Find out how the ETL method of data integration enhances enterprise data pipelines, and how eETL overcomes the most difficult challenges. Learn more here.
Table of Contents
Data Must be Integrated Before it's Pipelined
What are ETL Tools?
What are ETL Tools’ Advantages and Disadvantages?
Enter eETL, the Entity-Based Approach
Enterprises rely on the concept of a data pipeline 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 an enterprise data pipeline together with a comprehensive set of data integration tools – to 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
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, cleansed, enriched, and secured via data masking when necessary), 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 data migration, or 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.
To understand better what ETL tools are, let’s dive into each one of the steps involved:
Extract
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.
Transform
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:
Cleansing
The data is cleaned, to remove any irrelevant, duplicate, wrong, or missing elements. It is then secured, by data masking tools, and verified, with teams alerted if any issues emerge.
Standardization
This process ensures that all the data matches the desired destination format, follows the same set of rules, and is properly sorted by category.
Enrichment
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.
Load
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.
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.
Pros
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.
Cons
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.
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.