What is ETL?

ETL (Extract, Transform, Load) is a data integration process that collects data from multiple sources, standardizes it, and loads it into a data warehouse for analysis, databases for storage or some other type of data source. Organizations use ETL to transform their data that is spread across multiple systems in different languages into unified formats and styles,so they can analyze it more easily. With the information explosion, organizations have huge amounts of data at their disposal.

However, many organizations stumble at the diversity and the volume of the data. Many have trouble moving the data out of their source systems, translating it into a common language, and loading into another system so they can analyze it in its entirety to get a complete view of their data. ETL helps organizations to efficiently utilize data by extracting, transforming and loading data across various systems to enhance their business intelligence.

ETL has numerous use cases across multiple fields. One of them is deriving value from the customer data. Customers interact with a brand in different ways. ETL collates all these customer data from various sources, transforms the data to adhere to a standard format, and then loads it into a data warehouse or other data source for analyzing. When the company can easily analyze their data that is all in the same language and in the same location, this gives the organization an accurate 360-degree view of the customer’s interaction with their brand. It enables the organization to understand customer needs and provide them a highly personalized experience.

Why do organizations need ETL?

Data is one of the biggest assets of an organization. Most of the useful data is unstructured and scattered across multiple sources. Therefore, organizations need data integration in the form of ETL to gather and standardize data and prepare it for analysis in one location. ETL ensures easy and uniform access to data for all the teams. With standardized data, teams can make well-informed decisions, and it leads to better business intelligence. In today’s world, even a single piece of data can make a big difference in profitability. Organizations that want to leverage the power of data should consider adopting ETL.

What are the steps involved in ETL?

As the name suggests, ETL has three major steps – extract, transform, and load.

Step 1: extract data

Extraction is the process of collecting data from multiple sources. These sources might include the following.

  • Customer relationship management systems (CRM)
  • Social media and other online sources
  • Legacy databases and storage
  • Sales and marketing applications
  • Customer transaction data
  • Enterprise resource planning systems
  • Sensor data from the internet of things (IoT)

Data extraction is often performed in three different ways.

Data extraction based on notification of a change

Some of the data sources provide a notification to the ETL system when there is any data change. The ETL system only needs to extract the new data. Notification based data extraction is the easiest method, but many data sources do not provide notifications.

Incremental data extraction

Some sources may not be able to provide notifications on data change. But, they can identify and have a record of which data changed. An ETL system should periodically check such sources to see if there is any change in data. This method incrementally extracts the portion of data that has changed. The incremental extraction is more complex than notification-based extraction.

Full data extraction

Some sources may not have any mechanism to identify the changes in the data. When dealing with such sources, ETL needs to extract complete data from the source. The ETL needs to keep a copy of the last extract so that it can compare it with the new copy. Full data extraction involves a higher volume of data transfer than any of the other methods because each time, the entire dataset needs to be copied. It also increases the load on the ETL system.

Structured and unstructured data

Data from the above sources might be structured or unstructured. Structured data is ready for immediate extraction. The unstructured data needs some preparations to make it suitable for extraction. This includes cleansing the data – for example, removing the whitespaces or emoticons.

Step 2: transform data

Data from different sources might have different structures and characteristics. ‘Transform’ steps apply several techniques to standardize this diverse data. Organizations often apply business rules while transforming data. The sub-process involved in the data transformation are:

Standardization

The format of the extracted data from various sources might greatly vary. Standardization brings the data into a common format. For example, all the zeros in the raw data might be converted to NULL.

Cleansing

Data from sources like social media or email communications might have information that is not relevant. Data, in general, might have inconsistencies and missing values. Cleansing helps to remove the noise in the data and fix the missing values and inconsistencies.

Deduplication

The raw data from a source might have repetition and redundant information. Deduplication removes all such redundancy.

Format revision

This includes converting from one format to another based on the organization’s standards. It may include the unit of measurement conversion, date-time conversion, and conversion of the character set.

Verification

This step checks for data integrity. In this step, the ETL system identifies and flags data anomalies.

Transformation also includes advanced database operations like data aggregation, establishing a key-value relationship, splitting data, and filtering it.

Step 3: load data

In the final step of ETL, the transformed data is loaded into a data warehouse or again, another data source. There are two main ways to ‘load.’

Full load

In full load, all the data prepared in the ‘transform’ step is loaded into the data warehouse as a single batch. While the ‘full load’ takes a long time, it is less complex than the incremental load. The full load might lead to an exponential growth in the data volume in a warehouse, which might become difficult to manage.

Incremental load

It looks for the changes in the incoming data. It creates a new data record only if unique data is found. Incremental load is more manageable when compared to full load. However, it may lead to data inconsistencies if there is a system failure.

An organization can choose a ‘load’ strategy based on what it wants to do with the data. The loaded data might be used for different requirements.

  1. To create a layer of analytics or business intelligence over the data
  2. To use the data as a searchable database
  3. As a training set to a machine learning algorithm
  4. To create an alert system based on the data

Characteristics of the data destination (for example, a data warehouse) also need to be taken into account. The speed, capacity, and data interfaces of the destination may affect the load process.

What are the types of ETL tools?

Based on the requirements, organizations use different types of ETL tools. An organization might choose an ETL category based on what they need to do with the data.

Hand-coding

Some organizations choose not to use any specific ETL tools. Instead, they go for the hand-coding method. In hand-coding, custom scripts are created that perform the ETL workflow. It is challenging to manage and standardize the hand-coding method.

Batch processing tools

These tools process the data in batches, often during off hours so as not to interfere with daily operations. Organizations that do not need real-time ETL capabilities can rely on batch processing tools.

Open-source tools

Several open-source ETL tools are available online. It is a low-cost alternative to commercial ETL tools. Organizations that choose to use an open-source version should be ready to operate and maintain the ETL tool. There may be little or no support.

Cloud-based tools

If an organization cannot afford ETL’s infrastructure, they can look for an ETL platform as a service. Many companies offer ETL services on the cloud that ensure full support, easy integration, and scalability.

Real-time tools

Most of the tools discussed above work in non-real-time. Real-time ETL systems use continuous data processing to extract data from multiple sources and store them in the warehouse. These types of ETL tools are useful in processing stream data, or data from the sensors in the internet of things use case.

What are the benefits of ETL?

Consolidated data

Often organizations struggle with data from multiple sources. Data from different sources might differ in their volume, format, and complexity. ETL standardizes this data and provides a single point of view of the data. ETL enables organizations to quickly retrieve and analyze data. It helps in making better, faster decisions.

Historical Context

Many organizations have historical data stored in legacy data storage systems. ETL can extract data from legacy systems and unify it with the current data. This provides a historical context through which organizations can recognize long-term trends. Historical context helps companies to derive useful insights and enhance business intelligence.

Efficiency and productivity

ETL increases the efficiency of teams by providing them easy access to the data. It takes away the burden of writing custom scripts for data migration, hence increasing productivity. When data is readily available, employees can make well-informed decisions and spend more time on analysis and less time on lower value tasks.

What are the challenges of implementing an ETL workflow?

Huge volumes of data

The ETL system is often designed to handle a specific incoming data volume. In today’s world, enterprise data is quickly growing. The ETL system may not be able to handle the increased volume of data.

Solution: While implementing an ETL workflow/ tool, organizations need to think of scalability. Along with selecting a scalable solution, they should avoid full data loads. Organizations should identify crucial data and avoid all non-essential data. They should also look for parallel data processing.

Changing data formats

Organizations should take into account the dynamic nature of data formats. Data from an external source might not be in the same format or will not have the same frequency. The ETL system should be able to handle this diversity.

Solution: Cleansing data even before the “transform” step is crucial to deal with format changes. The ETL system should be able to identify and alert the transformation tool about the new format. Also, the transformation process in ETL should be flexible and not based on hard-coded rules.

Tightly coupled ETL pipeline

ETL is a complex system with many components and subsystems. Each of these components should be scalable, functional, and flexible. Often organizations tend to use similar technologies and systems for each of these components. When an organization implements the ETL workflow, they tend to apply similar solutions for all the parts. This leads to a tightly coupled, less flexible system.

Solution: Each component in the ETL system should be considered as an independent component. The organization should choose the right tools for each of these steps. Each of these components might need highly specialized solutions. Decoupling the ETL components helps organizations fix or change any part without having to rebuild the entire system from scratch.