Companies use different tools to store their operational data. For example, development teams use Jira, UX designers – Figma, marketers – Google Analytics, and so on. To make the data accessible for reporting, analytics and machine learning models, it should be stored in a single place or repository. For this, you need to extract data from the sources, transform the data to a unified format, and load it into your database. This is what the ETL process actually looks like.
What is ETL?
ETL is an automated process of integrating data from multiple sources into a central repository, such as a database or a data warehouse. ETL includes three steps, defined by each letter of its name:
As an integral element of business intelligence systems, ETL automates the process of data transfer from sources to a destination and contributes to discovering valuable business insights.
Benefits and challenges of ETL
|Dataflow automation||Data latency|
|Improved maintenance and traceability||Data source limitations|
|Enhanced performance to BI||Data validation|
|Improved data quality||Resources for implementing ETL|
|Information clarity and completeness||Data corruption due to ETL pipeline errors|
ETL and data integration
In view of the above, you may have a concern about what’s the difference between an ETL process and a data integration process. Both concepts are closely related and, as a matter of fact, Extract Transform Load is an approach to data integration.
The main difference is that data integration does not involve transforming data to provide you with a unified view of your data taken from multiple sources. The ETL process changes the format of information at the transformation step.
You may need to know this distinction to make a proper choice of a process that you will benefit from the most.
ETL and reporting
These two concepts are complementary to each other within business intelligence:
- An ETL process is responsible for collecting information from data sources and transferring it to one repository.
- A reporting process is responsible for accessing the information in the repository and presenting it to the stakeholders in a legible format, such as a dashboard.
Each concept can exist separately from another. However, if you use a bundle of ETL + reporting, you can benefit from their synergy in terms of automating reporting and speeding up decision making.
How ETL works
To automate data import from different sources into one database, you’ll need to implement an ETL process, which covers the following steps:
E – Extraction of data
Data is extracted from a source or an array of sources onto a staging server. The sources from which the data is extracted can include:
- Databases and data warehouses (Airtable, BigQuery)
- Cloud storages (Google Drive, OneDrive)
- E-commerce platforms (Shopify, WooCommerce)
- Analytics services (Google Analytics, Mixpanel)
- Project management tools (Trello, Jira)
- CRM tools (Pipedrive, HubSpot)
- and many more
T – Transformation of data
The extracted data is checked against a series of validation rules that transform data before loading it into the central repository. These rules may include:
- Conversion to a single format
- Sorting and/or ordering
- and many more
L – Loading of data
The transformed data is loaded into the destination (data warehouse, data lake, or another repository) in batches or all at once, depending on your needs.
What is ETL pipeline
An ETL pipeline is a kind of a data pipeline in which data is extracted, transformed and loaded to the output destination. ETL pipelines typically work in batches, i.e., one big chunk of data comes through ETL steps on a particular schedule, for example, every hour.
How to manage an ETL process and can I do this myself?
If you’re planning to build a custom ETL pipeline from scratch, you’ll have to handle a lot of tasks, such as:
- ETL process management
- Outline the Extract – Transform – Load process and providing system architecture for the entire pipeline
- Manage the development of the system and documenting its requirements
- Data modeling
- Define data formats (models) required for the data warehouse
- Outline the transformation stage
- Define technologies and solutions to implement formatting
- Data warehouse architecture
- Define data warehouse architecture (data marts, accessibility, queries, metadata, etc.)
- Define tools and solutions to load data
- ETL pipeline development
- Implement ETL tools to extract data from sources and upload to the staging area
- Set up the data formatting processes, such as cleansing, mapping, filtering, etc.
- Implement the process of loading the formatted data to the data warehouse.
- Validate data flow
- Test speed
For this, you’ll need an ETL developer or a data engineering team to build and maintain the ETL infrastructure. The team may include data architects, data engineers, data analysts, data scientists, and other dedicated experts who will manage the ETL process.
However, small to medium business owners are not necessary to carry such a burden of tasks. They can benefit from preset solutions with the ready-to-go integrations between data sources and data destinations. So, they won’t have much trouble with setting up their ETL pipeline.
Extract Transform Load pipeline example
Let’s take a look at a simple ETL pipeline in real life. A small e-commerce business uses multiple tools in operational activities, including Airtable, WordPress, Shopify, Hubspot, and Xero. Some data is also stored in three CSV files on a company Google Drive. These are the sources we will extract data from. This is the Extract step.
Google Sheets is the destination database where the historical data is to be stored. Actually, many small and medium-size businesses use spreadsheets as a database, so we will load the extracted data to Google Sheets. This is the Load step.
You’ve noticed that the second step, Transformation, is missing. Actually, it’s not, since the data will be transformed (converted to a single format) by the ETL tool we will use – Coupler.io. The tool provides out-of-the-box integrations for numerous apps, such as Pipedrive, WordPress, Airtable and others.
You can also set up custom integrations via APIs, as well as extract data from CSV and XLSX files. Google Sheets is not the only target destination supported by Coupler.io. You can also choose Excel or BigQuery as a data destination application.
Let’s see what the ETL pipeline setup looks like:
- Sign up to Coupler.io, or you can install the Coupler.io add-on for Google Sheets from the Google Workspace Marketplace.
- Set up the necessary integrations according to the apps and sources you need to load data from. In our case, these are:
- Airtable to Google Sheets
- WordPress to Google Sheets
- Hubspot to Google Sheets
- Xero to Google Sheets
- CSV to Google Sheets
- Shopify to Google Sheets
- To automate data load to the destination app, we can enable the automatic data refresh and customize the schedule for data refreshes.
Basically, one tool, Coupler.io, can Extract – Transform – Load data from your sources to the chosen destination.
The described ETL pipeline is rather simple, since we did not do much data transformation. If required, you’ll have to do this step manually in your spreadsheet using QUERY, FILTER or other Google Sheets functions. So, in our case, we had an ETLT pipeline (extract – transform – load – transform) 🙂
Nevertheless, small business owners who rely on spreadsheets will benefit from this flow. Larger companies, of course, choose other tools and solutions to cover their ETL process needs.
ETL best practices
Refine your source ETL data
You need to understand what data you’re going to extract. With this in mind, it’s better to build an ETL process that will operate refined data without any inconsistent or unnecessary information from your source. Such minimization will let you increase the performance of your ETL pipeline.
For example, you can cut redundant records at the extraction stage, rather than doing this at the transformation stage.
Use a proper destination
The purpose for data transfer should define the choice of the destination for your data. For example, if you want to create an updatable backup, it’s better to use a data warehouse, such as Google BigQuery or Amazon Redshift. For calculation or data visualization, you can go with spreadsheet apps, such as Excel or Google Sheets. So, make sure that your destination for data loading corresponds to your ETL purpose.
Append new entries instead of replacing the entire data set
Your ETL pipeline will load new entries from sources to destination automatically, but this can be carried in two ways: replacing and appending.
With the first option, the data set in the destination will be replaced with the data set from the source with every update. This entails bigger consumption of resources and time.
The better option is to append new entries to the ones that have already been loaded in the ETL pipeline. Such incremental data updates are much more efficient in terms of time and performance.
Use parallel processing in your ETL model
Parallel processing is another best practice aimed at saving your time and resources spent for extract transform and load of data. For example, you have multiple sources, the data from which should go to the destination every Friday. This can be done in series, one source after another, which can take hours of time depending on the data set. Instead, you can run these integrations in parallel, which will complete much faster. Of course, this is possible with a proper infrastructure or ETL tool that you use.
Set up a troubleshooting mechanism
Here you have a variety of options to implement advanced error handling. From this variety, the basic scenario is to set up an alert system that will notify you about any ETL failure. Notifications are good, but they do not solve the issue.
Another option is to tune the troubleshooting mechanism that will trigger bypasses or backup integrations in case of errors. This will let you keep the ETL pipeline running without any damage to your data.
Setting up checkpoints through the Extract Transform Load process is also a good practice. This will help you track errors in the most efficient way.
Log the ETL
Logging won’t save you from errors that have already happened, but documenting all activity in the ETL pipeline will allow you to understand and avoid critical failures in the future. So, log everything from extraction from the data source to loading to a destination.
Another popular best practice is to choose a proper ETL software for your needs. This is because the tool we mentioned above, Coupler.io, will work for small to medium businesses, but is unlikely to be a solution for enterprises and large corporations. What other options do you have?
To tailor a fully customized ETL pipeline, data engineers may use different technologies, such as Python, R, Perl, SQL, etc. However, today there are many ready-to-use solutions that allow data engineering teams to cut much of the scope and speed up the setup.
An ETL tool is a software that implements all steps of the ETL process:
- E – extracts data from one or multiple sources
- T – transforms data for the destination repository
- L – loads data to the destination repository
ETL tools are mostly classified by hosting type:
- Cloud-based: for example, Xplenty, AWS Glue, Skyvia, Coupler.io.
- On-premise: for example, Informatica PowerCenter, Talend, Pentaho.
Additionally, ETL tools can be differentiated by the time of data flow – batch and real-time solutions.
Batch ETL tools, such as Informatica PowerCenter or IBM InfoSphere DataStage use batch processing – they complete the data pipeline in batches of ETL jobs.
Real-time ETL tools, such as Hevo or Xplenty, provide the most efficient time-to-insight ratio by loading data in the shortest possible time.
How to choose ETL solutions
ETL tools speed up the ETL pipeline and save time for ETL developers. So, the choice of the tool should be most efficient, otherwise all the benefits will be spoiled.
ETL pipeline requirements
Before analyzing the market of ETL tools, an ETL developer or another person in charge should define the current and expected requirements:
- Sources to extract data from
- Destinations to load data to
- Rules to formatting data
- Maximum supported data volume
- Frequency of data updates (for real-time solutions)
These insights will shape a clear idea of what integrations you will be looking for. It’s crucial to look to the future and review the ETL tools from a long-term standpoint. So, take into account the data sources and/or destinations that you may be using in years to come.
The variety of ETL tools in the market results in a wide range of pricing models. Examine the options that you can afford, considering the defined requirements for your ETL pipeline.
Set of ETL tools
You’re lucky to find one tool that will cover all your integration requirements. However, the truth is that you may be needed to onboard a few solutions for a single ETL pipeline. It’s OK and likely better than building a custom ETL process from scratch.
ELT or ETL meaning: What is the difference?
With ETL (Extract – Transform – Load), the data reaches the repository in its transformed state. ETL is used in the vast majority of data warehouses.
With ELT (Extract – Load – Transform), the data is first loaded to the database and then transformed. ELT is typically used with massive data lakes and noSQL databases.
The difference between ETL vs ELT lies in the presence of a staging server. Since an ELT pipeline doesn’t have it, the data load is faster than with ETL. You should use ELT processes when you need to load huge data sets to a data lake immediately. The drawback, of course, is that the repository gets unrefined data.
Difference between data warehouses and data lakes
A data warehouse is a repository of structured data from multiple sources. Data warehouses contain up-to-date data that can be used for reporting and analytics. A warehouse may comprise multiple tools to represent data and make it accessible.
Examples of cloud data warehouses: BigQuery, Microsoft Azure SQL Database, Amazon Redshift.
A data lake is a repository of all structured and unstructured data that may require manual processing by the end user. The data in data lakes is used for reporting, advanced analytics and advanced machine learning. Read more about the differences between a data hub, a data lake and a data warehouse.
Examples of cloud data lakes: Google Cloud Storage, Amazon S3, Apache Hadoop.
ETL future development
The traditional ETL concept is a good solution for relational processing; however, its main limitation should be taken into account:
When the data volume goes up, the ETL performance goes down.
In view of this, some data analysts forecast the future transformation of ETL into ELT pipelines. The Extract Load Transform approach allows you to get your data as is and manipulate it right away. This is beneficial for unstructured or semi-structured information.
At the same time, the more obvious scenario of ETL evolution includes the advent of data management frameworks. This means that traditional ETL will combine data integration with data management. So, users will have an out-of-the-box hybrid solution to streamline their ETL pipelines.
Do I need ETL for my project?
You tell us 🙂 If you leverage multiple tools in your workflow and spend hours grabbing data from them manually, then you MUST implement an ETL pipeline. This will save you tons of time and, I believe, budget.
No business can survive without a proper analysis of data taken from different sources. Analyzing data is crucial, and the best way to do this is when you have all your data in one place. Good luck!