How does An Enterprise Begin It’s Big Data Journey?

 

With the amount of data doubling in size each year, any organization’s biggest challenge is to manage, store, ingest, analyze, transform, and process massive data sets. Initiating a successful big data journey might appear tough, considering the increasing number of new data sources, improved processing capacity, and demand for fresher data. Organizations need to overcome all these challenges to reach maximum operational efficiency and drive business growth.

In recent times, many organizations have started investing in the development of enterprise data warehouses (EDW) that serves as the central data system for reporting ETL offload processes. It ingests data from many different databases and other sources both inside and outside the enterprise. Since there is a constant increase in the velocity, volume, and variety of data, already clumsy and expensive EDWs are getting overloaded with data. In addition to this, traditional ETL tools are incapable of handling all the generated data that leads to the creation of bottlenecks in the enterprise data warehouse, resulting in major processing hurdles.

Due to this overload, organizations are turning to open source tools such as Hadoop as cost-effective solutions to offload data warehouse processing functions.  Despite Hadoop’s ability to help organizations lower costs improve efficiency, most businesses aren’t able to take its advantage because they lack the skill sets required for deploying Hadoop.

Where to Begin?

Organizations handling overburdened EDWs are on the lookout for solutions that can help them offload the ETL processing from the data warehouse to an alternate environment that can manage data sets. The most common question that organizations face is how can that be done in a simple, cost-effective manner without requiring any special skill sets?

Let’s begin with Hadoop.

As mentioned earlier, many organizations implement Hadoop for offloading their data warehouse processing functions. Hadoop, after all, is a highly scalable, cost-effective platform that helps store large volumes of structured, semi-structured, and unstructured data sets. Hadoop also assists in accelerating the ETL migration process that significantly reduces costs compared to running ETL functions in a traditional data warehouse. Although the benefits of Hadoop are alluring, the complexity of the platform makes it difficult to be adopted by many organizations.

The Path Forward

Offloading huge data sets from an EDW appears like a big hurdle to organizations seeking more effective ways to manage their ever-increasing data sets. Luckily, businesses can utilize the ETL offload opportunities using the ideal software and hardware needed to shift expensive workloads and the associated data from an overloaded enterprise data warehouse to Hadoop.

With the help of right tools from organizations such as Impetus, EDW investments can be better utilized by reducing ETL costs and resources.