Seamless Data Migration with
AWS and Snowflake
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Discover how Narwal streamlined data operations for an American payment processor, overcoming maintenance hurdles, ensuring scalability, and optimizing data availability through a powerful migration to AWS and Snowflake.
Unifying Data for
Actionable Insights
Discover how Narwal transformed a global industry leader’s data platform, harmonizing data across systems to enable advanced analytics, improve decision-making, and ensure scalability for 1M+ merchants worldwide.
Data
We apply our deep domain expertise and latest industry trends to solve complex business challenges, make data driven decisions to grow revenue, optimize operations and reduce risk.
Our Expertise
Data services can help your businesses increase revenue, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge, all with the goal of boosting business performance.
Narwal brings the most effective data management, governance, warehousing strategies and transform your data into business value and outcomes.
Our Core Areas
1. Data Engineering
In the realm of data engineering, where the effective management and utilization of data are paramount, our team stands out. We specialize in offering end-to-end data engineering services that are meticulously crafted to cater to the intricate needs of businesses fostering a data-first culture.
From traditional ETL/DWH/BI platforms to cutting-edge data lakes and lakehouses, we deliver solutions that not only meet but exceed industry standards.
Datawarehouse & BI
Build traditional ETL/DWH/BI platforms that integrate structured data sources, creating dimensionally modeled, consumption-ready data warehouses and data marts.
Data Pipelines
Develop Batch, Near-Real-Time & Real-Time pipelines and modernize existing data architectures to support AI use cases, including streaming data, orchestration, governance, and data collaboration.
Data Lakes & Lakehouse
Architect, design, and model next-generation data lakes and lakehouse architectures that support scalable analytics and AI-driven insights.
Grounds-Up
Start with the end in mind understand business drivers and objectives, then design a comprehensive data-to-decision pathway.
Clinical
Define a comprehensive framework to structure vast amounts of information into clear, prioritized requirements.
Targeted
Integrate, transform, and model data to answer key business queries:
What has happened?
Why is this happening?
Outcomes-based Delivery Capabilities/Execution
Deliver results with flexible capacity-based, Factory, or outcome-driven models.
Flexible Engagement Models
Designed to fit your business needs whether scalable, project-based, or long-term partnerships.
Agility
Rapid, adaptable, and AI-driven solutions to keep pace with evolving business demands.
Datawarehouse & BI
Build traditional ETL/DWH/BI platforms that integrate structured data sources, creating dimensionally modeled, consumption-ready data warehouses and data marts.
Data Pipelines
Develop Batch, Near-Real-Time & Real-Time pipelines and modernize existing data architectures to support AI use cases, including streaming data, orchestration, governance, and data collaboration.
Data Lakes & Lakehouse
Architect, design, and model next-generation data lakes and lakehouse architectures that support scalable analytics and AI-driven insights.
Grounds-Up
Start with the end in mind understand business drivers and objectives, then design a comprehensive data-to-decision pathway.
Clinical
Define a comprehensive framework to structure vast amounts of information into clear, prioritized requirements.
Targeted
Integrate, transform, and model data to answer key business queries:
What has happened?
Why is this happening?
Outcomes-based Delivery Capabilities/Execution
Deliver results with flexible capacity-based, Factory, or outcome-driven models.
Flexible Engagement Models
Designed to fit your business needs whether scalable, project-based, or long-term partnerships.
Agility
Rapid, adaptable, and AI-driven solutions to keep pace with evolving business demands.
Businesses today move at a blinding pace. Customers expect more – and quickly. Companies need to adapt in order to meet the demands. As volumes of data are gathered, Analytics, Artificial Intelligence and supporting technologies hold the promise of new business insights and significantly reduced costs. However, many organizations don’t know how to start harnessing the power that Analytics provides.
We can help jumpstart your journey to better business insights. We can implement fully integrated Big Data technologies processing vast volumes of data from various sources with better quality and speed. Our business-focused approach helps you harness the power of Analytics and AI that are tied to measurable business outcomes.
2. Big Data & Analytics
Data Lakes & Lakehouse
Data Pipelines
Datawarehouse & BI
Narwal’s Consulting practice assesses and benchmarks an organization’s analytics maturity to ensure that it outpaces peer standards. Our team of data scientists and next-generation architects help clients make sense of the vast tracts of data within the organization by devising new monetization strategies and creating new sources of value.
Assessment of Data Needs
⏵ Assess data quality, volume, velocity, and variety to determine appropriate technologies.
Infrastructure and Technology Selection
⏵ Consider cloud-based solutions for scalability and flexibility.
Data Ingestion and Integration
⏵ Utilize ETL (Extract, Transform, Load) processes to prepare data for analysis.
Data Storage and Management
⏵ Employ data warehouses, data lakes, or hybrid solutions depending on requirements.
Data Processing and Analysis
⏵ Apply advanced analytics, machine learning, and AI algorithms for insights.
Data Visualization and Reporting
⏵ Implement dashboards for real-time monitoring and reporting.
No Infrastructure Maintenance
Creating a cluster of servers and having a team to keep them running and maintained 24X7 is just too difficult. There’s a severe lack of talent in the space that makes implementing on premise solutions even harder. Cloud allows companies to outsource their physical infrastructure and talent headaches.
Reduced Downtime and High Availability
The best part about cloud solutions is that you can start small. Instead of investing in huge physical infrastructure, you can use cloud to create a minimum viable cluster setup. If your data insights are not as valuable as you thought they would be, you can scale back the project or if they are game changing you can scale it up. You don’t need to invest in separate hardware and talent, each time you change your data strategy.
Low Setup Costs and Easier Buy-in
If you are implementing an on premise solution, you’ll be stuck with high upfront costs which makes it a luxury that few businesses can afford. Cloud solutions follow a pay as you go model and even if the project doesn’t unveil any profitable insights you will have only invested as much as you can afford. As a result, you can also convince stakeholders to buy into a cloud solution as compared to a on premise implementation.
Enhanced Security
Public clouds have grown and matured to provide heightened security that includes the ability to protect data, systems, and assets to take advantage of cloud technologies to improve your security. There are tools and techniques if well-defined and practiced then you can respond security incidents more quickly and easily.
Scalability
Sometimes you need speed as a business and time is of the essence. If you’re on an on premise solution that you are limited by the number of server clusters that you have set up. So a cloud solution gives you the flexibility that an on premise implementation cannot.
Data Lakes & Lakehouse
Data Pipelines
Datawarehouse & BI
Narwal’s Consulting practice assesses and benchmarks an organization’s analytics maturity to ensure that it outpaces peer standards. Our team of data scientists and next-generation architects help clients make sense of the vast tracts of data within the organization by devising new monetization strategies and creating new sources of value.
Assessment of Data Needs
⏵ Assess data quality, volume, velocity, and variety to determine appropriate technologies.
Infrastructure and Technology Selection
⏵ Consider cloud-based solutions for scalability and flexibility.
Data Ingestion and Integration
⏵ Utilize ETL (Extract, Transform, Load) processes to prepare data for analysis.
Data Storage and Management
⏵ Employ data warehouses, data lakes, or hybrid solutions depending on requirements.
Data Processing and Analysis
⏵ Apply advanced analytics, machine learning, and AI algorithms for insights.
Data Visualization and Reporting
⏵ Implement dashboards for real-time monitoring and reporting.
No Infrastructure Maintenance
Creating a cluster of servers and having a team to keep them running and maintained 24X7 is just too difficult. There’s a severe lack of talent in the space that makes implementing on premise solutions even harder. Cloud allows companies to outsource their physical infrastructure and talent headaches.
Reduced Downtime and High Availability
The best part about cloud solutions is that you can start small. Instead of investing in huge physical infrastructure, you can use cloud to create a minimum viable cluster setup. If your data insights are not as valuable as you thought they would be, you can scale back the project or if they are game changing you can scale it up. You don’t need to invest in separate hardware and talent, each time you change your data strategy.
Low Setup Costs and Easier Buy-in
If you are implementing an on premise solution, you’ll be stuck with high upfront costs which makes it a luxury that few businesses can afford. Cloud solutions follow a pay as you go model and even if the project doesn’t unveil any profitable insights you will have only invested as much as you can afford. As a result, you can also convince stakeholders to buy into a cloud solution as compared to a on premise implementation.
Enhanced Security
Public clouds have grown and matured to provide heightened security that includes the ability to protect data, systems, and assets to take advantage of cloud technologies to improve your security. There are tools and techniques if well-defined and practiced then you can respond security incidents more quickly and easily.
Scalability
Sometimes you need speed as a business and time is of the essence. If you’re on an on premise solution that you are limited by the number of server clusters that you have set up. So a cloud solution gives you the flexibility that an on premise implementation cannot.
2. Data Modernization
To stay ahead in today’s competitive landscape, embracing a data-first culture and modernizing data platforms are essential.
Our data modernization services empower businesses to gain operational efficiencies and a competitive edge by deriving actionable insights from their data.
Define Cloud Data Platform
Develop data strategies, roadmap definitions, and infrastructure architectures, followed by the implementation of modern cloud data platforms.
Migrate Legacy Data Platforms
Conduct introspection, discovery, and on-prem to cloud data migration from traditional/proprietary databases and appliances to Snowflake and other modern solutions.
Data Science
From Proof of Concepts (POCs) to full-scale solution implementations, we leverage data science to drive actionable business insights.
Strategize
Define a value map across data providers, business consumers, data infrastructure, and organizational goals leading to a comprehensive data programs roadmap.
Introspect
Assess and align the organization’s data maturity model, conduct detailed due diligence, and identify gaps in the existing data ecosystem.
Discover
Problem Statement / Use Case Definition
Build MVP—Data Crunching, Wrangling, and Statistical Modeling through Visualization and Predictive Analytics
Outcomes-based Delivery Capabilities/Execution
Ensure seamless execution with end-to-end data modernization strategies.
Flexible Engagement Models
Tailor modernization efforts with capacity-based, Factory, or AI-powered models.
Agility
Scalable solutions built to adapt to rapid technological advancements and business needs.
Define Cloud Data Platform
Develop data strategies, roadmap definitions, and infrastructure architectures, followed by the implementation of modern cloud data platforms.
Migrate Legacy Data Platforms
Conduct introspection, discovery, and on-prem to cloud data migration from traditional/proprietary databases and appliances to Snowflake and other modern solutions.
Data Science
From Proof of Concepts (POCs) to full-scale solution implementations, we leverage data science to drive actionable business insights.
Strategize
Define a value map across data providers, business consumers, data infrastructure, and organizational goals leading to a comprehensive data programs roadmap.
Introspect
Assess and align the organization’s data maturity model, conduct detailed due diligence, and identify gaps in the existing data ecosystem.
Discover
Problem Statement / Use Case Definition
Build MVP—Data Crunching, Wrangling, and Statistical Modeling through Visualization and Predictive Analytics
Outcomes-based Delivery Capabilities/Execution
Ensure seamless execution with end-to-end data modernization strategies.
Flexible Engagement Models
Tailor modernization efforts with capacity-based, Factory, or AI-powered models.
Agility
Scalable solutions built to adapt to rapid technological advancements and business needs.
3. Data Monetization
In today’s marketplace, data is more than just information – it’s a strategic asset that can drive significant business value.
Our data monetization services help businesses leverage their data assets to gain a competitive edge and increase their top-line revenue.
Insights
Enable data-driven decision-making for business stakeholders through:
- Descriptive, Diagnostic, and Self-Service BI Measure, Decide, and Align.
- Predictive & Prescriptive Analytics Identify patterns, discover trends, and publish insights.
AI Enablement
Converge data modernization with AI to unlock high-value insights into customers, products, and operations, fostering creativity and innovation.
Data as a Service (DaaS)
Create harmonized, enriched, and ready-to-use datasets, enabling seamless integration with internal and external platforms.
Hindsights
A rear-view approach enabling businesses to:
- Measure → Decide → Act/Align
Insights
A windshield approach enabling businesses to:
- Explore → Discover → Innovate
Foresights
Proactive, AI-driven business interventions that:
- Preempt risks → Optimize operations → Enable timely course corrections
Outcomes-based Delivery Capabilities/Execution
Deliver high-impact, business-driven results with structured monetization strategies.
Flexible Engagement Models
Offer custom-built, scalable engagement models designed for data commercialization.
Agility
Enable real-time insights and adaptive monetization approaches to maximize data value.
Insights
Enable data-driven decision-making for business stakeholders through:
- Descriptive, Diagnostic, and Self-Service BI Measure, Decide, and Align.
- Predictive & Prescriptive Analytics Identify patterns, discover trends, and publish insights.
AI Enablement
Converge data modernization with AI to unlock high-value insights into customers, products, and operations, fostering creativity and innovation.
Data as a Service (DaaS)
Create harmonized, enriched, and ready-to-use datasets, enabling seamless integration with internal and external platforms.
Hindsights
A rear-view approach enabling businesses to:
- Measure → Decide → Act/Align
Insights
A windshield approach enabling businesses to:
- Explore → Discover → Innovate
Foresights
Proactive, AI-driven business interventions that:
- Preempt risks → Optimize operations → Enable timely course corrections
Outcomes-based Delivery Capabilities/Execution
Deliver high-impact, business-driven results with structured monetization strategies.
Flexible Engagement Models
Offer custom-built, scalable engagement models designed for data commercialization.
Agility
Enable real-time insights and adaptive monetization approaches to maximize data value.
How We Deliver Value
Assessment
We will do a comprehensive assessment of your organization’s needs, create a vision that aligns with your broader corporate strategies and capabilities, and build a transformative roadmap that drives maturity and scales as you grow.
Laying the Data Foundation
We will help create your data framework, build data strategy, data governance, optimize your infrastructure, processes, systems, and create the culture to become a data-driven organization.
Generating Insights
We will help you experience improved data visibility and derive actionable insights from real-time, historical, traditional, and Big Data. We will implement advanced data analytics methods using Machine Learning, AI, and Data Science models.
Outcomes and Business Value
Deliver the Data products and solutions to increase revenue, optimize customer experience, and improve operational efficiencies. We will help you with predictive Analytics and User Behavior Analytics to measure and grow the business outcomes and innovate.
Statistics After Choosing Narwal
Reduced Time To Market
Address Rapidly Changing Business Dynamics
On-demand
'X'
Address Rapidly Changing Business Dynamics
Democratized Data Ecosystem
Any Data Anywhere Anytime & For Anyone
20-25%
Reduced TCO
Data Driven
Mindset & Culture
10-15%
Innovation Led Revenue Uplift
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Success Stories
Use Cases
Blogs
FAQs
Data Modernization is the process of transforming legacy platforms into cloud-based data platforms. This evolution is essential for aligning with current and future business needs, enabling operational efficiencies, and gaining a competitive edge. By embracing a data-first culture, businesses can derive actionable insights that drive growth.
The critical components of data modernization include:
- Modernization Strategy
- Data Migration
- Data Engineering
- Intelligence & Analytics
At a high level, a data modernization strategy outlines a value map that connects data providers, business consumers, data infrastructure, and organizational goals. This strategy is followed by a detailed roadmap for implementing data programs that deliver measurable value.
The primary business drivers for data modernization include:
- Gaining operational efficiencies and a competitive edge.
- Managing CAPEX/OPEX and reducing total cost of ownership (TCO).
- Creating a unified, centralized source of truth for data.
- Designing scalable and high-performing data services and platforms.
Narwal helps clients overcome these challenges by modernizing data platforms, embracing a data-first culture, and enabling the derivation of valuable business insights.
Data Engineering involves designing and building modern, connected, unified, and trusted data platforms using hybrid architectures, data warehouses, lakes, and pipelines. It is a key process in harnessing and managing data effectively within a data-first culture, driving innovation, improving efficiency, and fostering business growth.
A Data Pipeline is an automated system that acquires, ingests, transforms, and stores data within a data lake or warehouse. This process ensures that data is ready for analysis and decision-making.
Data pipelines can be categorized as follows:
- Batch or Cold Data Pipelines: Process large volumes of data infrequently, often during off-peak hours.
- Near-Real-Time or Warm Data Pipelines: Handle data with minimal delay, typically processing it within seconds or minutes.
- Real-Time or Hot Data Pipelines: Manage continuous streams of real-time data, requiring low latency and high fault tolerance.
A typical data lake architecture follows the Medallion Design pattern, which manages data across multiple logical layers:
- Raw Data: Source data stored as-is, often in Parquet format, supporting scalability and performance.
- Filtered/Cleaned/Integrated Data: Sanitized and lightly transformed data, with support for change data capture.
- Transformed/Enriched Data: Business-facing data, dimensionally modeled for visualization and ready for consumption.
These logical layers are based on business and resource requirements and may vary.
AWS provides a comprehensive suite of data services for building data lakes, including:
- Acquisition/Ingestion Services: DMS, Lambda, Kinesis Firehose, Data Sync
- Orchestration, Integration & Transformation Services: Glue, MWAA Airflow, EMR, AWS Batch
- Storage (Medallion Architecture, including CDC): S3 with Iceberg + Redshift/RDS
- Cataloging: AWS Glue Catalog/Crawler
- Federation & Visualization: Athena, QuickSight
Data Monetization is the process of transforming data into a strategic asset that drives business value and growth. It can be approached in two ways:
- Direct Monetization: Selling or trading data through Data-as-a-Service (DaaS) tools, embedded analytics platforms, or data sharing.
- Indirect Monetization: Using data for process improvement, product development, sales, marketing, and other efforts that enhance profitability.
Data analytics can be categorized into three types:
- Hindsight: Rear-view analysis enabling businesses to measure, decide, and act or align based on past data.
- Insight: Forward-looking analysis that allows businesses to explore, discover, and innovate.
- Foresight: Predictive analysis that enables timely business interventions, course corrections, and optimization.
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