What does a data platform engagement typically include?

Architecture, lakes and warehouses, scalable pipelines, quality and governance, cloud platform adoption, and foundations for analytics and AI workloads.

Can you support both batch and real-time data?

Yes. We design for batch and streaming patterns—including Kafka-based pipelines where real-time access is required.

How does this relate to AI and analytics?

Reliable data foundations are required for trustworthy analytics and production AI. This service builds the infrastructure those programs depend on.

Service

Data Engineering & Cloud

Build the data foundation behind analytics and AI

Discuss this service How We Think

Quick overview

Data Engineering & Cloud. Build the data foundation behind analytics and AI

Who is this for?: Data, analytics, engineering, and technology leaders building scalable data foundations for analytics and AI.
Why choose XELARVIS?: XELARVIS designs and implements scalable data platforms that make information reliable, accessible, secure, and ready for analytics and AI.
How do we deliver?: Architecture → lakes/warehouses → pipelines → quality & governance → cloud platforms → MLOps infrastructure.
Where we apply this: Technology, Banking & Financial Services, Manufacturing, Retail & E-Commerce, Logistics & Supply Chain, Healthcare & Life Sciences
Expected outcomes: Reliable, scalable data foundations that enable analytics, AI, and enterprise decision-making.

What we help organizations do

Design modern data architectures
Build data lakes, lakehouses, and warehouses
Develop scalable ETL and ELT pipelines
Integrate batch and real-time data
Modernize data platforms in the cloud
Improve data quality and governance
Build analytics-ready data foundations
Establish MLOps and AI infrastructure

Business outcome. Reliable, scalable data foundations that enable analytics, AI, and enterprise decision-making.

Overview

Challenges we solve

Enterprises need reliable data platforms, pipelines, and governance foundations to power analytics and AI at scale.

XELARVIS designs and implements scalable data platforms that make information reliable, accessible, secure, and ready for analytics and AI—with governance and quality built in.

Outcomes

Key benefits

01
Data Architecture
Target architectures for analytics and AI workloads.
02
Data Lakes
Flexible storage for structured and unstructured data.
03
Warehouses
Trusted analytical warehouses for reporting and BI.
04
Pipelines
Reliable, observable data movement at scale.
05
ETL/ELT
Batch and incremental transformation patterns.
06
Big Data
Distributed processing for high-volume workloads.
07
Cloud Data Platforms
AWS, Azure, and GCP foundations for the data estate.
08
Governance
Access, lineage, catalog, and compliance controls.
09
Quality
Profiling, rules, and monitoring for trusted data.
10
MLOps Infrastructure
Platform support for model training and deployment.

Capabilities

Data Architecture
Data Lakes
Data Warehouses
Data Pipelines
ETL/ELT
Big Data
Cloud Data Platforms
Data Governance
Data Quality
MLOps Infrastructure

How we deliver

How we move from business problem to governed delivery for this service.

01
Data Architecture Assessment
Evaluate systems, sources, quality, and platform requirements.
02
Platform & Lakehouse Design
Design data lakes, warehouses, and scalable architectures.
03
Pipeline & ETL/ELT Development
Build automated pipelines for reliable movement and transformation.
04
Cloud Data Platform Implementation
Deploy secure infrastructure for storage, analytics, and AI.
05
Big Data & Streaming
Process high-volume batch and real-time workloads.
06
Governance & Data Quality
Access controls, lineage, quality rules, and monitoring.
07
MLOps Infrastructure
CI/CD, model deployment foundations, and operational monitoring.
08
Managed Optimization
Ongoing performance, cost, and reliability improvements.

Deliverables

Data architecture and platform design
Lakes, warehouses, and pipeline implementations
ETL/ELT and streaming workloads
Governance, quality, and observability controls
MLOps infrastructure for analytics and AI

Outcomes

Reliable data foundations for analytics and AI
Lower latency and higher trust in critical datasets
Governed access, lineage, and quality monitoring
Scalable cloud cost and performance posture

Technologies

data

Apache Spark

Large-scale data processing.

data

Apache Kafka

Real-time data streaming.

data

Snowflake

Cloud data warehouse platform.

data

Databricks

Lakehouse analytics and ML.

cloud

AWS

Secure, scalable cloud foundations.

cloud

Microsoft Azure

Enterprise cloud and AI services.

cloud

Google Cloud Platform

Cloud infrastructure and data services.

devops

Docker

Containerized application delivery.

devops

Kubernetes

Orchestration at enterprise scale.

Explore related capabilities

Talk to an expert

Solutions

Industries

Technologies

Careers

Join the team

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Senior Software Engineer

Hyderabad / Remotefull timehybrid4–8 YearsPosted 10 Sept 20261 opening

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Frequently asked questions

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Quick overview

Data Engineering & Cloud. Build the data foundation behind analytics and AI

Who is this for?

Data, analytics, engineering, and technology leaders building scalable data foundations for analytics and AI.

Why choose XELARVIS?

XELARVIS designs and implements scalable data platforms that make information reliable, accessible, secure, and ready for analytics and AI.

How do we deliver?

Architecture → lakes/warehouses → pipelines → quality & governance → cloud platforms → MLOps infrastructure.

Where we apply this

Technology, Banking & Financial Services, Manufacturing, Retail & E-Commerce, Logistics & Supply Chain, Healthcare & Life Sciences

Expected outcomes

Reliable, scalable data foundations that enable analytics, AI, and enterprise decision-making.

What we help organizations do

Design modern data architectures

Build data lakes, lakehouses, and warehouses

Develop scalable ETL and ELT pipelines

Integrate batch and real-time data

Modernize data platforms in the cloud

Improve data quality and governance

Build analytics-ready data foundations

Establish MLOps and AI infrastructure

Business outcome. Reliable, scalable data foundations that enable analytics, AI, and enterprise decision-making.

How we deliver

How we move from business problem to governed delivery for this service.

Data Architecture Assessment

Evaluate systems, sources, quality, and platform requirements.

Platform & Lakehouse Design

Design data lakes, warehouses, and scalable architectures.

Pipeline & ETL/ELT Development

Build automated pipelines for reliable movement and transformation.

Cloud Data Platform Implementation

Deploy secure infrastructure for storage, analytics, and AI.

Big Data & Streaming

Process high-volume batch and real-time workloads.

Governance & Data Quality

Access controls, lineage, quality rules, and monitoring.

MLOps Infrastructure

CI/CD, model deployment foundations, and operational monitoring.

Managed Optimization

Ongoing performance, cost, and reliability improvements.

Technologies

data

Apache Spark

Large-scale data processing.

data

Apache Kafka

Real-time data streaming.

data

Snowflake

Cloud data warehouse platform.

data

Databricks

Lakehouse analytics and ML.

cloud

AWS

Secure, scalable cloud foundations.

cloud

Microsoft Azure

Enterprise cloud and AI services.

cloud

Google Cloud Platform

Cloud infrastructure and data services.

devops

Docker

Containerized application delivery.

devops

Kubernetes

Orchestration at enterprise scale.

Data Engineering & Cloud

Quick overview

What we help organizations do

Challenges we solve

Key benefits

Data Architecture

Data Lakes

Warehouses

Pipelines

ETL/ELT

Big Data

Cloud Data Platforms

Governance

Quality

MLOps Infrastructure

Capabilities

How we deliver

Data Architecture Assessment

Platform & Lakehouse Design

Pipeline & ETL/ELT Development

Cloud Data Platform Implementation

Big Data & Streaming

Governance & Data Quality

MLOps Infrastructure

Managed Optimization

Deliverables

Outcomes

Technologies

Apache Spark

Apache Kafka

Snowflake

Databricks

AWS

Microsoft Azure

Google Cloud Platform

Docker

Kubernetes

Explore related capabilities

Solutions

Industries

Technologies

Join the team

Senior Software Engineer

Frequently asked questions

What does a data platform engagement typically include?

Can you support both batch and real-time data?

How does this relate to AI and analytics?

Have a business or technology challenge?

Data Engineering & Cloud

Quick overview

What we help organizations do

Challenges we solve

Key benefits

Data Architecture

Data Lakes

Warehouses

Pipelines

ETL/ELT

Big Data

Cloud Data Platforms

Governance

Quality

MLOps Infrastructure

Capabilities

How we deliver

Data Architecture Assessment

Platform & Lakehouse Design

Pipeline & ETL/ELT Development

Cloud Data Platform Implementation

Big Data & Streaming

Governance & Data Quality

MLOps Infrastructure

Managed Optimization

Deliverables

Outcomes

Technologies

Apache Spark

Apache Kafka

Snowflake

Databricks