ETL in the Cloud
ETL otherwise known as extract, transform, and load and has become a widely accepted method for organizations to combine data from multiple disparate systems into a single database, data store, data warehouse, or data lake. ETL can be used to store legacy data, or—as is more typical today—aggregate data to analyze and drive business decisions. A practical example is the consolidation of data across multiple Enterprise Resource Planning (ERP) systems due to merger or acquisition.
It is true that organizations have been using ETL for decades. However, the shift to the cloud has introduced both sources of data and target databases that are no longer necessarily in your physical corporate data center.
The industry is beginning to see a shift toward streaming ETL pipelines or “open data flow” managing continuous streams of data in real time versus data handled in aggregate batches. This allows for much larger ETL execution on a continuous basis.
Today’s modern ETL solutions must cope with a rapidly increasing volume and ever more critical speed of delivery of data. Additionally, the ability to ingest, enrich, and manage transactions, and support both structured and unstructured data in real time from any source—whether on-premises or in the cloud—are now basic requirements for today’s enterprise ETL solutions.
Extraction
Extraction is the process of retrieving data from one or more sources—online, on-premises, legacy, SaaS, or others. After the retrieval, or extraction, is complete, the data is loaded into a staging area.
Transformation
Transformation involves taking that data, scrubbing it, and putting it into a common format, so it can be stored in a targeted database, data store, data warehouse, or data lake. This process typically involves taking out duplicate, incomplete, or incorrect records.
Loading
Loading is the process of inserting that formatted data into the target database, data store, data warehouse, or data lake.
Use Cases
Cloud migration
Companies are moving their data and applications from on-premises to the cloud to save money, make their applications more scalable, and secure their data, and ETL is commonly used to run these migrations.
IoT data integration
The Internet of Things (IoT) is the term applied to a hive of connected devices capable of gathering and transmitting data through sensors / beacons embedded in or accessible to the network. IoT devices can include everything from a smartphone to a manufacturing execution system. Or from a SCADA system to factory equipment, network servers, or other machines. ETL helps move data from multiple IoT sources to a single place where you can analyze it. A practical example of this application is the consolidation of patient rooming data in a regional clinic. You might have a system that acknowledges the arrival of that patient via a geofence, data from the EMS software to software that registers a time stamp when the patient has been roomed. By aggregating all that data through and ETL process – a clinic manager would be able to get a sense of the actual wait times and bottlenecks experienced by each patient.
ComputeHub.io provides an interesting alternative to more expensive and complex cloud platforms when it comes to processes like ETL. By offering a wide variety of low-cost, pay-by-the-drink cloud computing options, ComputeHub has a solution for your ETL needs. And we are always open to feedback and suggestions. So if there are certain features or help that you need with your ETL journey, reach out to us directly here. Your success if our success!
