Engineering Foundations for Scaling Analytics and AI with Enterprise IT

Creating effective AI/ML solutions requires extensive data engineering and data wrangling. Scalable modern solution architectures and robust data engineering are essential for AI/ML solutions in production. We take a business-focused approach, aligning analytics, AI/ML strategies, and technology. To enable agile analytics within an enterprise restricted by legacy platforms and infrastructure, a data-first approach driven by an analytics partner is necessary. Understanding the process from data to decision-making involves excellent team dynamics and the analysis, design, and construction of the AI/ML application.
NOTE: seemed to be a really long paragraph on the website so I just shortened it a bit.

Our Services

Cloud engineering

  • AWS, Azure and GCP engineering for end to end applications
  • Spark/ Scala analytics workloads
  • Microservice architectures
  • IoT/stream analytics

AI Engineering and MLOps

  • Scalable architectures using DevOps and MLOps
  • Model registry and ML CI/CD pipelines on Cloud
  • AI/ML platforms for Data Science

Digital analytics and engineering

  • E-commerce
  • Web analytics
  • Digital marketing analytics
  • Ad platform technologies

Data Lakes and Data Platforms

  • Dimensional modeling
  • Data warehouse and Data marts design
  • Data and platform governance
  • Database migration to the cloud
  • ITIL/ITSM services for data platforms

Full Stack engineering, Business Intelligence and Visualization

  • Interactive dashboards
  • Automated reporting solutions
  • Complete web & mobile applications development
  • App modernization & migration to Cloud

App modernization & migration to Cloud

  • E-commerce
  • Web analytics
  • Digital marketing analytics
  • Ad platform technologies
Data Engineering & AI
  • Data Integration and ETL Pipelines
  • Building Lakehouse Architecture using Databricks
  • LLM & GPT Model integration
  • Real-time data streaming and processing
  • Location data integration
Big Data and Advanced Analytics
  • Predictive Analytics
  • Fraud or Leak-pipe analytics
  • User-Journey Analytics
  • Omni channel Next Best Action Detection
  • Anomaly detection
Cloud/Data Migration
  • Onprem to cloud data migration
  • Building Pipelines for data integration
  • DevOps and MLOps automation
Data Governance and Security
  • Data governance frameworks
  • Data Classification
  • Regulatory Compliance (PII, HIPAA,PCI, etc..)
  • Dashboard design & development
  • Data Accessibility and interactivity
  • Data storytelling
  • Mobile-Friendly visualization
  • Interactive Data Exploration
Data Quality Engineering
  • Data Validation and Verification
  • Data Classification and Profiling
  • Data Poisoning Evaluation
  • Data Testing and Validation

Our Development Process

Understanding Business Needs and Technical Requirements
Analyzing Existing and Future Data Sources
Building and Implementing a Data Lake
Designing and Implementing Data Pipelines
Automation and Deployment

Acarin is an experienced Data Engineering company. We help companies all over the world make the most of the data they process every day. Firstly, our data engineering team carries out the workshops and discovery calls with potential end-users. Then, we get all the necessary information from the technical departments. Let’s discuss a data engineering solution for your business!

At this stage, it is essential to go through current data sources to maximize the value of data. You should identify multiple data sources from which structured and unstructured data may be collected.

During this step, our experts will prioritize and assess them.

Data Lakes are the most cost-effective alternatives for storing data. A data lake is a data repository system that stores raw and processed structured and unstructured data files. A system like stores flat, source, transformed, or raw files.

Data Lakes could be established or accessed using specific tools such as Hadoop, S3, GCS, or Azure Data Lake on-premises or in the cloud.

After selecting data sources and storage, it is time to begin developing data processing jobs.

These are the most critical activities in the data pipeline because they turn data into relevant information and generate unified data models.

The next step is one of the most important parts in data development consulting – DevOps. Our team develops the right DevOps strategy to deploy and automate the data pipeline.

This strategy plays an important role as it helps to save a lot of time spent, as well as take care of the management and deployment of the pipeline.

Testing, measuring, and learning — are important at the last stage of the Data Engineering Consulting Process.

DevOps automation is vital at this moment.

Technology Stack


Azure Data Factory, Synapse, Functions

Google BigQuery, Vertex AI

AWS Glue, Kinesis, Redshift

Open AI / GPT

Apache Spark

Tableau / PowerBI


Case Study

Intrinsic value of data

Establish trusted, democratized and reusable data products that improve speed and efficiency, even in the absence of advanced AI.

Value: Reduced cost of rework, more timely insights, improved adoption through transparency and trust.

Accelerated data + AI value

Evaluate marketplace innovation and establish effective governance for AI solution integration into the broader architecture, avoiding cloud-based silos.

Value: AI-driven automation of complex processes can deliver in-year returns with pre-built solutions vs. multiple years to stand up custom builds.

Exponential value with AI

Deploy workflows that empower teams to experiment with AI more quickly, with a fast path to production.

Value: Re-invented workflows like customer interactions, new product introduction, physical and digital supply chains, asset maintenance.