Data Engineering Services
Data Prism offers end-to-end data engineering solutions for high-performance pipelines, integration, and scalable architectures for business intelligence.
Data Engineering Services we Offer
We bring clarity and control to complex data environments. From real-time ETL pipelines to modern data lakes and cloud migration, our services are tailored to drive agility, security, and performance across your data stack.
Data Integration
Unify data from multiple sources and platforms into a single, consistent view. We synchronize systems, APIs, and databases to ensure seamless access and consistency across your organization.
Data/ETL Pipelines
Automate the entire data journey — from ingestion to transformation and delivery. We build ETL/ELT pipelines optimized for scale, real-time streaming, and efficient orchestration.
Data Warehousing
Create centralized repositories that support fast querying and scalable storage. Our solutions are designed for BI tools, dashboards, and high-volume analytics.
Data Cloud Strategies
Leverage the power of cloud platforms like AWS, GCP, and Azure with custom strategies that match your infrastructure, budget, and growth goals.
Data Migration
Seamlessly migrate legacy systems, databases, or cloud platforms with zero data loss and minimal downtime. We ensure a secure and smooth transition to your new architecture.
Data Lake Implementation
Build modern data lakes to store structured, semi-structured, and unstructured data in its raw form — enabling advanced analytics, ML, and data discovery.
Data Management
Govern and maintain your data with best-in-class management practices covering accessibility, lineage, compliance, and lifecycle optimization.
Technologies We Use for Data Solutions
Programming Languages
Node Js
Python
JavaScript
Data Orchestration
Apache Airflow
Azure Data Factory
Databricks
Dagster
RESTful Services
Postman
Rest
SOAP
Requests
Databases
MySQL
SQL Server
PostgreSQL
MongoDB
DynamoDB
SQLite
Redis
Firebase
Data Warehouses
Snowflake
BigQuery
Redshift
Data Visualization
PowerBI
Tableau
Looker Studio
Cloud Platforms
AWS
Azure
GCP
Heroku
Data Transformation
AWS Glue
Talend
Apache Kafka
DBT
Security
OAuth
SSL/TLS
Containerization
Docker
Kubernetes
Node Js
Python
JavaScript
MySQL
SQL Server
PostgreSQL
MongoDB
Snowflake
BigQuery
Redshift
Apache Airflow
Azure Data Factory
Databricks
Dagster
AWS Glue
Talend
Apache Kafka
DBT
PowerBI
Tableau
Looker Studio
AWS
Azure
GCP
Heroku
Docker
Kubernetes
Postman
Rest
SOAP
Requests
OAuth
SSL/TLS
Our Data Engineering Process
Before any transformation begins, we apply rigorous checks to validate data accuracy, completeness, and consistency, preventing issues that could compromise reporting or decision-making later.
Using ETL/ELT processes, we clean, enrich, and reformat raw data into a structured, analytics-ready format tailored to your specific business intelligence or machine learning use cases.
Transformed data is securely stored in high-performance storage solutions like data warehouses, lakes, or cloud-native repositories, designed to scale and support real-time or batch querying.
We automate recurring workflows and manage task dependencies using orchestration tools like Airflow or Prefect, ensuring timely, error-free, and fully governed data operations.
The final processed data is delivered through dashboards, APIs, or reporting layers, making it accessible to business users, analysts, and downstream systems in real time or scheduled intervals.
Success Stories
We’ve partnered with fast-growing startups and global enterprises to design intelligent data ecosystems that power smarter decisions and digital growth.
Amazon Vendor Central Project
Our smart security clients (August Home and Yale Security) are huge vendors on Amazon and needed a way to get useful insights for their traffic, sales, inventory, and sales forecasting data. The reports had to be generated on a daily basis and according to their specific criteria.
Instagram-Facebook API Integration (Freestak.com)
Freestak, a marketplace for endurance influencers, wanted to integrate key insights coming from marketing campaigns with their associated influencers. Freestak required obtaining post data and engagement metrics of posts, stories and reels of Instagram influencers.
Food Ordering Scraper (Loop)
Our client needed merchant-side data of orders coming to restaurants through 3 major ordering companies (DoorDash, Uber Eats, and Grubhub). We were required to implement a smart algorithm to retrieve such a huge volume of information and prevent blocking, duplication, and other problems.
High Growth Startup Finder (Redpoint Ventures)
As a Venture Capitalist, our client found it very tiring and expensive to find startup leads that they can investigate further for potential funding rounds. We were required to come up with a smart algorithm to find startups which can possibly be the next best investment for our client.
Automated Newsletter Emails
We fetched the users’ data from an API and checked all the subscriptions of every user to create a filtered list. Once we had the list, we created a templated transactional email which was then used to send relevant newsletters to all the subscribers.
Technology Stack
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Languages
C#
JavaScript
Java
Python
Frameworks
.Net
Node JS
Angular
React
Vue JS
Spring
Django
Falsk
Database management
PostgreSQL
Microsoft SQL
MySQL
MongoDB
Cloud Offerings
Cloud
Amazon Web Services (AWS)
Microsoft Azure
Google Cloud
Mobile
Swift
Kotlin/Java
HTML5
React Native
Xamarin
Blockchain
Ethereum
Hyperledger
Contact Us
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