What is Data Engineering
Data Engineering is a data-driven approach to scale the decision-making process and enhance machine learning capabilities. The build system is utilised by data scientists, data analysts, and other specialists to collect, transform, and deliver data.
Data Ingestion from internal and external sources in real-time or in batches.
Organise and secure data for governance and compliance.
Monitor and evaluate data quality and performance.
Data Cleaning to enhance its value and usability.
Data Enrichment by matching, cleaning, and augmenting the data.
Customer Data Model to leverage database features, and revamp data quality.
Workflow Management for scheduling, audit, performance, and data quality.
Analyze data through analytics, artificial learning, and machine learning.
Serving data to applications, processes, or people aligned with specific needs.
Govern the data catalogs, dictionaries, and mapping.
Our Service Offerings
Data Collection & Integration
Assembling relevant data from various sources, including internal databases, external APIs, social media platforms, and market research reports. Cleaning, transforming, and integrating data to make it available on a cloud system, data scientists, and business analysts.
Data Storage & ETL/ELT
Extracting, transforming, and loading data into various data storage, including relational databases, NoSQL databases, cloud storage, and big data systems, depending upon the specific need of a business.
Data Modernisation and Migration
Efficiently transform and migrate data from legacy systems to cloud storage systems or the latest target system, improving data quality and enhancing efficiency and decision-making at a cost-effective rate.
Building standard and independent data workflow pipelines to transfer, transform and store data. Big Data, cloud orchestration, and data management pipelines tools and techniques like DF, Databricks, Synapse, Informatica, and others, to process data in batch and real-time.
Who Needs Data Engineering?
IT and data management teams to prepare data for analytics, impeccable experience, and ML users.
Businesses make impactful data-driven decisions to enhance marketing, product development, and customer service.
Product owners monetise valuable data to increase revenue through analytics and machine learning.
Data scientists for access to all forms of data and help in making informed decisions.
Data governance and security teams to manage and protect data and ensure data quality and reliability.