What is ETL (Extract, Transform, Load)? – The Ultimate Guide
Today’s enterprises have access to more data than ever before. Websites, analytics tools, and SaaS applications create a never-ending stream of data, but unfortunately, the way businesses store this data makes it increasingly difficult to analyze and extract valuable insights.
A solution is to integrate enterprise data into a centralized data store using ETL.
The term ETL pops up whenever there’s talk about big data, data warehousing, and analytics. So what is ETL all about, and how can it help you make more informed business decisions?
Table of Contents
What is ETL?
ETL is a process that allows companies to gather data from multiple source systems and store it in a single location to analyze or visualize consolidated data. When moving large volumes of data from different sources to a single centralized database, you need to
- Extract data from its original source
- Transform data by removing duplicates, combining similar sets, and ensuring quality
- Load data into the target system
The ETL process has a key role in achieving business intelligence and enabling a wide range of data management strategies.
As the popularity of databases grew in the 1970s, ETL was introduced as a way of integrating and loading data for analysis and computation. Over time, it has become the primary method of processing data for data warehousing workflows.
ETL is an essential part of data analytics and machine learning processes. Using a series of rules, ETL cleans and organizes data in a way that suits specific business intelligence needs, such as monthly reporting.
However, ETL can also handle more advanced analytics, allowing teams to improve both the back-end processes and end-user experience.
Companies often use ETL to:
- Extract data from legacy systems.
- Cleanse their data to improve its quality and make it more consistent.
- Load data into a target destination, such as a data warehouse.
How does ETL work?
To explain how ETL works, we need to understand what takes place in each step of the process.
Data-driven businesses manage data from a variety of sources and use different data analysis tools to achieve business intelligence. For this process to work, the data must flow freely between systems and apps.
In the extraction phase, raw data is copied or exported from data source locations to a staging area. In other words, it enters the data pipeline. This data can come from a variety of data sources, both structured and unstructured data. Those sources may include:
- SQL or NoSQL servers
- CRM and ERP systems
- Sales and marketing apps
- Flat files
- Analytics tools
- Web pages
- Filtering, cleansing, deduplication, validation, and authentication of data.
- Calculations, translations, and summarization of raw data. This often involves changing row and column headers for consistency, converting currencies and other measurement units, editing text strings, etc.
- Running audits to ensure data integrity and compliance.
- Removing, encrypting, or protecting data governed by industry or governmental regulators.
- Formatting the data into tables or joined tables to match the dataset schema of the data warehouse.
Transformation is typically the most important part of the ETL process, as it improves data integrity, removes duplicate data, and ensures that raw data arrives at its destination in a state ready to use.
In the last step of the ETL process, the transformed data goes from the staging area into a client’s data warehouse. The data is usually loaded as a whole (full loading), which is followed by periodic changes (incremental loading) and, less often, full refreshes to erase and replace unnecessary data in the warehouse.
- Full loading: In this loading scenario, everything that comes from the transformation pipeline lands into new unique records in the data warehouse. Although reasonably fast, the full loading process produces datasets that quickly grow to the point where they become difficult to maintain.
- Incremental loading: A slower but more manageable approach where incoming data is compared with what is already in the storage and only produces additional records if new and unique information is found.
The ETL process should be automated, well-defined, continuous, and occur in batches. But it wasn’t always that way. Historically, ETL was time-consuming and prone to error, even if it bound whole teams of tech to manage it.
Now, however, many ETL tools automate and simplify the process.
One of those tools is Whatagraph.
When designing Whatagraph, we were going for a tool that would be able to quickly and reliably load data from various sources into a central repository, while ensuring data quality.
To achieve this, Whatgraph delivered a data transfer workload that saves the time needed to load data from multiple sources to BigQuery.
Traditional vs. modern ETL
The ETL process can be traced to the emergence of relational databases and attempts to convert data from transactional data formats, such as financial and logistical data, to relational data formats, such as Microsoft SQL Servers, Oracle Database, and MySQL, which are suitable for analysis.
Historically, raw data was typically stored in transactional databases that could handle many read and write requests but offered limited analytical capabilities.
For example, in an eCommerce environment, the transactional database stored the purchased item, customer details, and order details in one transaction — think of it as one row in a spreadsheet.
Over time, the list of transactions grew, and many of them had repeated entries for the same customer who purchased multiple items or different customers who purchased the same item.
As data was duplicated, it became increasingly difficult to analyze purchasing trends or habits.
To solve this issue, ETL tools automatically converted this transactional data into relational data with interconnected tables, where teams could use queries to identify patterns and trends.
With the advent of cloud technology, both data types and data sources increased exponentially, enabling the creation of vast databases known as data sinks. Those data sinks receive data from various sources and have underlying hardware that can scale over time. ETL tools have also become more advanced and able to convert data from legacy systems to modern data formats and store it in:
- Data warehouses: These are centralized data storages that can store multiple databases. Data is organized into tables and columns that describe the data types in the table (metadata). Data warehouses can exist on multiple types of storage hardware, such as SSDs, hard drives, or cloud storage.
Examples: Google BigQuery, Amazon Redshift, and Snowflake.
- Data lakes: Repositories that accept structured or unstructured data at any scale. You can store data without first having to structure it. Data lakes are useful to data engineers as they can run different types of analytics on the data, such as SQL queries, big data analytics, full-text search, real-time analytics, machine learning, etc.
Examples: Google Cloud, Amazon Web Services, and Oracle Database.
What is the difference between ETL and ELT?
The main difference between ETL and ELT (Extract, Load, Transform) is in the order of operations.
In an ELT process, data is extracted from the source locations, but instead of loading it into a staging area pending transformation, raw data is loaded directly to the target data repository to be transformed as needed.
Both the ETL and ELT process can make use of a range of data repositories such as databases, data warehouses, and data lakes, each process has its pros and cons.
- ELT is useful for high-volume, unstructured datasets as loading occurs directly from the source. Because of this, ELT is more suitable for big data management since there’s not much upfront planning.
- The ETL process, on the other hand, needs more definition in the pre-stage. It must identify specific data points for extraction, and then there are business rules for data transformations that need to be defined. Still, it’s a well-established process with a standing record of good practices and use cases, while ELT is a relatively new process.
How does ETL benefit business intelligence?
Automated ETL processes are a massive leap from manual data loading. But technical perspective aside, what can ETL do for your business intelligence?
Presents a consolidated point of view
ETL provides a consolidated view of data for in–depth analysis and reporting. Managing multiple data sets simultaneously requires time and coordination, which can cause delays. ETL solves this problem by combining databases and different forms of source data into a single, unified view.
This data integration process not only improves the data quality but also saves the time needed to move data and makes it easier to aggregate, analyze, and visualize large datasets.
Provides historical context
ETL allows users to combine on-premises legacy data with data collected from new platforms and SaaS apps. This results in a historical view of data where older datasets can be visualized along with more recent information.
Automates data processing
ETL automates repetitive tasks for a more efficient transformation process. ETL tools automate much of the data migration process, so you can choose to integrate new data periodically or at runtime. As a result, teams can spend more time obtaining actionable insights from data instead of being pinned down by data extraction and formatting tasks.
Enables accurate data analysis
ETL delivers more accurate data analysis that makes it easier to meet compliance and regulatory standards. You can further integrate ETL solutions with data quality tools that profile, audit, and clean data, ensuring its trustworthiness.
How can ETL help your business?
Multiple departments in a modern organization can benefit from insights derived from big data brought together by ETL.
- Marketing teams can assess the conversion rates of campaigns and develop more impactful strategies.
- Sales teams always need accurate, relevant, and high-quality information about prospective customers and their purchasing habits.
- Customer success teams want to delve deep into the feedback customers leave across the touchpoints to address problems and improve customer experience.
By extracting and aggregating the data your teams need, ETL can help solve these and many other issues.
When you use an ETL tool like Whatagraph, analytics and reporting workflows become increasingly more straightforward and efficient.
With Whatagraph, you can initiate a data transfer from a selected source to BigQuery in a few clicks and then visualize BigQuery data in a dashboard in only three steps:
- Select your BigQuery account.
- Connect your sources — Google Analytics, Google Ads, Facebook Ads, and many more.
- Drag and drop the metrics and dimensions you want to see.
ETL also helps create an environment that supports data governance and data democracy.
- Data governance: All-around management of your business data, including availability, usability, integrity, and security.
- Data democracy: Everyone in your company who needs to analyze data has access to it. Allows you to flatten learning curves and help teams ask the right questions.
How to choose an ETL pipeline?
You can achieve ETL in two ways:
- You can hire developers to build your own ETL pipeline however, this process can take a long time, be prone to delays, and be expensive, not to mention trial and error.
- This is why most companies choose proven and tested third-party ETL tools that not only save time and money but make data less vulnerable to security risks and loss of data due to human error.
When choosing an ETL solution, consider the following:
- Number and diversity of connectors
- Ease of use.
Whatagraph is specially designed to allow non-tech users to move complex datasets from a variety of marketing data tools to BigQuery safely and effortlessly.
If you’re considering Whatagraph as your ETL tool, you’ll be happy to hear that our data transfer functionality is available even with the most basic pricing plan.
If you’re looking for an easy-to-use solution to connect data from various marketing sources, keep your marketing data safe, or want to visualize your BigQuery data, book a demo call and find out how Whatagraph can help.
Published on Apr 05 2023
WRITTEN BYNikola Gemes
Nikola is a content marketer at Whatagraph with extensive writing experience in SaaS and tech niches. With a background in content management apps and composable architectures, it's his job to educate readers about the latest developments in the world of marketing data, data warehousing, headless architectures, and federated content platforms.
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