Once data has been ingested and stored, the next crucial step is data transformation. This process involves converting raw data into usable information by cleansing, enriching and structuring it to meet the specific needs of users and analytical systems.

Why is transformation important?

Data transformation is essential to ensure that your data is ready for analysis. The quality of the analysis depends directly on the quality of the data. If data is poorly structured, incomplete or inconsistent, results will be erroneous and unreliable. Effective transformation prepares data for BI reports, machine learning models and any other form of advanced analysis.

Data transformation objectives :

• Clean up data: Eliminate duplicates, correct errors and standardize formats to obtain clean, consistent data.
• Enrich data: Add additional information to datasets to make them more useful and exploitable.
• Structure data: Organize data so that it is ready for use by BI, data science or machine learning systems.

Transformation types

There are several approaches to data transformation, depending on your needs:

1. Batch transformation: This process consists of applying transformations to data sets at defined times. This method is ideal for periodic reports or processes that do not require immediate updating.
2. Real-time transformation: Transformations are applied as soon as the data is ingested, providing constantly updated information. This mode is crucial for use cases requiring instant analysis, such as IoT sensor data or real-time fraud detection.
3. ETL vs ELT :
   • ETL (Extraction, Transformation, Loading): Data is first transformed before being loaded into the data warehouse or data lake.
   • ELT (Extraction, Loading, Transformation): Data is loaded first, then transformed directly into the storage system, enabling faster analysis of massive volumes of data.

Data transformation solutions

Numerous tools are available for data transformation, ranging from open source solutions to proprietary platforms. Here's an overview of the solutions available on the market:

Open Source tools

• Apache Spark: Spark is one of the most powerful and popular transformation tools on the market. It enables distributed, high-performance transformations, whether in batch or real time, while supporting several languages (Python, Java, Scala). Spark is particularly well suited to environments requiring the management of large quantities of data.
• Apache Flink: An open source tool specialized in real-time stream processing. Flink is designed for transformations requiring low latency and continuous data processing.
• DBT (Data Build Tool): A tool that enables collaborative management of data transformations, with strong integration to modern data warehouses. DBT enables analysts to transform data directly in the data warehouse, with a versioning and change-tracking approach.

Proprietary tools

Proprietary solutions generally offer a more intuitive interface and advanced features for companies wishing to avoid the complexity of open source systems.

• Matillion: Matillion is an ETL/ELT platform specially designed for the cloud. It integrates easily with solutions such as Snowflake, Redshift and BigQuery, and offers advanced transformation functionalities via a no-code/low-code interface.
• Informatica PowerCenter: A robust and widely adopted data integration solution, PowerCenter offers advanced capabilities for transforming and orchestrating data pipelines. It is particularly useful for large enterprises with complex transformation processes requiring high automation and scalability.
• Alteryx: Alteryx is an intuitive tool, aimed at business users who don't necessarily have coding skills. It enables transformation and analysis workflows to be created via a visual interface, while offering integrations with cloud platforms and data warehouses.

Set-up time and complexity

The implementation of a transformation solution depends on a number of factors, including the complexity of data pipelines and the size of datasets. Here's an estimate of typical implementation steps:

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Challenges to overcome

• Data quality: Ensuring that transformed data is clean, consistent and ready for use by analytics or machine learning systems.
• Scalability: transformations need to be efficient and scalable, especially as data volumes increase.
• Automation and orchestration: Transformation processes need to be well orchestrated to ensure that data is processed in the right order and efficiently.

Why choose Cleyrop?

Cleyrop offers you a complete platform that integrates advanced data transformation functionalities. We enable you not only to transform your data efficiently, but also to manage it in a secure, collaborative environment. Thanks to our no-code/low-code approach, business users can easily participate in the transformation process, while technical experts can take advantage of advanced tools to orchestrate and automate complex workflows.

At Cleyrop, data transformation is part of a wider chain that includes ingestion, storage, governance, and even artificial intelligence, enabling you to make the most of your data without the usual technical constraints.

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