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The ultimate guide to data transformation

Data is a valuable asset utilized by organizations to derive actionable insights, support informed decision-making, and develop far-reaching strategies. However, before businesses can harness the ever-expanding data streams, they must navigate the complex process of data transformation.
16 min read
what is data transformation
what is data transformation

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    Types Purpose Benefits Examples
    Enterprise-level tools Cater to businesses, lacking resources or time to build in-house, proprietary solutions. High levels of scalability, ability to manage ever-growing datasets, streamlined workflows. IBM InfoSphere DataStage, SAP Data Services, the commercial version of Talend.
    Open-source tools Suitable for companies whose data engineers are proficient in building, managing, and maintaining data pipeline processes. Minimize license-related costs. Talend Open Studio, Apache NiFi, CloverDX.
    Cloud-based tools Used to accommodate continuously growing volumes of data and scale the transformation process based on exact needs. Unparalleled levels of scalability, elasticity, and low latency. AWS Glue, Azure Data Factory, Data Build Tool (dbt).
    Custom tools A good option for companies that prioritize building bespoke instruments. Align with unique requirements, integrate with existing systems and infrastructures. Require knowledge of Java, Python, R, SQL, and more.

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    Conclusion

    F.A.Q about data transformation

    • Data transformation is the process of converting raw data from one format, structure, or representation into a clean, validated, and usable form.

    • Depending on their business demands, organizations can conduct constructive, destructive, aesthetic, and structural data transformation.

    • The key steps involved in the data transformation process occur within ETL and ELT workflows, encompassing discovery, mapping, execution, actual modification with code generation and execution, and review.

    • Essential data transformation techniques used by data engineers include but are not limited to aggregation, smoothing, feature engineering, generalization, discretization, and normalization.

    • Based on their goals, companies can use enterprise-level, open-source, cloud-based, and custom data transformation instruments.

    • The benefits of converting data into a usable format include improved data quality; enhanced compatibility; efficient data organization, management, and processing; and refined customer experiences.

    • Costly implementation, security concerns, and performance constraints are the key challenges surrounding data transformation.

    • Numerous industries, including healthcare, e-commerce, manufacturing, BFSI, and education, can capitalize on data transformation.

    • Real-life examples of data transformation include improving patient care, developing personalized marketing strategies, optimizing manufacturing processes, identifying fraudulent activities, and guiding education research.

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