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DATA ENGINEERING

Unlock Your Product's Hidden Potential with Our Data Engineering Dream Team!

Data Integration and ETL
  • Talend
  • Alteryx
  • AWS Glue
Database and Query Languages
  • Core Java
  • SQL (Oracle, MSSQL, Redshift)
  • Hive
Job Scheduling and Automation
  • Control M and TAC
Project Management and Collaboration
  • Jira
  • GitHub
  • WinSCP
Cloud Services
  • AWS Redshift
  • AWS S3
  • AWS Lambda
L1/ L2 support

End-to-End Data Solutions

Our end-to-end data solutions encompass the entire data lifecycle, from acquisition to storage, processing, and analysis. We understand that a seamless data journey is vital for extracting valuable insights. With our comprehensive approach, we ensure that your organization’s data is not just managed but optimized for strategic decision-making.

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Data Architecture Design
Data Architecture Design

This involves creating a blueprint for how data will flow, be stored, and be accessed across various systems. Data architecture defines the high-level structure, data storage patterns, and integration points, while data engineers implement and operationalize these designs.
Azure offers a wide array of architectural patterns and best practices to design scalable and resilient data architectures. Whether it's building a data lake, a real-time analytics platform, or a hybrid solution, Azure's architecture resources help organizations create robust data foundations.

Data Integration
Data Integration

This involves integrating data from different sources, which might include databases, APIs, flat files, streaming sources, and more. This involves understanding the data schema, format, and transformation requirements.

Azure’s ETL (Extract, Transform, Load) service simplifies data integration by providing a cloud-based platform to create, schedule, and manage data pipelines. It supports a wide range of data sources, enabling seamless data movement and transformation.

Azure offers a wide array of architectural patterns and best practices to design scalable and resilient data architectures. Whether it’s building a data lake, a real-time analytics platform, or a hybrid solution, Azure’s architecture resources help organizations create robust data foundations.

  • Azure Data Factory
  • Azure Logic Apps
  • Azure Data Integration
  • Azure Data Share
  • Azure Stream Analytics
Data Warehousing
Data Warehousing

This involves creating a blueprint for how data will flow, be stored, and be accessed across various systems. Data architecture defines the high-level structure, data storage patterns, and integration points, while data engineers implement and operationalize these designs.
This deals with creating and managing data warehouses or data lakes where data is stored for analysis.
Selecting appropriate storage technologies based on the nature of the data and the requirements of the business.

This involves creating a blueprint for how data will flow, be stored, and be accessed across various systems. Data architecture defines the high-level structure, data storage patterns, and integration points, while data engineers implement and operationalize these designs.
This deals with creating and managing data warehouses or data lakes where data is stored for analysis. Selecting appropriate storage technologies based on the nature of the data and the requirements of the business.

  • Azure Synapse Analytics: Azure Synapse Analytics, formerly SQL Data Warehouse, is a fully managed, enterprise-grade analytics service designed for data warehousing and large-scale analytics. It integrates seamlessly with various Azure services, offering unparalleled scalability and performance for data warehousing.
Data Modeling
Data Modeling

Developing data models that facilitate efficient storage and retrieval of data is another key responsibility. This involves designing of database schemas that align with business needs and support data analytics.
Azure offers a wide array of architectural patterns and best practices to design scalable and resilient data architectures. Whether it's building a data lake, a real-time analytics platform, or a hybrid solution, Azure's architecture resources help organizations create robust data foundations.

  • Azure SQL Database: Azure SQL Database not only provides a platform for data modeling but also supports various data modeling approaches. It allows the creation of relational database schemas that align with business needs, making it suitable for online transaction processing (OLTP) workloads. Its compatibility with SQL Server makes it familiar for data professionals.
  • Azure Analysis Services: For organizations looking to build analytical data models, Azure Analysis Services provides a robust solution. It supports tabular and multidimensional models, enabling businesses to create comprehensive analytical models that can be used for interactive reporting and data exploration.
ETL (Extract, Transform, Load)
ETL (Extract, Transform, Load)

This deals with designing of ETL processes to extract data from source systems, transform it into a suitable format, and load it into the target systems. These processes often involve data cleansing, enrichment, and aggregation.
Azure offers a wide array of architectural patterns and best practices to design scalable and resilient data architectures. Whether it's building a data lake, a real-time analytics platform, or a hybrid solution, Azure's architecture resources help organizations create robust data foundations.

  • Azure Data Factory: This robust service in Azure empowers organizations to create, schedule, and orchestrate complex ETL workflows efficiently. It supports diverse data sources and destinations, enabling the extraction, transformation, and loading of data at scale. It’s an ideal choice for building data pipelines, data movement, and data transformation tasks.
  • Azure Databricks: Azure Databricks is not only an analytics platform but also a powerful ETL tool. It simplifies big data processing and transformation by leveraging Apache Spark. Data engineers can use it to clean, enrich, and transform data from various sources, enabling advanced analytics and machine learning.
Data Migration
Data Migration

Data migration is the process of transferring data from one system or location to another while ensuring its integrity, security, and compliance.
Specialists in data migration plan and execute these transfers efficiently and accurately, minimizing disruptions.
Azure offers a wide array of architectural patterns and best practices to design scalable and resilient data architectures. Whether it's building a data lake, a real-time analytics platform, or a hybrid solution, Azure's architecture resources help organizations create robust data foundations.

  • Azure Database Migration Service: This service simplifies and automates database migration tasks. It supports various source database engines, including SQL Server, MySQL, and PostgreSQL. It not only handles data movement but also schema conversion, ensuring a smooth migration process to Azure data platforms.
  • Azure Site Recovery: While primarily known for disaster recovery, Azure Site Recovery also serves as a tool for data migration from on-premises environments to Azure. It offers replication and migration capabilities, making it suitable for scenarios where data residency, availability, and compliance are crucial considerations.
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