Get to know the ETL advantages, disadvantages, and innovations.
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Data Must be Integrated Before it is Pipelined
Enterprises rely on their data pipelines to promote business goals more than ever, as the need for high-quality, trustworthy, and clean data comes up during countless tasks. That’s why it’s essential to build your enterprise data pipeline with a specific data integration style and strategy in mind – and understand the different options before making a final choice. Today, we’ll discuss one of the more traditional approaches as we answer the burning question: What are ETL tools?
Let’s examine the nature of this data integration methodology, its advantages and disadvantages, and how a new entity-based ETL approach is rapidly gaining ground
What are ETL Tools?
ETL, which stands for Extract-Transform-Load, goes back to the 1970s as one of the oldest, and more established, approaches to data integration. As the name suggests, it is based on a process in which data is extracted (from multiple sources), transformed (processed to fit specific formats, cleaned, enriched, and masked for security reasons), and finally loaded (into its target destination).
When big data stores became the norm, ETL allowed enterprises to extract a large amount of data in batches, and process it in preparation for storage in a data lake or data warehouse for analytical purposes. Data scientists typically use this stored data to run various analytical reports and models.
With ETL, the extracted data is first transformed, and then loaded to storage.
To understand better what ETL tools are, let’s dive into each one of the steps involved:
The first step is to collect the structured or unstructured data, which is often fragmented and spread out amount multiple sources, including databases, streams, apps, warehouses, and files. ETL tools enable enterprises to address all these data sources at once, thanks to smart automation that is replacing manual data extraction – a more efficient process that delivers comprehensive results.
Now that we’ve gathered all the relevant data from its various sources, it must be cleaned, enriched, anonymized, and standardized, in order to allow analysts to work with the data and generate business insights.
Transformation is a multi-step process that includes the following stages:
The data is cleaned, to remove any irrelevant, duplicate, wrong, or missing elements. It is then verified, and teams are alerted regarding any issues.
This process ensures that all the data matches the desired destination format, follows the same set of rules, and is properly sorted by category.
Any rules that can enhance the data, and improve its quality and integrity, are added.
After the transformation process is completed, the data should be reliable and clean when it arrives at the storage target destination.
This is the last step of the ETL process, in which the data is loaded into the data lake or data warehouse for use by the enterprise. The data may be divided into sections in an incremental load, or transferred all at once. Incremental loading prevents massive bulks of data from being uploaded when the system cannot properly maintain and manage them, ensuring that only unique records are loaded.
What are ETL Tools’ Advantages and Disadvantages?
Every data integration approach has strengths and weaknesses to consider. When choosing the most suitable method, focus on what the ETL tools’ pros and cons mean for your enterprise.
This established integration method is supported by many different tools. ETL has been around for decades, and data teams are very familiar with it. The transformation process helps to improve the data’s accuracy and integrity, with audit results that meet advanced compliance requirements and protect end customers’ privacy. Being able to upload the data in bulk improves efficiency. It provides access to historical data, while smart automation enables teams to cover plenty of ground without compromising quality or doing too much manual coding.
For high-scale, high-volume extractions, the data transformation phase can be very heavy, in terms of I/O and CPU processing. This limitation often forces data engineering teams to settle on smaller extractions. Data teams also have to provide the business rules in advance, which offers less flexibility, can cost more to maintain, and might make the process more complex. The time-to-insight is relatively long, and the data only reaches its destination after it has been processed, denying analysts access to raw information.
eETL overcomes the disadvantages of ETL and ELT.
Enter eETL, the Entity-Based Approach
While data teams debate between ETL vs ELT, a new enterprise data pipeline solution, called eETL, is emerging, that offers the advantages of both approaches, without the disadvantages. The business entity-based methodology covers all source systems and offers speed and agility without leaving quality or compliance behind. Companies can attribute data to specific customers, products, tasks, sites, and other criteria and use the no-code platform to transform and integrate the data. The process is automated, efficient, fast, and easy. Business entities allow organizations to update the data in real time, ensuring that the business insights gleaned from the data are based on the most current information.