You have undoubtedly heard about the hugely popular “Transformers” movie franchise, but have you ever heard about the Transformers of the Alumio integration platform? Spoiler alert: it has nothing to do with cool cars. Instead, transformers have everything to do with data mapping and transformation, which are both crucial processes in data integration. Ready to deep-dive into what data transformation is and how it works? Keep on reading!
What is data transformation?
Data transformation refers to the process of taking existing data in a particular format or state and converting it into a different format or state to facilitate the seamless integration between a source system and a destination system.
Data transformation can take on many shapes and be constructive (adding and replicating data), negative (deleting irrelevant data), aesthetic (standardizing incoming data and fine-tuning it to suit destination requirements), or structural (renaming or integrating columns in a database). Essentially, it is the cornerstone of data integration across organizations, and Alumio makes it easy to achieve.
How does data transformation work?
Picture the following: there are two systems: System A and System B. Certain data is retrieved from System A (source system), but System B (target system) requires it in another format to be able to interpret it. For example, perhaps the data from System A is in XML format, but System B only understands data if it is in JSON format.
In this scenario, you have to convert and map the data to the format required by System B, and you may also have to filter the source data since there might be irrelevant information within it that System B doesn’t require.
What is data mapping?
Data mapping refers to the process of connecting a data field from one source to a data field in another source. As such, it is a visual representation of data movement and transformation and is also known as the first step in the process of data integration.
Why is data mapping important?
Due to the complexity and high volume of data today, data mapping has become more crucial than ever. With data mapping, the potential for errors is reduced, and data is standardized, thus making it easier to understand and interpret. Not unlike a map, data mapping helps visualize the best way to get data from point A to point B, and just like missing an exit can thwart your travels, data mapping errors can jeopardize your data’s movement from point A to point B.
Is this all starting to sound a bit too complicated? Let’s approach data transformation from the standpoint of an analogy. In this case, we will use language translation as an analogy to better understand what data transformation is and how it works.
So, how does data transformation relate to language translation?
Data transformation is like translating a book from one language to another. Imagine you have a book written in Dutch and want to make it accessible to English readers.
In this case, the Dutch book would be the source system, also known as System A, containing the source data (Dutch language). This source data is structured and formatted in a way that the source system understands (just as a Dutch reader would), but lacks the structure and format for System B, also known as the target system, to understand it (just as an English reader wouldn’t understand the Dutch book). Thus, the answer is simple: translate the book from Dutch to English, i.e., translate the source data from System A so System B can understand.
This translation will be carried out by a translator, the entity transformer. The entity transformer defines how the data should be converted while preserving the meaning, just as a translator would.
However, to carry out any translation, translators must follow translation rules based on grammar, context, etc. that specify how to convert specific terms, phrases, or structures from one language to another. These rules would be the data mapping features, which define how data fields, attributes, and structures are converted from the source to the target format.
Just as a translator might need to add context or footnotes to clarify certain passages in the book to readers, sometimes, during data transformation, you might need to enrich the data with additional information relevant to system B, which Alumio’s tools facilitate.
Just as a translated book may need proofreading to catch errors and ensure accuracy, data transformation processes include checks to verify that the transformed data is correct and handle any errors that may arise. In this case, the proofreader would be Alumio, as the platform provides users with the necessary tools to verify and check the resulting data.
Lastly, once the book is successfully translated and validated, it can be published or made available to readers who speak the target language. In the context of data integration, the transformed data is synchronized with the target system, making it accessible and usable for its intended purpose.
How does Alumio transform data?
The Alumio integration platform as-a-service (iPaaS) transforms data by using entity transformers. Entity transformers are used to execute data actions within the integration, such as mapping, enriching, and transforming data into desired formats and filtering out unnecessary data. Entity transformers can also be used to develop caching layers that optimize integrations.
In the Alumio dashboard, entity transformers can be created and altered by going to Connections -> Entity transformers. With these transformers, data can be modified since they allow data selection/reduction, translation/mapping, encoding, calculation, sorting/ordering, and merging/joining/lookup from other sources, which allows for aggregations, the generation of surrogate keys, transposing/pivoting of array/object keys and values, as well as validation.
Interestingly, transformers also have the ability to filter out entire data points that are produced by incoming configurations, often preventing unnecessary queued items.
Learn how to map and filter data using Alumio’s Entity Transformers →
Additionally, transformers enable the combination of data flows that offer business logic that decides whether the transformer will be applied to a certain data set. The convergence of transformers and Alumio’s features allow for the storage of data and the combination of data sets, which can be compared to create, update, and delete data feeds, as well as many more functions.
Overall, transformers are magical tools that enable you to create your own custom code with endless possibilities.
Deep-dive into how to make use of all the functionalities of Alumio’s entity transformers →
What are the benefits of data transformation with Alumio?
Improved data quality: Transformation processes can help standardize and clean data, ensuring system consistency and accuracy while reducing manual efforts and the likelihood of errors.
Efficient data mapping: Alumio offers tools for easy data mapping between different formats and structures, facilitating seamless communication between disparate systems and promoting interoperability at a rapid pace.
Agility and scalability: Alumio's data transformation capabilities can contribute to increased agility in adapting to changing business requirements and emerging data formats. When adapting to new information and data formats, Alumio promotes scalability to handle growing data volumes and increase integration complexities as businesses expand.
Compliance and security: Ensuring data is transformed securely and in compliance with relevant regulations is crucial, and Alumio provides the necessary features to support these requirements.
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