Understanding Cloud ETL | C2C Community

Understanding Cloud ETL

Categories: Storage and Data Transfer
Understanding Cloud ETL

What is Cloud ETL

 

Digital data is constantly being moved between various pipelines for various purposes. Data engineers and IT specialists often use a method called ETL to securely and efficiently move this information to different places, such as data warehouses and centralized databases. 

 

ETL stands for extract, transform and load, noting the three main parts of the overall process. When used to its full capabilities, ETL is helpful in taking disorganized data and making it easier for organizations to navigate and digest. 

 

The ETL Process

 

Data Extraction

The first part of the process is extracting. This occurs when specified data (either structured or unstructured) is taken from a particular source such as: 

Once the data is retrieved, it is sent to a staging area to await data transformation. 

 

Data Transformation

After the data has been extracted and sent to the staging area, it can be transformed in preparation for loading into a new storage area. For example, the data may be a list of names, first followed by last, in a random order. During the transformation process, the data can be reorganized to last name followed by first and alphabetized for easier navigation, and data masking and aggregators are added.

 

Data transformation (or cleaning) often consists of the following actions:

  • Cleaning: finding any inconsistencies in the data and correcting them if possible
  • Standardizing: formatting the data based on predetermined guidelines set by an organization to ensure the data displayed is ready for consumption
  • Deduplication: identifying any duplicate data entries and removing any redundancies
  • Verifying: identifying any data that doesn’t fit in with the data’s eventual uses and removing it
  • Sorting: scanning and reorganizing all data based on the needs of the organization

 

Data Loading

The final part of the process involves actually transferring the reorganized data through pipelines to their final destination, usually data warehouses or data lakes. Data loading is typically performed in one of two ways:

  • Incremental loading: loads only new or updated data points
  • Full loading: fully loads all data

 

Incremental Load vs Full Load

While incremental loading is more efficient and faster because only certain data points are updated, full loading allows all data to be reloaded if there’s an error, something that incremental loading does not allow. 

 

Cloud ETL Tools

 

While the ETL process can help organizations to effectively organize and transfer data in numerous ways, executing the process with only manpower can be complicated, leading to potential errors. ETL tools automate the entire process, reducing errors and speeding up transfers.

 

Benefits of Cloud ETL Tools

Organizations often have massive amounts of data that need to be moved to various places. ETL tools ensure that all pieces of data are securely transferred to all locations necessary with very little (if any) human interference. 

 

Sometimes this means sending data not only to a data warehouse, but also to a cloud setting. ETL tools help to ensure no data is lost or duplicated when sending it to completely different destinations, and ensures that digesting data is as streamlined and accurate as possible. 

 

Finally, cloud ETL tools typically include various forms of technical support, such as chats and forums, adding an additional layer of knowledge and help that organizations wouldn't have when transferring massive amounts of data in-house. 

 

Types of Cloud ETL Tools

 

Cloud ETL Tools

Cloud ETL tools allow those in an organization (with proper access) to easily access and digest massive amounts of cloud-specific data from anywhere. 

 

Batch ETL Tools

Batch ETL tools extract data from different sources, but do so in batches to limit the resources needed during extraction. This makes batch tools more cost-efficient than other tools.

 

Hybrid ETL Tools

Hybrid tools are used when organizations require a data transfer solution that is tailored to their specific goals and needs. Hybrid tools can take aspects from other toolsets to ensure total customization. 

 

Real-Time ETL Tools

Also known as streaming ETL tools, this solution allows for data extraction and transformation to be done in real time, giving businesses actionable data faster than other solutions.

 

Custom ETL Tools

Custom tools often allow for the highest customization and usability. That said, this solution requires IT specialists to program most of the workflow with Python scripts. This makes the overall setup more time consuming and leaves more room for human error than other ETL tools. 

 

On-Premise ETL Tools

On-premise tools are typically best suited for older data transfer architecture. These on-premise workflows typically use data management protocols that aren’t quite up to date, making them perfect for on-premise tools. 

 

Open-Source ETL Tools

When organizations don’t want to depend on third party solutions when securing and analyzing private data, they can use open-source ETL tools as the groundwork to further build their toolset, allowing for more customization and security. 


 

Learn more about the benefits of ETL tools and processes from our community, or contact us today to learn more about partnering with C2C. 

 

Interesting topic on ETL in the Cloud for data transformation /.manipulation


Great information and well articulated but, where are the manuals or tutorials?