What is Flexter?

This section gives an introduction to Flexter and its benefits.

Flexter is a data warehouse automation solution for complex industry data standards, XML, and JSON. Flexter liberates data from complex file formats such as XML or JSON and makes it available for downstream consumers, e.g. data analysts, ETL engineers, report developers etc..

Benefits of Flexter

  1. Cost - The cost of transforming proprietary file formats is drastically reduced.
  2. Performance - Flexter helps organisations to meet their Service Level Agreements (SLAs). Flexter is extremely fast when processing data as it uses advanced algorithms and in-memory processing.
  3. Scale (Big Data Ready) – Flexter can handle any amount and volume of data. It is built on top of a distributed compute model and scales linearly. This is an important benefit in the era of exponential data growth.
  4. Risk – The data in proprietary file formats is typically embedded in a complex structure and requires specialist knowledge and niche skills for processing. It may prove impossible for data analysts or developers to develop a custom solution to the problem. The risk of failure increases exponentially with the complexity of the XML/JSON files.
  5. Meet Deadlines – By using Flexter companies can instantly meet project timelines without spending hundreds of man days on figuring out the best way of processing the data. Data becomes available instantly and analysts and developers can focus on delivering real value.
  6. Existing tools are not fit for purpose for processing large volumes of complex XML/JSON files:

Processing XML/JSON files with these tools is a very manual, risky, and labour intensive development process. The risk of failure and the number of developer man days increases exponentially with the complexity of the XML/JSON files.

Existing tools don’t scale. They rely on a single server architecture and don’t scale out to multiple servers. This limits the amount of data volume that can be processed.

Processing XML/JSON files is very demanding on hardware resources. Existing tools offer sequential approach and loop over, again and again, element by element, file by file to extract all the data. This is highly inefficient.