Engineering foundations for scaling analytics and AI with enterprise IT

The making of well-designed AI / ML solutions requires significant data engineering and data wrangling exercises. Data engineering and scalable modern solution architectures are key requirements for an AI/ML solution for production use. We use a business-focused approach to IT in engineering the solution, aligning analytics, AI/ML approaches, and technology. Unleashing agile analytics within an enterprise where data is imprisoned in legacy platforms and infrastructure requires not just an IT transformation – but a data-first approach driven by an analytics partner. Finally, to understand how the data-to-decision making comes together requires excellent team dynamics and analyzing, designing, and building the AI/ML application.

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

Our development Process

1. Understanding business needs and technical requirements

2. Analysis of existing and future data sources

3. Building and implementing a Data Lake

4. Designing and implementing Data Pipelines

5. Automation and deployment

6. Testing

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

Amazon Web Services

Jenkins

Microsoft Azure

Snowflake

Google Cloud Platform

Airflow

Redshift

Spark

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.