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In a world where data is considered to be the ‘new oil’, managing massive amount of unstructuredand raw data can become challenging. Data engineering is all about building the pipeline of the data in a way that it can be used to its full potential by the stakeholders not limited to data scientist,& business analytics. To build this reliable data pipeline, numerous actions are performed on the data right from aggregation, cleaning, wrangling to designing data model/ architecture for its storage, scalability,and security. Further, the field of data engineering has become quite specialised and advanced with numerous technologies, frameworks, and multiple deployment models (cloud/ on-premise/ hybrid)at the disposal.
We offer multitude business solutions in data engineering to transform raw data into valuable analyticsand insights.Based on the nature of the data and the end user requirements, the data warehouse, data lakes and data marts are designed and managed.
This includes feeding data from different raw data sources (ERP, CRM, IOT Sensors, API’s) into preferred destinations – which could be on-premises database or cloud based data and in some cases data lake or data mart. Though ETL Model, structuring data transformations and performing data cleansing is also performed.
In alignment with the business objective, data warehouses are designed for faster data retrieval and analysis of complex data. The cloud bases data warehouse are preferred by the enterprises owning to its flexibility, performance, and scalability.
From analytics perspective, organisation derives more values by building data lakes. – in which entire unrefined Data is ingested from multiple sources in batches or real time with no pre-defined schema at lower cost.Any and all data types can be collected and retained indefinitely in a data lake. With theflexibility offered by data lakes, the users are enabled with the power to use different skills, toolsand languages to accomplish various analytical tasks all at once
Entity, attributes, and relationships in the data setsare extracted to buildconceptual, logical, & physical data models as per the business needs.While designing the data model, it is imperative to ensure entity and referential integrity and quick convenient retrieval of the information for users.
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