Data Transformation

For most companies, becoming data-driven may sound easy. Simply integrate all your data, analyze and visualize it and there you have it. However, the start of this journey may be the most complex part - ETL processes. This is usually the first step in the critical path of data - you have to access it from sources to be able to perform any analytics at all.

ETL consists of Extracting data from sources, Transforming it to a format you can actually use and Loading it to your database. Though extracting and loading are intuitively understood, transforming data is not very much so. It consists of changing the original structure found in the data source to a structure that you prefer or that is easier to analyze. Though this may still sound simple enough, the reality is not so.

First of all, we have to consider how critical ETL is. A failed or slow ETL means a failed or slow data adoption. Second, we have to analyze how complex it may be. People performing these tasks must have a deep understanding of IT, data, their database technology and how each data source behaves. Then, a different ETL process must be set up for each separate source. Added to the fact that each system has its own details and everyone is prone to fail, this means a big headache for many teams. Though extracting and loading data might be accomplished with success, if it’s not correctly transformed to the desired structure, these efforts will be wasted.

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SlicingDice's built-in ETL module can handle complex data transformations


SlicingDice’s data loading & sync module allows organizations to even forget this was a process. Having a built-in module with 150+ connectors to different sources. With a 1-minute setup you choose a source type and data will be extracted from there and loaded to your SlicingDice database, even updating automatically as you need. Our data transformation capabilities complement that, ensuring that no matter what structure your sources use, data will fit our near-schemaless structure when it’s loaded, ensuring you can analyze it as fast as you need.

Companies leverage these capabilities by nearly skipping the ETL phase of data warehousing - it’s so simple it seems another thing entirely. This means that they can move forward to what really delivers value faster - and won’t need to come back to ETL again. Risks are lowered, as you mitigate the chance of failure of possibly the most critical step in data analytics. This also increases the deployment speed and reduces expertise and time requirements, which improves the overall value obtained from data, meaning better ROI and less pressure on IT teams.


Check the Documentation
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SlicingDice Advantage


Few but powerful reasons to start using SlicingDice's for doing data transformations.

Transform any data on the fly.

Make transformations whenever you need, with a few clicks, with no impact on your database.

Use standard SQL commands. No complex coding required.

Our module simplifies things. Use SQL to define how you want data to be transformed. Save time and ensure efficiency.

Transformation jobs are automatically scalable.

Hundreds or billions of rows, it makes no difference. Transform data as you need without worrying about performance bottlenecks.

Fully managed.

Don’t worry about integration, maintenance, backups, sharding, partitioning or any configuration. Focus on improving your business.

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