Bringing Automated Approach to Big Data Mapping

Meeting Big data challenge

Today the volume of data being generated is exploding. Aside from large chunks of legacy data, there is also data coming from new sources. As a result businesses are coming under increased pressure to find out ways to leverage both structured and unstructured data to increase ROI and boost bottom line. Enterprises are looking to cash in on the big data success and they are adopting emerging solutions to meet big data challenge. But the challenge is to integrate the data coming from new sources, which is fast evolving and the legacy tools are proving insufficient to process large chunks of unstructured data. It is a fact that the conventional tools were basically designed around the structure data, as a result they are unable to handle huge volumes of unstructured data. There is a growing need for tools in this space that can scale up or down depending on the requirement.

Role of ETL tools

ETL tools are vital in the light of growing importance of big data in business. In the enterprise data warehousing area, organizations are relying on ETL (extract, transform and load) tools to integrate data from disparate sources and transforming data into actionable insights to accelerate decision making. The use of ETL tools for data migration is shown to boost productivity, simplify metadata management and address ETL migration challenges. Before the data is made available to the data warehousing and BI applications, it requires a set of processes that needs to be completed.

Pre-ETL source to data mappings phase as it is popularly known, is predominantly manual work as a result requires frequent manual intervention which makes it prone to errors. Some of the fairly common issues facing the organizations include identifying mapping processes each time a new project commences, lack of traceability and auditability to name a few.

Enterprise data mapping tools for big data can go a long way

There are a number of ETL tools out there in the market but the fact remains that there are very few in pre-ETL data mapping area of data integration. There is a reason why a number of organizations are embracing enterprise data mapping tools. Enterprise data mapping tools are purpose built to automate manual processes in metadata management. No wonder enterprise data mapping tools perform better than generic data mapping software applications.

Purpose built data mapping vs generic mapping tools

Purpose built data mapping offers standardization, collaboration, repeatability, auditability something generic tools lack. Moreover these tools also complement major integration tools, ETL suite and standardize source to target mapping processes leveraging Pre etl automation tools.

Big data Pre ETL tools

Big volumes of data is being generated from multiple sources continuously and there is a need to process it to a similar extent at the same time ensure data integrity and data quality. The data mapping automation tools for big data can help enterprises to capitalize on the benefits of big data.  Mapping tools are essential in that they help overcome the big data challenges and gain operational efficiencies.

Conclusion

Given the fact that the traditional tools were designed to process structure data and understanding the growing role of big data in decision making, there is a need to adopt data integration tools for big data mapping, that way it can help transform big data into actionable information.