Our Portfolio
Crafted With Love
Built With Passion...

WorkForce

Decent Work and Economic Growth

• Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
• Make cities and human settlements inclusive, safe, resilient and sustainable
• Digital WorkSmart is a platform that improvises employees communication across departments and with customers. It offers chat / call / video interaction within teams and dynamic forms allows for the creation and completion of orders and tasks. Itincreases the overall customer experience and transforms the way business operates that offers sustainable economic growth for all.

Automotive Industry

Data engineering and analysis of vehicle services and spare parts life. Helps customers to identify the life of a specific part and predict the AMC for vehicles. Based on years of data with parts and Its utilization along with predictive analysis, the application recommends the profitable price margin for the automotive spare parts Industry.

Gone is the guess-work of years ago. Know your market, price your parts right and stay ahead of the competition.

Pharmaceutical Company

we crafted a system that lets clients their sales data along with the market trends. Captures health information from various public sources and compares it against the customer’s pharma business to capture micro details that help the business to stay competitive.

Because staying on top of all of today’s pharma information is now in your hands. Future-proof in today’s ever-changing environment.

Owlshead

Sustainable Cities and Communities

We have built trails applications that protect sensitive natural resources and threatened species such as desert tortoise and squirrels. This helps trail users to be on a safer side, know where they are located and how they can enjoy the natural environment by lowering the risks posed by abandoned mines, unsafe terrain, and other fire hazards. This Project seeks to protectthe prospects of a sustainable future for and safe human settlement and recreation activities on our public lands.

Data Quality Management

Objective

Automation is the final goal. Data from both the data lake and other sources are organized and accumulated to understand the quality of the data. The objective is to segregate the high-quality data from the crowd to ensure efficiency in the process.

The high-quality data is then ranked for easy access. This helps the data scientists who monitor the manual process to bring out their best game.

Solution architecture

Our workflow is airtight and we intend to keep it that way. The diagram below is a simplified version of high-level data architecture.

Tech-Stack

• Great Expectations
• Azure Databricks
• Azure Blob storage
• Azure Data lake

Data Quality Analyzing Process

• Collecting sampled dataset from data table using spark in the Azure databricks.
• Mounting the Blob Storage path of the Great Expectation folder into the Azure databricks.
• Preparing the batch of data to be evaluated.
• Executing the expectation validation on top of the selected sampled data.
• Collecting the Great Expectation results and store those results in a separate data lake table.
• Consuming the result data using the Power BI to visualize the data.

Azure synapse ETL with DW

Objective

The objective is to accumulate as much data possible from sources like databases, flat files, web services, RSS feeds, etc. and ingest the data into Azure synapse and transform it visually, like facts and dimension tables.

The ingest pipeline design shows how the raw data is transformed from the source to synapse and further explains the functions of the Azure data factory in the data ingestion phase.

Raw data ingestion design

Step1:

• Surrogate key – MD5 Hash based on natural key
• MD5_HASH column – for all dimensional attributes to track the changes
• LAST_LOAD_TS - housekeeping column
• Dimensional attributes

Step 2: Create Store Procedure to perform Inserts / Updates for loading SCD Type 1 dimensions. This procedure takes source table, target table names and primary key column as inputs

Step 3: Create and load Dimensional tables from Staging VIEWS and Store Procedure

Azure synapse ETL with snowflake

Objective

The objective is to accumulate as much data possible from sources like databases, flat files, web services, RSS feeds, etc. and ingest the data into Azure synapse and transform it visually, like facts and dimension tables.

The ingest pipeline design shows how the raw data is transformed from the source to synapse and further explains the functions of the Azure data factory in the data ingestion phase.

Raw data ingestion design

The diagram represents the process of copying data from various sources like SAP ECC, flat files, and ARGUS -SQL server to the sink on cloud Azure synapse analytics.

The process of configuration driven framework deals with the copying of data from sources to target, using a CSV file that consists of source and destination schema, table and path info, etc. which is stored in ADLS2.

Step 1:

Pipeline reads data from config file to get database, tables, path

Step 2:

Using ADF linked service and data set objects, copy data from source to sink

Step 3:

All raw data ingestion load is configured to perform “Truncate and load” method

Azure synapse- Fintech

Objective

The objective is to accumulate as much data possible from sources like databases, flat files, web services, RSS feeds, etc. and ingest the data into Azure synapse and transform it visually, like facts and dimension tables. The transformed data is now connected to the SQL dedicated tool to power BI for generating reports based on their business needs.

Ingestion RAW data

The data for the Fintech application needs to be extracted from various sources like PostgreSQL, MongoDB, and flat files. This data is then ingested into Azure data lake gen 2 since data ingestion will be huge between PostgreSQL and synapse.

Tech-Stack

• Great Expectations
• Azure Databricks
• Azure Blob storage
• Azure Data lake

Data Quality Analyzing Process

• Collecting sampled dataset from data table using spark in the Azure databricks.
• Mounting the Blob Storage path of the Great Expectation folder into the Azure databricks.
• Preparing the batch of data to be evaluated.
• Executing the expectation validation on top of the selected sampled data.
• Collecting the Great Expectation results and store those results in a separate data lake table.

The diagram represents the process of copying data from various sources like SAP ECC, flat files, and ARGUS -SQL server to the sink on cloud Azure synapse analytics.

The process of configuration driven framework deals with the copying of data from sources to target, using a CSV file that consists of source and destination schema, table and path info, etc. which is stored in ADLS2.

Step 1:

Pipeline reads data from config file to get database, tables, path

Step 2:

Using ADF linked service and data set objects, copy data from source to sink

Step 3:

All raw data ingestion load is configured to perform “Truncate and load”

Azure Synapse destination:

Pipeline auto-creates tables directly based on source column names and data types

Data transformation design

It describes how the raw data is visually represented and restructured into facts and tables as per the designed data model, using star schema.

The process of data transformation is carried out using two methodologies- SQL script-driven and Code free.

• SQL script-driven

Pipeline reads data from config file to get database, tables, and path.

• Code-free

Using ADF data flow activity to transform & load data into the synapse.

Transformation using T-SQL

Both our Dim and Fact implement using Slow changing dimensional type1 approach in TSQL.

Dimension And Fact Load:

Step 1: Create SQL views for dimension that holds transformation logic

• Consuming the result data using the Power BI to visualize the data.

Saskatoon Health application

Saskatchewan Health Authority Web is designed for the environmental services team to know about the dynamically changing healthcare information systems. It makes the team to become aware of the quality and hygiene followed at Saskatchewan healthcare services.

Doctors have to pass the authority identity standards in order to keep track of the Acute Bed facility in a day with updates provided in every 5 hours duration. It enables healthcare personnel’s in levelling with the management of clinical processes. Saskatchewan Health Authority can now work proactively in treating critical care patients and eventually attend to other patients out lastingly.

Manageteamz

ManageTeamz Web is an automated delivery management system that can make each and every delivery process seem easier through connecting drivers and customers exclusively. It has a powerful dashboard to handle delivery persons, view customer’s incoming orders, allocate task and real-time track deliveries. It provides delivery drivers the flexibility to update their status directly to the admin dashboard with options to upload reports and feedbacks.

ManageTeamz Web is made compatible for viewing from any laptops and desktop systems. Our Web Developers made it an Open Source based application that is built using PHP programming, Angular, MySQL, XCode and Android Studios.

Elite

The goal of Elite is to facilitate content regulation and monitoring. It also doubles down as an employee monitoring application. Elite creates these filters directly at the router. This ensures a high level of security for the organization. It also monitors the employee’s internet activities and presents them in visual form for easy interpretation.

Elite is available for both Android and iOS devices. There is also a web version of it for cross-device functionality. The web version is created using angular and Node JS for better compatibility. Python is used in backend processing.

Futuristic data intelligence system

The goal of this software is to devise a strategy that maps an adequate pricing range for the products with a correlation to the market price. As there are heaps of data to analyze, we incorporated AI and Machine Learning tech to create the tool.

There is a scope for expansion as the franchise system decentralizes the entity and allows innovation in that particular system. This changes the landscape completely as there are many possible tweaks that can be done in the menu.

The tool can be efficient if it is integrated with cloud tech, hence we have used AWS. To seamlessly manoeuvre through heaps of data, we have used Python, Pandas, Jupyter and Numpy. This proved helpful during data analysis. We have used Angular for building a fluid interface. Django framework and Python is used for backend processes.

Automobile sales and dealership management application

This application is designed based on the end-user experience and ease of access on the admin side. The real-time challenges in the dealership management are overcome by the minimal design and verifying the credibility of the sources.

The payment gateway is simpler than ever. The goal is to be functional with the minimal available resources. Customers can choose from a variety of options that can be streamlined by different filters.

Event and party management application

The practical challenges in an event management application are maintaining the volume of the customers and updating the venue status without disrupting the user experience. We focused on the fluidity factor to bring out the best experience.

There needs to be a strong backend process that accumulates and organizes various data collected from the application. Admin end process avoids complex workflows and is made as simple as possible.

Tourism and hospitality application

The primary goal of this application is usability and cross-device functionality. We consciously avoided the cluttering of features and laid out a simple and straightforward design for maximum customer engagement.

This platform allows both tourists and businesses to find each other. Local businesses can use this platform to increase their customer engagement and tourists can explore all the nearby attractions and plan their trip ahead.

Paragon Mobile App

Paragon is a Healthcare Mobile App that is built for health deteriorating patients to fix up appointment at real-time basis with any one of the available Physicians.

Paragon is initially made to be an Open Source Mobile App that works perfectly well across both iPhones and Android Mobiles. It follows the MVC model and selects technology stack from Angular