Data Engineering
& Analytics

Connect to various data sources and ingest data to the system.

• Create a pipeline to handle both real-time streaming data and batch data.
• Automate data movement, data transformation, processing, and cleansing.
• Real-time analytics using Kinesis or Kafka streams.
• Search, analytics, and dashboards for real-time and batch data

Apache Kafka Migration

Implement and maintain Data Lakes or Data Mesh with structured and unstructured data.

• Set-up Data Lake Governance to manage the data effectively and efficiently.
• Implement Semantic Data Lakes to have data discovery, data management, and enterprise analytics.
• Implement Data Lake Catalog to make the data consistently accessible for consumption for any purpose, including analytics.

Visualization platform for real-time alerting, insights, and dashboards.

• Dashboards and applications that provide valuable business insights accessed from multichannel.
• Expertise in AWS products like Amazon QuickSight, to quickly create and publish interactive dashboards.
• Custom dashboards do not have user-based pricing and can be embedded into applications, portals, and websites.

Streamlining the data collation and transformation process. This is achieved through:

• Data extraction from data sources, identifying the changes, and pulling out the required data.
• Transforming the data into a consistent format, using data pipelines, while in transition or at the target source.
• Loading transformed or untransformed data into the warehouse.

Develop SQL based data marts and data warehouses, which will be your single source of truth

• Migrate data tables and structures from cloud or on-prem to cloud.
• Integrate across data sources or systems.
• Create data marts for specific functionalities and use cases.