Data Engineering

Enter a whole new world of data insights with our information engineers who
help you create big data ETL pipelines focused on scaling, security, and

Data engineering involves developing and building systems for aggregating, storing, and investigating data at scale. It finds applications across industries and sectors where organizations collect large amounts of data. They often need to employ the right people and technology to ensure that is data is in a usable state by the time it makes its way downstream to data scientists and analysts.

Our team of information engineers and supporting functions have worked with various multinational companies to set up and implement the right systems, solutions and pipelines that help them gather and collate data for onward processing.

Data Integration

Unify data from heterogenous sources like never before

At MAG, we develop and implement various solutions that enable your business to combine data residing in different source systems and give you an aggregated view of them. When done correctly, data integration has the potential to reduce IT costs, improve data quality, free-up resources, and foster innovation without making sweeping changes to existing data structures or applications.

Among the many other benefits that can lend a competitive advantage to businesses are better data quality through automated data transformations that apply business rules to data, more valuable insights through a holistic view of data that is easier to analyse and improved operational efficiency by reducing the need to manually transform and combine data sets.

Data Lake

Building reservoirs to quench your thirst for rich insights

Any centralized repository that allows you to store structured and unstructured data at any scale can be characterized as a data lake. It enables you to store data as-is, without first having to structure it. Apart from raw data, it can also store transformed data – from dashboards and visualizations to big data processing, real-time analytics, and machine learning (ML) data.

Firms successfully generating business value from their data, outperform their peers. According to a survey, organizations employing data lakes see a 9% higher growth in organic revenue. It enables establishments to perform new types of analytics like ML over new sources like log files, data from click streams, social media, and internet connected devices stored in the data lake.

These insights can help you identify, and act upon opportunities for business growth quicker by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.

Data Warehousing

Augment your Data Lake with faster queries

A data warehouse is a database optimized to analyse relational data coming from transactional systems and line of business applications. The data structure, and schema are predefined to optimize for fast SQL queries, where the results are typically used for operational reporting and analysis. Data is cleaned, enriched, and transformed so it can act as the single source of truth that users can trust.

At MAG, we create solutions that make firms see the benefits of data lakes, helping them evolve their warehouse to include them, and enable diverse query capabilities, data science use-cases, and advanced capabilities for discovering new information models. Gartner names this evolution the Data Management Solution for Analytics.

Data Virtualization

Forget about the where and how

Among the many approaches to data management, data virtualization is one that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located. It provides a single customer view of the overall data.

The data remains in place, unlike the traditional ETL process, and real-time access is given to the source system for it. This can mitigate the risk of data errors, of the workload moving data around that may never be used, and it does not attempt to impose a single data model on the data.

At MAG, we build technology solutions that support the writing of transaction data updates back to the source systems and help resolve differences in source and consumer formats and semantics using various abstraction and transformation techniques.


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