With a view to getting a competitive advantage, many businesses are investing heavily in Big Data (BD) initiatives. However, distilling the initiatives and getting the true value out of it is a difficult challenge. The biggest difficulty in this process is extracting information from multiple sources, information transformation and information loading in a warehouse. This whole process is called as Extract, Transform and Load (ETL). Businesses can use ETL data mapping to solve this problem and make the most out of their mission critical initiatives. However, before referring any integration software, businesses must ensure that it packs controls to deal with different Big Data integration issues.
Big Data and the Associated ETL Issues
Huge amounts of Poly-structured information traversing through and around organizations, i.e., video, logs, and transactional records is organizational Big Data. There are immense benefits of using this pool. Experts believe that businesses using analytics position themselves better in the market. As nearly 80% of any BD project consists of data integration and the remaining job is data analysis.
The cost required to manage such large volumes of diverse data on enterprise applications and open source software is huge. Organizations with traditional warehouses (EDW) using poor ETL are many times incapable of retrieving information from multiple sources. A traditional process fails to solve the purpose as it involves humongous amount of coding, time and cost.
Combination of Controls Required in a Data Mapping Software
Distributed Software Platform: A distributed software platform helps developers in effectively storing and processing the data. Such a model should run on industry-standard servers with direct-attached storage. This model can help users in storing petabytes of data, and scaling the performance by including cost-effective cluster nodes. The distributed framework should help developers in troubleshooting data-parallel problems where the obtained data can be fragmented into small chunks and processed independently.
Distributed Data Ecosystem: This feature helps in scalability and fault tolerance by storing huge files after dividing them into blocks and replicating them on different servers. Such an ecosystem should also offer APIs to read and write data parallelly.
Components for Data Management: It is quintessential for integration software to pack components for aggregating, and transferring large amounts of data from multiple sources into a centralized place. Such an information virtualization feature can transform raw information into a valuable asset.
Data Transferring Components: The ETL data mapping tools should have components for transferring data between different databases. Such components should automate complex processing operations like importing data from MySQL, Oracle database or exporting it back to RDBMS, etc.