3 Phase of ETL – How It is Used?

In today’s world, all are curious to know more about ETL. ETL is a data integration that refers to the three steps (extract, transform, load). And also it will be used to blend data from multiple sources.

Furthermore, it will be used to build a data warehouse. During this operation, data extracted from a source system. And also transformed into a format that can be analyzed.

Extract, load, the transform will take turns but related approaches designed to push processing down to the database.

Also Read: Deep Learning AI: The Future of Smart Network

3 Phases of ETL are:

1. Extract
2. Transform
3. Load

1. Extract

Extract means extracting data from the source system. And making it available for later processing. And the main purpose of the Extract phase is to get data safely from the sender.

Importantly, This phase has a high response time, performance, and security.

Particularly, before data transmitted to a new system, it should be extracted fully from its source end. During the first phase, arranged and unarranged data will be sent and transformed into a single repository.

Unprocessed Data can also be Extracted From a Wide Range of Sources:

1. Different databases
2. Hybrid environment
3. Cloud storage system
4. Mobile applications
5. Data warehouses
6. CRM system
7. Mobile devices

ETL Tools:

1. SAP Data Services
2. Talend open studio & Integration suite
3. SQL Services Integration Services(SSIS)
4. IBM Information Server(Datastage)
5. Oracle Warehouse Builder(OWB)
6. CloverETL
7. Microsoft SSIS
8. Informatica PowerCenter
9. Syncsort DMX
10. Cognos Data Manager for good and improved performance.

2. Transform

Through this phase, laws, direction, regulation, and different types of rules can be applied. That ensures data quality, privacy, and accessibility. In this operation, we have to process data to check its usage to the end-user.

This phase consist of different sub-processes:

• Cleaning
• Standardization
• Duplicating
• Verifying
• Sorting
• Other

Data transfer is also an important phase of the ETL process. As it is responsible for the integration of data. Formation of data for the next phase. This phase helps data to be available for ready to use and fully accessible.

3. Load

This is the last and final phase of the ETL process, responsible for loading the newly transferred data. Throughout the load operation, It is compulsory to check that the load is exercised. And also with little sources and time.

Data can be loaded as:

1. All at once(Full load)
2. At scheduled Intervals(Incremental load or partial load)

All at once (Full load):

1. In this operation, ETL full loading means Everything that comes by transferring data. That directly goes to the new, latest, and unique location in the data warehouse.
2. On these occasions, Full loading is difficult as per the maintenance point of view.

At Scheduled Intervals(Incremental load or partial load):

1. It is also a  less inclusive but more managed approach in this type of loading.
2. Indeed, scheduled intervals loading compares to the incoming data with the already existing data. And produces extra records if any of the new information available or found.
3. In addition, this loading system is beneficial for smaller data warehouses to maintain data.

How ETL(Extract Transform Load) Is Being Used?

Core ETL and ELT tools will work with other data integration tools. And also with various other aspects of data management. They are data quality, data governance, virtualization, and metadata. Popular uses of today include:

ETL and Traditional Uses

• ETL is a proven method that many other organizations use every day.
•  Such as retailers who need to watch sales data regularly or health care providers are looking for an accurate depiction of claims. 
• It can combine and also transact data from a warehouse or other data store. So that it’s ready for business people to view in a format in a way that they can understand. Nowadays  ETL  also used to migrate data from legacy systems to modern systems. It also used to merge data from business mergers and to collect and join data from external suppliers or partners.

ETL With Big(more) Data – Transformations and Adapters

• Whoever gets the more data, wins. While that’s not really true, having easy access to a broad scope of data can give businesses a competitive edge. Today, businesses need entrance to all sorts of more data. Mainly from videos, social media,  IoT, server logs, spatial data, open or crowdsourced data, and more.
• ETL traders also add new transformations to their tools to support and to engage these emerging requirements and new data sources. And also Adapters give access to a huge and a good variety of data sources, and data integration tools to interact with these adapters to extract and load data efficiently.

ETL for Hadoop – and More

• ETL has also evolved to support integration across much more than traditional data warehouses. One more important thing to notice is that Advanced ETL tools can load and convert structured data into Hadoop.
• These tools and write multiple files in parallel from and to Hadoop. And also simplifying how data combined with a common transformation process. Some solutions incorporate libraries of prebuilt ETL transformations for both the transaction and also interaction data that run on Hadoop. 
• And also ETL provides integration across transactional systems, operational data stores, BI platforms, data management hubs, and cloud.

ETL and Self-Service Data Access

• Self-service data preparation is always being a fast-growing trend that puts the power of accessing, blending and transforming data.
• This approach also increases organizational agility and frees IT from the burden of provisioning data for business users.
• In consequence, both business and IT data professionals can improve productivity.

Data Quality and ETL

• ETL and data integration software tools that are beneficial for data cleansing. And also beneficial for profiling, and auditing ensure that data is reliable. ETL tools also combine with data quality tools. 
• And also ETL vendors incorporate associated tools to build their solutions. Mainly such as those beneficial for data mapping and data lineage.
• ETL and Metadata
• Metadata helps us understand all the lineage of data and its impact on other data assets in the organization. 
• As data architectures become more complex, it’s important to track how the different data elements in your organization are beneficial. 

Mainly such as ETL jobs, applications will be affected.

Final Words:

From the above discussion, it is clear that data is the most dominant element to the success of any business. Following, the Phases should be easily available, accessible. ETL phases are of the highest compelling.

On the other side when we talk about performance, there are risks too available.

There is also the possibility of data corruption or missing in the case of ETL phases, there are other issues available. But while phases are also beneficial to any business. To avoid all risks related to the ETL phases data, protect every aspect of data.