Once upon a time, the invention of ETL (extract, transform and load) was a necessity. Now it has become a bygone era of the relational database. In this article, we will discuss a few methods on how one can eliminate the ETL process from the EL.
The Hidden Burden of ETL Process for the IT Department
With the rapid growth in enterprise systems, ETL has enabled the IT departments to fetch data from the relational databases powering the mission-critical business applications, transform it into the right aggregations, and stack it into ODS (operational data stores) or DWs (data warehouses) built for analytics. The workload of this data integration is growing larger as additional data are extracted from the source. This has made it difficult for many organizations that are used to working with old ETL processes, to cope up with their yearly (30%-40%) data growth.
Running the ETL processes is a daily task and this includes:
- Addressing performance issues by the in-process database.
- Updating the ETL scripts constantly to cope with the changing reports and sources.
- Rectifying performance issues and errors as there are missing ETL windows that can delay report generation.
Direct solutions that can support huge data for the existing systems are very expensive. It is the responsibility of the IT departments to press on and run the ETL process. But the pain can also be felt by business professionals as well. If you want to make better decisions based on the data that lies in a DW of ODS, then ETL keeps you from accessing the data timely.
ETL on Hadoop: Taking Data Integration to the Correct direction
Scalability Hadoop is cost-effective and is an ideal fit for ETL processes struggling with huge data growth. Its primary players include organizations like Dell and Cloudera. By adopting Hadoop in the process of handling the data transformation before using Impala, Hive or other Hadoop-based analytical tools, the companies can easily change their cost structure and also eliminate the costly ODS and DW options.
Further, using ETL on Hadoop can be fragile due to its nature of batch processing. In case if any errors need to be fixed or records need to be upgraded, then you need to restart the whole ETL job, which is very time-consuming. Trading away the ACID compliance and transactional integrity of relational database machines, ETL on Hadoop decreases the performance while mitigating the profits in intelligence which can be achieved by tapping more data.
Affixing ETL Over Hadoop via a Hadoop RDBMS
Splice Machine with the help of Hadoop RDMBS can solve these issues in an effective manner. Splice Machine saves cost and the scale-out architecture of Hadoop will all the transactional capabilities are given by RDBMS.
Splice Machine is basically a read-write process, fully supports transactions, if a flaw/failure occurs, one can restart the job from the final executed transaction. Hence, the companies can delete and update data easily and reliably at the record level. Issues like duplication of data while running a job and removing restarts that are highly time-wasting are few issues that Splice Machine solves.
The Splice Machine Hadoop RDBMS can execute and run the reporting workloads in real-time to clear the ETL method issues. Most of the organizations are now using Hadoop more than ETL. Instead of keeping data in a file system, it is kept in the relational database supporting non-ETL methods such as powerful real-time and analysis applications.
Elimination of ETL in the Future
The invention of ETL data integration took place because the traditional systems were unable to handle the OLAP and OLTP in a single system and provide good performance. The use of ETL on Hadoop led to a new way of thinking about the ETL procedure as it changed the entire cost structure around harnessing the Big Data. ETL became analytical as it enabled the tiered storage capability, which allowed data from the operational applications to be manipulated and managed in the data warehouse. A typical Hadoop system cannot be an alternative for operational applications but can be for the data warehouse.
Current advancements in in-memory technology such as Spark makes the ETL outdated by running the OLAP and OLTP applications from the same platforms. This eradicates the need to load or extract data because all the applications can fetch from an HDFS instance and transform it to suit the target database format. Further, it saves time as today’s ETL processes take mere seconds by enabling analysts and applications to get benefited with near-real-time data which is continuously delivered all along the day. More advances are yet to come in this field.
Most of the organizations need more agility, flexibility, and cost-effectiveness, thereby eliminating the traditional methods of ETL and embedding new ones.