Will Big Data replace ETL?

ETL technology is employed to extract information from source databases, and transform and clean the data before loading it back into a target database. As a result, (extract, transform, and load), or ETL, is an essential element in data warehousing technologies.

A User-friendly Software

ETL is considered user-friendly as it is employed to speedily map columns and tables between target databases and their source. ETL also supplies the functionality to transform data values in multiple systems.

Therefore, extract, transform, and load processes serve as the backbone for enterprise data warehousing. However, with the popularity of big data ETL tools, such as Hadoop, some IT experts are seeing a new method of transforming data.

This specific development has provided the fodder for controversy. For example, Hadoop advocates believe that the data platform is an ideal venue to manage the transformation of data as it features cost benefits and scalability over conventional type ETL software.

The ETL/Big Data Controversy

Alternatively, defenders of ETL software say that the transformation of data through Hadoop does not eliminate the extract and load processes, nor does it address such components as data-governance or data quality. Nevertheless, some IT specialists believe that big data is replacing ETL. Others believe ETL is merely undergoing an alteration but, ultimately, will prevail.

Those companies that use big data tools believe employing Hadoop instead of ETL reduces enterprise data warehouse (EDW) costs and enhances overall performance. By using this approach, data is stored and processed conveniently in one location.

In turn, multiple transformation jobs can be run and information can be delivered to more than one system. This type of approach results in quicker analytics while making it possible to condense both software and hardware within a Hadoop infrastructure.

How Hadoop Can Be Coordinated with ETL

With that being said, it is clear that ETL needs to evolve and adapt to today’s performance preferences, not to mention the scale and latency requirements of current applications. Big data ETL tools, like Hadoop, provide an engine upon which data profiling, data quality, and ETL can operate. Therefore, it appears that software platforms, such as Hadoop, are good for basic ETL, but another tool should be used for advanced ETL applications.

Definitively, Hadoop is an open-source type software framework that allocates the storage of big data sets on computerized clusters. In other words, Hadoop permits you to scale your data up and down without any concern that your hardware will fail.

Hadoop supplies large amounts of storage for about any type of data, an impressive processing power, and the capacity to manage endless concurrent jobs or tasks. In order to harness this kind of power, you need to be well-acquainted with Java. Having this type of familiarity will reap you operational rewards. Many technologies and companies today are integrated with Hadoop.

The hTrunk platform, when employed, demonstrates that ETL is still considered a viable component in the field of IT. Designed to optimize big data ETL tools and operations, the analyst-friendly platform is less complicated and faster-running than its conventional counterparts.

The platform leverages Apache Spark as a data processing tier while accelerating business insight and improving regulatory or governance compliance requirements.

How hTrunk Enhances the Use of ETL

Htrunk employs one of the big data ETL tools, Hadoop, to off-load ETL, thus saving a company on operational costs. All these minor miracles can be achieved by using this approach. Therefore, you can use Hadoop to manage your ETL for more advanced applications, as long as you implement the hTrunk tool as well.

In turn, companies can use hTrunk for upselling, risk management, data aggregation, and data warehouse (DWH) optimization. Because big data is now a part of the IT landscape, you need to integrate software solutions with Hadoop in order to address more advanced ETL issues.

One company, Apex, addresses these concerns. Hadoop consultants in the company specialize in the delivery of scalable Hadoop-based solutions while taking a practical approach to building solutions that can be delivered for improved IT access and management. Their addition of hTrunk for big data and ETL is revolutionizing the way IT specialists handle and manage data.

Need to design a Big Data ETL application? See how hTrunk makes it easy.