template, integrate with your application, 40 different multi dimensional visualizations
CUSTOM DESIGN
If you need any application to be custom designed, our expert team can provide design and development service on top of this platform for rapid roll out
AI / ML
Pre built ML functions to train ML models. Saved ML models can be executed on data
IP Protection
The system is semantic agnostics and your IP is encapsulated in your map reduce graph and that is your IP
Run time & SDK separation
You can use SDK to build applications and deploy it on the run time engine. End users will view only the end application.
Input/ Output APIs
Most powerful feature. Enables you to create entire web application. Supports PUSH and PULL on input and output side
Design-Test and Deploy
Using SDK, one can design an entire web application, mobile APP, API handling, Stream analytics, IoT data handling, ETL and analytics, north bound interfaces. It can be tested on the SDK and deployed as an application for users
Performance Monitor
A robust and detailed performance monitor enables users to debug performance issues, data issues, scalability issues and access control issues
Individual Workspace
Each user work space on the SDK as well as the run time engine is access controlled depending upon the user access permission
Bulk Injection
System can be used to import bulk data from various data lakes and analysed and stored back into the data lakes using output storage or API
Custom Script
Ability to run your custom scripts as part of the map reduce graph so that you can use the rest of the system for ingestion, cleaning, filtering and storage
Make the start your
15 days free trial
Convert your raw data into useful business insights with a no-code drag and drop platform. The solution can be deployed on premises or in the cloud
Risk Free No Credit Card Required
End to-End Data Secure
ASSETS
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