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WorkForce

• Sustainable development is not just about environment conservation and protection. An effective sustainable development focuses on economic inequalities like unemployment and class differences. We devised a system that can promote economic growth and increase the productivity of the employees for a better output ratio.
• Our software can help establish a strong communication channel inside an organization to increase the employee’s efficiency and precision.

Automobile sales and dealership management application

Our system helped the company to understand more about their product’s performance and helped them to place their product in the relevant market. We collected tons of data on every minute detail of various spare parts and converted the data into practical information to create an efficient pricing model.

In such large-scale industries, the scope for optimization starts from the bottom and rises to the top. Focusing on the right metrics can help you target the right audience and would also get valuable insights on the production quality and quantity. Establishing a proper demand-supply chain can make wonders in the market.

Pharmaceutical Company

We designed a system that enhances the essence of the healthcare systems while staying ahead of the competitors. The goal is to help the company to filter real data from the bunch and provide precise market pricing.

One of the biggest challenges is to identify the potential data that can be useful for the company. adulteration in this process can reduce the precision of the results. This helps the company to stay a mile ahead of its competitors while providing high-quality services to the customers.

Owlshead

Sustainable Cities and Communities

This is an outdoor exploration app with a twist! As people explore the wilderness and go on off-road adventures, this application helps them to be environmentally conscious without endangering their lives too. The application shows potential areas of risk like mines, remote factories, etc. and finds a more suitable route for the explorers to carry on their adventure. It also notifies the explorers of the endangered flora and fauna so that they can help conserve them.

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.

Data analytics system for hospitals in Saskatoon, Canada

The aim is to enhance the efficiency of the system by facilitating data collection and organization. The data is collected from the Environment Services’ workers and is built to create a report that paints the ground reality of the hygiene situation.

This can also integrate the data collected from reliable sources to manage basic services in a hospital, like sanitary and hygiene maintenance, bed availability and allocation for the patients, monitoring the hospital resources, and a lot more.

Manageteamz

ManageTeamz is developed for smoother operations in the logistics and on-demand delivery industry. It enhances the delivery operations by facilitating the delivery executives to work with precise information. It helps in route optimization, allocation of delivery executives, tracking the products and instant updating of the location, obtaining proof of delivery, etc.

This operates in the cloud platform so there is no problem with remote deployment and performance. There is also a web version of ManageTeamz that can run on any device with a web browser.

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

Our system helped the company to understand more about their product’s performance and helped them to place their product in the relevant market. We collected tons of data on every minute detail of various spare parts and converted the data into practical information to create an efficient pricing model.

In such large-scale industries, the scope for optimization starts from the bottom and rises to the top. Focusing on the right metrics can help you target the right audience and would also get valuable insights on the production quality and quantity. Establishing a proper demand-supply chain can make wonders in the market.

Event and party management application

The first priority of the development process is to make the interface clutter-free! There is a lot of data that a customer should know like the availability of the venue, timings, capacity, etc. We were conscious to not overwhelm the users with excess information. The process needs to be hassle-free and straightforward.

The backend process is as direct as possible. Admin can look at the critical data at a glance and the interface itself is fluid and responsive. The backend should have enough space to accumulate the user data for better customer engagement.

Tourism and hospitality application

We envisioned a tourist application that can run on minimum resources. The focus of this application is to help tourists and travellers to spend less time in deciding the process and more time exploring the destination. The system can run on even a meagre network, so the tourists don’t have to worry about all these factors.

There is also another side to this coin. Businesses from particular destinations can register on this platform to increase the chances of being discovered by tourists. They can also get feedback from the tourists to enhance their services.

Paragon Mobile App

This is a state-of-the-art healthcare application that helps patients to book appointments with the available physicians. The service provider had the vision to make healthcare viable by all. We shared this vision and tweaked certain parts to serve the patients even better.

This is also an open-source application that works seamlessly in both the major platforms- Android & iOS.