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Enterprise Data Engineering Services & Consulting - Databricks, Snowflake, AWS & Azure

From data pipelines and cloud lakehouses on Databricks and Snowflake, to real-time streaming on Azure and AWS – LatentView builds the data infrastructure Fortune 500 enterprises depend on.

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Thrive Through Data With Next-gen Data Engineering Solutions

We help enterprises design, build, and manage scalable data infrastructure through our data engineering services, transforming fragmented data into reliable, AI-ready assets for analytics, automation, and faster business decisions.

 

Our data engineering consulting services are built for enterprise complexity. Whether you’re migrating to Databricks Lakehouse, modernizing a Snowflake data warehouse, building real-time pipelines on Azure or AWS, or engineering the data infrastructure for AI and machine learning – LatentView delivers end-to-end execution, not just advice.

 

With 20+ years of experience and a Fortune 500 client base, we unify fragmented data across cloud and on premise systems, eliminate data silos, implement Unity Catalog governance frameworks, and build the scalable pipelines that power reliable analytics and AI decisions.

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Years

Modernizing Enterprise Data Platforms
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Fortune 500 Clients

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Databricks Certified Engineers

Including 4 Partner Champions

What Enterprise Data Engineering Challenges We Solve?

Enterprise data environments are complex, distributed, and constantly evolving. Our data engineering platform address the most common challenges that prevent organizations from getting reliable, data-driven insights.

  • Fragmented data across systems and platforms – Data spread across cloud, on-prem, and legacy systems makes integration and consistency difficult.
  • Unreliable data quality and lack of trust – Inconsistent pipelines and poor validation reduce confidence in analytics outputs.
  • Scalability limitations as data volumes grow – Data platforms fail to keep up with increasing data velocity, variety, and real-time processing needs.
  • Slow analytics and delayed decision-making – Inefficient data pipelines increase latency and limit timely insights.
  • Governance, security, and compliance constraints – Enterprises must balance data accessibility with security, governance, and regulatory requirements.
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Core End to End Data Engineering Services We Deliver

Data Pipeline Development

We design and build scalable ETL and ELT pipelines across structured and unstructured sources, handling multi-terabyte data volumes on AWS, Azure, and Google Cloud using Spark, Hadoop, and cloud-native frameworks.

  • ETL/ELT pipeline development
  • Batch and real-time stream processing
  • Multi-source data ingestion
  • Pipeline orchestration and scheduling
  • Pipeline testing and validation

Data Pipeline Development

We design and build scalable ETL and ELT pipelines across structured and unstructured sources, handling multi-terabyte data volumes on AWS, Azure, and Google Cloud using Spark, Hadoop, and cloud-native frameworks.

  • ETL/ELT pipeline development
  • Batch and real-time stream processing
  • Multi-source data ingestion
  • Pipeline orchestration and scheduling
  • Pipeline testing and validation

Data Warehouse & Lakehouse Architecture

With our data engineering services, we architect and implement cloud-native data warehouses and lakehouse environments on Databricks, Snowflake, and AWS, unifying batch analytics, streaming, and ML workloads on a single governed platform.

  • Data warehouse design and implementation
  • Delta Lake lakehouse architecture
  • Batch and streaming unification
  • Schema design and data modeling
  • Query optimization and performance tuning
  • Warehouse cost management

Data Warehouse & Lakehouse Architecture

With our data engineering services, we architect and implement cloud-native data warehouses and lakehouse environments on Databricks, Snowflake, and AWS, unifying batch analytics, streaming, and ML workloads on a single governed platform.

  • Data warehouse design and implementation
  • Delta Lake lakehouse architecture
  • Batch and streaming unification
  • Schema design and data modeling
  • Query optimization and performance tuning
  • Warehouse cost management

Cloud Migration & Platform Modernization

We migrate legacy platforms including Hadoop, Teradata, and on-premise SQL environments to modern cloud-native targets using MigrateMate, our proprietary accelerator that has delivered migrations two months ahead of deadline with $1M+ in annual savings for Fortune 500 clients.

  • Legacy platform assessment and roadmap
  • Hadoop and on-premise to cloud migration
  • Snowflake to Databricks migration
  • Automated migration using MigrateMate
  • Parallel validation and phased cutover

Cloud Migration & Platform Modernization

We migrate legacy platforms including Hadoop, Teradata, and on-premise SQL environments to modern cloud-native targets using MigrateMate, our proprietary accelerator that has delivered migrations two months ahead of deadline with $1M+ in annual savings for Fortune 500 clients.

  • Legacy platform assessment and roadmap
  • Hadoop and on-premise to cloud migration
  • Snowflake to Databricks migration
  • Automated migration using MigrateMate
  • Parallel validation and phased cutover

Data Integration & Cleansing

We consolidate data from multiple disjointed structured and unstructured sources into a single governed view, with cleansing and validation built into every pipeline layer. This approach delivered $500K in annual savings for the world's largest entertainment production company by resolving revenue leaks.

  • Multi-source data integration
  • Structured and unstructured data harmonization
  • Data cleansing and standardization
  • Data quality validation
  • Data silo elimination

Data Integration & Cleansing

We consolidate data from multiple disjointed structured and unstructured sources into a single governed view, with cleansing and validation built into every pipeline layer. This approach delivered $500K in annual savings for the world's largest entertainment production company by resolving revenue leaks.

  • Multi-source data integration
  • Structured and unstructured data harmonization
  • Data cleansing and standardization
  • Data quality validation
  • Data silo elimination

Data Governance & Security

With our data engineering services, we implement Unity Catalog across Databricks environments with column-level access controls, role-based security, automated lineage tracking, and PII classification built in from day one. Quality checks are embedded into every pipeline so data integrity is validated before it reaches analytics or AI consumers.

  • Unity Catalog implementation
  • Role-based access control and column-level security
  • PII classification and masking
  • Data lineage tracking
  • Audit readiness and compliance reporting

Data Governance & Security

With our data engineering services, we implement Unity Catalog across Databricks environments with column-level access controls, role-based security, automated lineage tracking, and PII classification built in from day one. Quality checks are embedded into every pipeline so data integrity is validated before it reaches analytics or AI consumers.

  • Unity Catalog implementation
  • Role-based access control and column-level security
  • PII classification and masking
  • Data lineage tracking
  • Audit readiness and compliance reporting

AI & ML Infrastructure

We build scalable data foundations for AI, ML, and GenAI workloads at enterprise scale including real-time pipelines, governed data catalogs, and model serving infrastructure on Databricks, powering data products across B2C and B2B teams.

  • AI-ready data platform design
  • Real-time data pipelines for model serving
  • Governed data catalogs for AI consumption
  • GenAI data infrastructure on Databricks
  • Model serving and inference pipeline development

AI & ML Infrastructure

We build scalable data foundations for AI, ML, and GenAI workloads at enterprise scale including real-time pipelines, governed data catalogs, and model serving infrastructure on Databricks, powering data products across B2C and B2B teams.

  • AI-ready data platform design
  • Real-time data pipelines for model serving
  • Governed data catalogs for AI consumption
  • GenAI data infrastructure on Databricks
  • Model serving and inference pipeline development

Data Pipeline Development

We design and build scalable ETL and ELT pipelines across structured and unstructured sources, handling multi-terabyte data volumes on AWS, Azure, and Google Cloud using Spark, Hadoop, and cloud-native frameworks.

  • ETL/ELT pipeline development
  • Batch and real-time stream processing
  • Multi-source data ingestion
  • Pipeline orchestration and scheduling
  • Pipeline testing and validation

Data Warehouse & Lakehouse Architecture

With our data engineering services, we architect and implement cloud-native data warehouses and lakehouse environments on Databricks, Snowflake, and AWS, unifying batch analytics, streaming, and ML workloads on a single governed platform.

  • Data warehouse design and implementation
  • Delta Lake lakehouse architecture
  • Batch and streaming unification
  • Schema design and data modeling
  • Query optimization and performance tuning
  • Warehouse cost management

Cloud Migration & Platform Modernization

We migrate legacy platforms including Hadoop, Teradata, and on-premise SQL environments to modern cloud-native targets using MigrateMate, our proprietary accelerator that has delivered migrations two months ahead of deadline with $1M+ in annual savings for Fortune 500 clients.

  • Legacy platform assessment and roadmap
  • Hadoop and on-premise to cloud migration
  • Snowflake to Databricks migration
  • Automated migration using MigrateMate
  • Parallel validation and phased cutover

Data Integration & Cleansing

We consolidate data from multiple disjointed structured and unstructured sources into a single governed view, with cleansing and validation built into every pipeline layer. This approach delivered $500K in annual savings for the world’s largest entertainment production company by resolving revenue leaks.

  • Multi-source data integration
  • Structured and unstructured data harmonization
  • Data cleansing and standardization
  • Data quality validation
  • Data silo elimination

Data Governance & Security

With our data engineering services, we implement Unity Catalog across Databricks environments with column-level access controls, role-based security, automated lineage tracking, and PII classification built in from day one. Quality checks are embedded into every pipeline so data integrity is validated before it reaches analytics or AI consumers.

  • Unity Catalog implementation
  • Role-based access control and column-level security
  • PII classification and masking
  • Data lineage tracking
  • Audit readiness and compliance reporting

AI & ML Infrastructure

We build scalable data foundations for AI, ML, and GenAI workloads at enterprise scale including real-time pipelines, governed data catalogs, and model serving infrastructure on Databricks, powering data products across B2C and B2B teams.

  • AI-ready data platform design
  • Real-time data pipelines for model serving
  • Governed data catalogs for AI consumption
  • GenAI data infrastructure on Databricks
  • Model serving and inference pipeline development
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Our Best Data Engineering Services
and Capabilities

Data Migration

Migrate data faster, better, and cost-effectively

  • Migrate data seamlessly from legacy systems to modern platforms: Bridge the gap between outdated infrastructure and cutting-edge technology, future-proofing your data management.
  • Ensure data integrity and security throughout the process: Implement robust migration strategies that protect your valuable information from errors and unauthorized access.
  • Minimize downtime and disruption: Orchestrate smooth data migrations with minimal impact on your daily operations, ensuring business continuity.

Data Activation

Accurate Data. Quick intelligence. Timely action. Better outcomes.

  • Data Activation Break down data silos and democratize access: Make your data readily available and easy to use for everyone in your organization, fostering data-driven decision-making at all levels.
  • Develop self-service analytics platforms: Empower users to explore and analyze data independently without relying on technical expertise.
  • Integrate data seamlessly across applications: Eliminate data silos and create a unified data landscape, ensuring consistent and reliable information across your organization.

Data Platform Modernization

Redefine efficiency and streamline data movement

  • Modernize data platforms: Scale horizontally and vertically to handle growing data volumes and evolving business requirements. Ensure that your architecture allows seamless scalability by leveraging cloud-based solutions, containerization, or other scalable infrastructure technologies.
  • Extract data from diverse sources: Connect to an array of data sources, both internal and external, to gather the information you need to make informed decisions. Cleanse, validate, and transform your data to align with your specific needs and analytics requirements.
  • Load data into your target systems: Deliver ready-to-analyze data to your data warehouse, data lake, or other analytics platforms, ensuring timely and efficient access for users. Prioritize security measures to safeguard sensitive data. Implement encryption, authentication, and authorization mechanisms throughout the data lifecycle.

Databricks & Snowflake Engineering

Build enterprise lakehouses and cloud data warehouses on the platforms Fortune 500 data teams run on

  • Databricks Lakehouse architecture: design, build, and optimize end-to-end on the Databricks platform
  • Unity Catalog implementation: enterprise data governance, access control, and lineage tracking on Databricks
  • Snowflake data warehouse design, performance tuning, and cost optimization
  • dbt transformation pipelines on both Databricks and Snowflake
  • Legacy warehouse migration: Teradata, Netezza, Oracle, Redshift to Databricks or Snowflake
  • Databricks vs Snowflake advisory: objective platform selection based on your workload, team, and cost profile

Cloud Data Engineering - Azure, AWS & Google Cloud

Multi-cloud data engineering built for enterprise scale, security, and compliance

  • Microsoft Azure – Azure Synapse Analytics architecture and implementation, Microsoft Fabric data engineering (Lakehouse, Pipelines, Data Warehouse), Azure Data Factory pipeline development, Azure Databricks deployment and optimization
  • Amazon Web Services (AWS) – AWS Redshift design, migration, and performance tuning, AWS Glue ETL pipeline development, Amazon EMR and Kinesis for big data and real-time processing
  • Google Cloud Platform – BigQuery architecture and optimization, Google Cloud Dataflow and Pub/Sub pipeline engineering
  • Orchestration & Transformation – Apache Airflow pipeline orchestration, Apache Kafka and Spark for real-time streaming, dbt for SQL-based transformation on any cloud platform

AI & Generative AI Data Engineering

Build the data infrastructure that makes enterprise AI and GenAI possible. Reliable AI starts with reliable data

  • Feature store design and ML pipeline engineering for production AI
  • RAG (Retrieval-Augmented Generation) pipeline architecture – vector databases, embedding pipelines, and LLM data connectors
  • Real-time inference data pipelines for low-latency AI applications
  • Data quality and validation pipelines for AI training datasets
  • AI-ready lakehouse architecture on Databricks – Delta Lake, MLflow, and Unity Catalog
  • LLM data preparation – chunking strategies, metadata tagging, and retrieval optimization
  • Generative AI data engineering on Azure OpenAI and AWS Bedrock

Data Migration

Migrate data faster, better, and cost-effectively

  • Migrate data seamlessly from legacy systems to modern platforms: Bridge the gap between outdated infrastructure and cutting-edge technology, future-proofing your data management.
  • Ensure data integrity and security throughout the process: Implement robust migration strategies that protect your valuable information from errors and unauthorized access.
  • Minimize downtime and disruption: Orchestrate smooth data migrations with minimal impact on your daily operations, ensuring business continuity.
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Data Activation

Accurate Data. Quick intelligence. Timely action. Better outcomes.

  • Data Activation Break down data silos and democratize access: Make your data readily available and easy to use for everyone in your organization, fostering data-driven decision-making at all levels.
  • Develop self-service analytics platforms: Empower users to explore and analyze data independently without relying on technical expertise.
  • Integrate data seamlessly across applications: Eliminate data silos and create a unified data landscape, ensuring consistent and reliable information across your organization.
Know More

Data Platform Modernization

Redefine efficiency and streamline data movement

  • Modernize data platforms: Scale horizontally and vertically to handle growing data volumes and evolving business requirements. Ensure that your architecture allows seamless scalability by leveraging cloud-based solutions, containerization, or other scalable infrastructure technologies.
  • Extract data from diverse sources: Connect to an array of data sources, both internal and external, to gather the information you need to make informed decisions. Cleanse, validate, and transform your data to align with your specific needs and analytics requirements.
  • Load data into your target systems: Deliver ready-to-analyze data to your data warehouse, data lake, or other analytics platforms, ensuring timely and efficient access for users. Prioritize security measures to safeguard sensitive data. Implement encryption, authentication, and authorization mechanisms throughout the data lifecycle.
Know More

Databricks & Snowflake Engineering

Build enterprise lakehouses and cloud data warehouses on the platforms Fortune 500 data teams run on

  • Databricks Lakehouse architecture: design, build, and optimize end-to-end on the Databricks platform
  • Unity Catalog implementation: enterprise data governance, access control, and lineage tracking on Databricks
  • Snowflake data warehouse design, performance tuning, and cost optimization
  • dbt transformation pipelines on both Databricks and Snowflake
  • Legacy warehouse migration: Teradata, Netezza, Oracle, Redshift to Databricks or Snowflake
  • Databricks vs Snowflake advisory: objective platform selection based on your workload, team, and cost profile
Know More

Cloud Data Engineering - Azure, AWS & Google Cloud

Multi-cloud data engineering built for enterprise scale, security, and compliance

  • Microsoft Azure – Azure Synapse Analytics architecture and implementation, Microsoft Fabric data engineering (Lakehouse, Pipelines, Data Warehouse), Azure Data Factory pipeline development, Azure Databricks deployment and optimization
  • Amazon Web Services (AWS) – AWS Redshift design, migration, and performance tuning, AWS Glue ETL pipeline development, Amazon EMR and Kinesis for big data and real-time processing
  • Google Cloud Platform – BigQuery architecture and optimization, Google Cloud Dataflow and Pub/Sub pipeline engineering
  • Orchestration & Transformation – Apache Airflow pipeline orchestration, Apache Kafka and Spark for real-time streaming, dbt for SQL-based transformation on any cloud platform
Know More

AI & Generative AI Data Engineering

Build the data infrastructure that makes enterprise AI and GenAI possible. Reliable AI starts with reliable data

  • Feature store design and ML pipeline engineering for production AI
  • RAG (Retrieval-Augmented Generation) pipeline architecture – vector databases, embedding pipelines, and LLM data connectors
  • Real-time inference data pipelines for low-latency AI applications
  • Data quality and validation pipelines for AI training datasets
  • AI-ready lakehouse architecture on Databricks – Delta Lake, MLflow, and Unity Catalog
  • LLM data preparation – chunking strategies, metadata tagging, and retrieval optimization
  • Generative AI data engineering on Azure OpenAI and AWS Bedrock
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Swift, seamless, and secure cloud migration

MigrateMate
Our Customized In-house Data Migration Solution

MigrateMate is a platform-agnostic and automated data migration solution, designed to simplify and streamline moving your valuable data from on-premise platforms to the cloud.
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Enterprise Data Engineering Case Studies

Fast-Tracking Cloud Migration to Unlock $1M+ in Annual Savings for a US-based Shopping Channel

Fast-Tracking Cloud Migration to Unlock $1M+ in Annual Savings for a US-based Shopping Channel

Fast-Tracking Cloud Migration to Unlock $1M+ in Annual Savings for a US-based Shopping Channel
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Transforming Enterprise Analytics_ Migrating from Snowflake to Databricks for a Leading Beverage Technology Provider

Migrating from Snowflake to Databricks for a Leading Beverage Technology Provider

Transforming Enterprise Analytics: Migrating from Snowflake to Databricks for a Leading Beverage Technology Provider
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Modernizing Enterprise Analytics on Databricks for a Global Leader in Digital Media

Modernizing Enterprise Analytics on Databricks for a Global Leader in Digital Media

Modernizing Enterprise Analytics on Databricks for a Global Leader in Digital Media
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Enhancing Sales Strategy with MARKEE on Databricks

Enhancing Sales Strategy with MARKEE on Databricks

Enhancing Sales Strategy with MARKEE on Databricks
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VIEW ALL CASE STUDIES

Data Engineering Solutions by Industry

Every industry has unique data complexity. Our data engineering services are tailored to your sector’s regulatory, operational, and analytical requirements – built on 20+ years of enterprise experience across Fortune 500 clients.

Technology

Modernize data platforms to support product analytics, user telemetry, and AI/ML workloads at scale across cloud-native environments. Enable real-time experimentation frameworks, scalable feature stores, and event streaming pipelines for machine learning.

Financial Services

Re-architect data infrastructure to meet regulatory requirements including SOC 2, PCI-DSS, and SEC data retention mandates. Enable real-time risk analytics, fraud detection, and compliance reporting with governed, audit-ready data foundations.

CPG

Consolidate fragmented data from supply chain, retail syndicated sources, and consumer touchpoints into unified platforms. Enable demand forecasting, trade promotion optimization, and revenue growth management with AI-ready data architecture

Retail

Build real-time POS data pipelines, omnichannel customer data unification, and inventory analytics infrastructure. Enable personalized marketing, dynamic pricing engines, and demand sensing with clean, governed retail data at scale.

Manufacturing

Architect IoT sensor data pipelines, supply chain data integration, and predictive maintenance infrastructure. Enable real-time production monitoring, quality analytics, and digital twin data foundations across global operations.

Technology

Modernize data platforms to support product analytics, user telemetry, and AI/ML workloads at scale across cloud-native environments. Enable real-time experimentation frameworks, scalable feature stores, and event streaming pipelines for machine learning.
Modernize data platforms to support product analytics, user telemetry, and AI/ML workloads at scale across cloud-native environments. Enable real-time experimentation frameworks, scalable feature stores, and event streaming pipelines for machine learning.
Technology

Financial Services

Re-architect data infrastructure to meet regulatory requirements including SOC 2, PCI-DSS, and SEC data retention mandates. Enable real-time risk analytics, fraud detection, and compliance reporting with governed, audit-ready data foundations.
Re-architect data infrastructure to meet regulatory requirements including SOC 2, PCI-DSS, and SEC data retention mandates. Enable real-time risk analytics, fraud detection, and compliance reporting with governed, audit-ready data foundations.
Financial Services

CPG

Consolidate fragmented data from supply chain, retail syndicated sources, and consumer touchpoints into unified platforms. Enable demand forecasting, trade promotion optimization, and revenue growth management with AI-ready data architecture
Consolidate fragmented data from supply chain, retail syndicated sources, and consumer touchpoints into unified platforms. Enable demand forecasting, trade promotion optimization, and revenue growth management with AI-ready data architecture
CPG

Retail

Build real-time POS data pipelines, omnichannel customer data unification, and inventory analytics infrastructure. Enable personalized marketing, dynamic pricing engines, and demand sensing with clean, governed retail data at scale.
Build real-time POS data pipelines, omnichannel customer data unification, and inventory analytics infrastructure. Enable personalized marketing, dynamic pricing engines, and demand sensing with clean, governed retail data at scale.
Retail

Manufacturing

Architect IoT sensor data pipelines, supply chain data integration, and predictive maintenance infrastructure. Enable real-time production monitoring, quality analytics, and digital twin data foundations across global operations.
Architect IoT sensor data pipelines, supply chain data integration, and predictive maintenance infrastructure. Enable real-time production monitoring, quality analytics, and digital twin data foundations across global operations.
Industrials
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Awards & Recognitions

We are a leader in innovation, excellence, and work culture.
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Success Stories

Achieving Optimal Data Consumption Through Cloud Migration

An American Fortune 500 consumer retail company wanted to optimize its data operations by retiring its on-premise data management setup to a cloud-based data platform. Watch how LatentView Analytics’ effective cloud migration solution resulted in optimal data consumption.
0 %
Cost savings with the proposed POC
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Reducing throughput assay time by 85% with AI-powered computer vision

A Fortune 500 conglomerate wanted to utilize Computer Vision to assist in drug discovery. LatentView Analytics developed a powerful model using a combination of hand-crafted and deep-learning-based features from convolutional neural networks. The model quantifies the mechanism of action of drugs by capturing several morphological changes due to active drug samples.
0 %
Reduction in the throughput assay time
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Be “One of a Kind” with Smart
Innovation 3

Smart Innovation, an AI/ML-powered engine, dramatically improves the success rate of innovation by a remarkable five times. It achieves this by analyzing data points derived from consumer reviews, social media conversations, and product-related data. This analysis identifies potential opportunities within emerging trends and prescribes precise product attributes that align with them. By enhancing the success rate of innovation, Smart Innovation identifies trends ahead of the curve and significantly reduces time to market
READ MORE

Be “One of a Kind” with Smart
Innovation 4

Smart Innovation, an AI/ML-powered engine, dramatically improves the success rate of innovation by a remarkable five times. It achieves this by analyzing data points derived from consumer reviews, social media conversations, and product-related data. This analysis identifies potential opportunities within emerging trends and prescribes precise product attributes that align with them. By enhancing the success rate of innovation, Smart Innovation identifies trends ahead of the curve and significantly reduces time to market
READ MORE

Platforms and Tools We Use for Enterprise Data Engineering

As a data engineering consulting company, LatentView enables secure, large-scale data infrastructure using leading cloud platforms, modern data warehouses, and enterprise orchestration tools.

Data Analytics - Azure
Data Analytics - aws
Data Analytics - Databricks
Data Analytics consulting - neo4j
Data Analytics - five-tran
Data Analytics - snowflake
Data Analytics consulting - power-bi
Data Analytics - Google

Put Your Data On The Pedestal

We help you take control of your data and build reliable systems to empower your data teams and drive better business outcomes.

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FAQs

01What are data engineering services?

Data engineering services help enterprises build scalable data pipelines, cloud platforms, and governed data infrastructure on Databricks, Snowflake, and Azure that powers analytics and AI decisions.

We build scalable data pipelines, modernize cloud platforms on Databricks, Snowflake, and Azure, and deliver end-to-end data engineering – from strategy and architecture to deployment and optimization.

We integrate data from on-premises, cloud, SaaS, APIs, and legacy systems using dbt, Apache Spark, Azure Data Factory, and AWS Glue – unifying fragmented data into one governed platform.
We architect on Databricks Lakehouse and Snowflake with auto-scaling compute and storage, plus distributed processing with Apache Spark and Kafka for real-time workloads at any scale.

Scalability, performance, governance, cost-efficiency, and flexibility. LatentView evaluates Databricks, Snowflake, and Microsoft Fabric to find the best fit for your workload and budget.

We implement encryption, role-based access controls, data masking, audit logging, and automated compliance monitoring. On Databricks, we deploy Unity Catalog for centralized governance.

Data pipeline development, cloud platform architecture (Databricks, Snowflake, Azure, AWS, GCP), ETL/ELT, data migration, data quality, governance, real-time streaming, and AI data infrastructure.

To enable reliable analytics and AI, reduce data processing costs, and make faster decisions. Without solid data infrastructure, analytics and AI models produce unreliable results.

Databricks, Snowflake, Microsoft Fabric, Azure Synapse, AWS Redshift, BigQuery, Apache Kafka, Spark, Airflow, and dbt. Platform selection is based on your workload and cost requirements.

We combine deep analytics and AI expertise with hands-on data engineering delivery. 20+ years serving Fortune 500 clients – strategic consulting depth plus specialist engineering execution.

Both. From data strategy and platform selection (Databricks vs Snowflake vs Azure) through architecture, pipeline development, deployment, and managed services — we scale to match your needs.

Assessments take 2–4 weeks. Pipeline or migration projects take 8–16 weeks. Enterprise-wide programs run 6–12 months. We provide a detailed timeline during the initial assessment phase.

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Latent View Analytics Limited
Registered Office: 5th Floor,
Neville Tower, Unit 6, 7 and 8,
Ramanujan IT City, Rajiv Gandhi Salai,
Taramani, Chennai – 600113, Tamil Nadu, India
Tel: +91 044 4344 1700
CIN: L72300TN2006PLC058481
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