Data Engineering
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Our Services
Our Cloud services enhance clients’ experience, business outcomes & organization’s capabilities. The cloud enablement service assists our clients to meet their business needs.
Technology Capabilities
Our Microsoft SQL engineers use this database management system for the primary function of storing and retrieving data as requested by other software applications—which may run either on the same computer or on another computer across a network.
This is a cloud-based data integration service that allows our engineers to create data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. We shall not be able to store any data here since Azure Data Factory does not store any data itself.
AWS Lambda is a Serverless, event-driven compute service used by our engineers to run the code virtually on any type of application or backend service without provisioning or managing servers. We can trigger Lambda from over 200 AWS services and software-as-a-service (SaaS) applications and only pay for what you use.
With Power Apps, our engineers can customise, and develop your websites & apps using drag-and-drop technology and built-in templates. By being a part of your digital transformation journey, our Power Apps experts can guide you from use case analysis to maintenance and support.
As a part of Microsoft Azure cloud service, we use Microsoft Graph Service and Microsoft Graph API . This can run on any Microsoft cloud platform such as Azure, on-premise systems or Google Cloud Platform. To connect to Microsoft Maps, launching it on Azure cloud is the easiest way.
Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes using AWS-designed hardware and machine learning to deliver the best price-performance at any scale.
Databricks runs on AWS and integrates with all of the major services you use like S3, EC2, Redshift and more. The Databricks Lakehouse Platform sits at the heart of the AWS ecosystem and easily integrates with popular Data + AI services like Kinesis streams, S3 buckets, Glue, Athena, Redshift, QuickSight and much more.
Snowflake offers a cloud-based data storage and analytics service, generally termed “data-as-a-service”. It allows corporate users to store and analyze data using cloud-based hardware and software. It offers unified data across clouds to accelerate and scale analytics with near-zero administration.
Key Data Challenges
01
Organising
Data
Data organization helps us to arrange the raw data in an understandable order. Organizing data include classification, pictorial representation, graphical representation, etc.
02
Identify Right Tools
A huge amount of unused data may result in adverse business effects.
03
Visual Data Presentation
Operating with poor KPIs increases the risk of failure due to a lack of quantification
04
Data
Scalability
Crucial information is often overlooked when not presented right
05
Data
Security
Maintaining data on-premise leads to stunted scalability issues and even loss of data
Our Award-Winning Team
A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better
50+
AI & ML
Engineers
40+
AI & ML
Projects for
reputed Clients
5yrs
in AI & ML
Engineering
Success Stories
Data Visualisation Platform for Enterprises
Data Analysis for FinTech Industry
Data Automation for Marketplace Industry
ML for Retail Demand Forecasting
Industries We Serve
HealthTech
ManuTech
FinTech
MarTech
ConsTech
Others
Resource Capabilities
Machine Learning
Engineers
Data
Engineers
Data
Analyst