If your business has a data warehouse, then you've used ETL (or Extract, Transform, Load). Whether you were loading data from your sales stack into your warehouse, or you were constructing simple pipelines between basic apps, ETL is the lever that unlocks the value of your data warehouse.
But what is ETL? And what kind of ETL tool should you choose to help you flesh out your ETL experience?
Let's talk about the basics of ETL and ETL tools.
The Rise of Big Data
By next year, there will be over 44 trillion GB of data up for grabs. For businesses, that data is gold. Businesses that are tapping into big data are seeing profits jump from 8 - 10%. Plus, around 80% of CEOs say that failure to embrace big data will leave companies crippled and at a significant disadvantage.
So, it's no wonder that data warehouses are considered a significant asset by over 70% of businesses this year. A massive 90% of IT leaders plan on increasing BI spending this year to accommodate this landfall of data.
When it comes to leveraging big data meaningfully, brands have three core tools:
While the data warehouse acts as the storage place for all your data and BI tools serve as the mechanism that consumes the data to give you insights, ETL is the intermediary that pushes all of the data from your tech stack and customer tools into the data warehouse for analysis. The ETL phase is where your business will spend a good chunk of its time and energy when developing a warehouse solution.
But, how does ETL work, and do you really need to use an ETL tool to successfully load data from one system to the next?
What is ETL?
ETL (or Extract, Transform, Load) is a process of data integration that encompasses three steps — extraction, transformation, and loading. In a nutshell, ETL takes raw data from multiple sources, converts it for analysis, and loads that data into your warehouse. Let's cover the three primary ETL steps.
In the first step, data is extracted from a source (e.g., Salesforce, Google AdWords, etc.) into a staging area. The staging area acts as a buffer between the data warehouse and the source data. Since data may be coming from multiple different sources, it's likely in various formats, and directly transferring the data to the warehouse may result in corrupted data. The staging area is used for data cleansing and organization.
A big challenge during the extraction phase is how your ETL tool handles structured and unstructured data. All of those unstructured items (e.g., emails, web pages, etc.) can be difficult to extract without the right tool, and you may have to create a custom solution to assist you in transferring unstructured data if you chose a tool with poor unstructured data capabilities.
The data cleaning and organization stage is the transformation stage. All of that data from multiple source systems will be normalized and converted to a single system format — improving data quality and compliance. During the transformation stage, data may undergo any of the following:
Finally, data that has been extracted to a staging area and transformed is loaded into your data warehouse. Depending upon your business needs, data can be loaded in batches or all at once. The exact nature of the loading will depend upon the data source, ETL tools, and various other factors.
ETL vs. ELT
While ETL is the primary method of loading data from one system to another, some choose to use another method. ELT (or Extract, Load, Transform) extracts the data and immediately loads it onto the source system BEFORE the data is transformed. You'll typically see ELT in use with Hadoop clusters and other non-SQL databases.
There are pros and cons to both ETL and ELT. But, the vast majority of data warehouse use cases will leverage ETL. We start to see the value of ELT when we talk about massive data lakes. Since there isn't a staging server, ELT is typically poor for your average query run. The primary benefit driving ELT is that it has rapid ingestion speeds due to its nature. That being said, the speed is rapid because the data isn't being cleansed before it's dumped into your warehouse.
We heavily recommend that you avoid ELT processes unless you're working on a data lake project or something that requires a bunch of data immediately, regardless of the value of that data.
ETL and OLAP Data Warehouses
Data engineers have been using ETL for over two decades to integrate diverse types of data into online analytical processing (OLAP) data warehouses. The reason for doing this is simple: to make data analysis easier.
Normally, business applications use online transactional processing (OLTP) database systems. These are optimized for writing, updating, and editing the information inside them. They’re not good at reading and analysis. However, online analytical processing database systems are excellent at high-speed reading and analysis. That’s why ETL is necessary to transform OLTP information, so it can work with an OLAP data warehouse.
During the ETL process, information is:
- Extracted from various relational database systems (OLTP or RDBMS) and other sources.
- Transformed within a staging area, into a compatible relational format, and integrated with other data sources.
- Loaded into the online analytical processing (OLAP) data warehouse server.
In the past, data engineers hand-coded ETL pipelines in R, Python, and SQL—a laborious process that could take months to complete. Today, hand-coded ETL continues to be necessary in many cases. However, modern ETL solutions like Xplenty allow data teams to skip hand-coding and automatically integrate the most popular data sources into their data warehouses. This has dramatically increased the speed of setting up an ETL pipeline, while eliminating the risk of human error.
As this Xplenty user notes, “The speed and consistency of Xplenty is impressive, and it more than makes up for what a few tools in our kit may be lacking."
Once the data is integrated into your data warehouse, the highly-efficient nature of the OLAP data system allows for stable, rapid analysis.
ELT and Data Lakes
In contrast to the more widely-used ETL, ELT introduces more flexibility into the data transformation/integration process. By loading data into a "data lake" instead of a structured OLAP data warehouse, you can upload and save all types of structured and unstructured information for use at a later time.
ELT and data lakes take advantage of the high-powered processing offered by modern, cloud-based servers like Snowflake, Google BigQuery, and Redshift. These servers are so powerful that they can perform data transformations on the fly, allowing ELT to skip the staging area and transform only the data you need to analyze at the moment.
In other words, ELT performs transformations right before introducing the data to your BI tools. Because ELT and data lakes work with raw, unstructured information, they allow you to introduce unstructured information like emails and written customer survey responses to machine learning algorithms to derive new kinds of insights.
While ELT lets you store and work with unstructured information, there are important challenges associated with the ELT process that businesses need to be aware of:
- It's still a new technology that continues to be refined.
- The data lakes and ELT tools required for the process are still evolving.
- It's harder to find experts who can develop ELT pipelines.
- ELT is not yet as stable and reliable as ETL.
Even though the upload process in ELT is fast, the need to perform data transformations each time you want to analyze information slows down analysis compared to the high-speed queries and analyses possible with a pre-structured OLAP data warehouse.
The Technical Aspects of ETL and ELT
It's important to pay close attention to the following when designing your ETL and ELT processes:
- Ensure accurate logging: It's vital to make sure your data system provides "accurate logging" of new information. To ensure accurate logging, you'll need to audit data after loading to check for lost or corrupt files. With proper auditing procedures, you can debug your ETL/ELT process when data integrity challenges arise (as they invariably do).
- Flexibility to work with diverse sources of structured and unstructured data: Your data warehouse may need to integrate information from a lot of incompatible sources like PostgreSQL, Salesforce, Cassandra, and in-house financial applications. Some of this information could lack the data structures required for analysis. You need to design your ETL/ELT process to deal with all forms of data—structured and unstructured alike.
- Stability and reliability: ETL/ELT pipelines get overloaded, crash, and run into problems. Your goal should be to build a fault-tolerant system that can recover after a shutdown so your data can move without getting lost or corrupted even in the face of unexpected issues.
- Designing an alert system: To ensure the accuracy of your business insights, an alert system that notifies you of potential problems with the ETL/ELT process is essential. For example, you’ll want to receive notifications and reports for expired API credentials, bugs related to third-party APIs, connector errors, general database errors, and more.
- Strategies to speed up the flow of data: When data warehouses and BI platforms have access to information that is up-to-date, they offer better, more accurate insights at a moment’s notice. Therefore, it’s important to focus on reducing data latency, i.e., the time it takes for a data packet to move from one area of the system to the next.
- Growth flexibility: Your ETL/ELT solution should be flexible to scale up and down according to your organization’s changing data needs. This will save money on cloud-server processing and storage fees, while providing the ability to scale up as required.
- Support for incremental loading: Using change data capture (CDC) speeds up the ETL process by permitting incremental loading. This lets you update only a small part of your data warehouse while ensuring data synchronicity.
ETL: To Tool or Not to Tool
Should you use ETL packaged tools OR should you patch together a library, framework, and other open-source solutions? Better yet, should you just do the whole ETL process by hand?
This is a complex question. It will really depend on your business needs, time commitment, schemas, integrations, and overall ETL needs. If you're looking to perform a few really simple jobs, you might be able to custom code a Python solution for your ETL needs. If you're handling jobs that are a little bigger, you can use workflow orchestrators like Apache Airflow, or you can simply use pandas to create a solution.
When we talk about ETL tools, we mean full-blown ETL solutions. So, Apache Airflow and Luigi certainly qualify as tools. But, so do many of the cloud-based tools on the market.
Cloud-based ETL Tools vs. Open Source ETL Tools
Choosing the right ETL tool is a critical component of your overall data warehouse structure. There are a few different options that businesses can choose depending upon their overall ETL needs, data schemas, and operational structure.
Cloud-based ETL tools like Xplenty offer rapid, real-time streaming, quick integrations, and easy pipeline creation. The primary benefit of cloud-based ETL tools is that they work immediately out-of-the-box. Plus, they're hyper-useful for a variety of ETL needs, especially if the majority of your warehouse exists in the cloud (i.e., Redshift, Snowflake, or Big Query).
Open source ETL tools come in a variety of shapes and sizes. There are ETL frameworks and libraries that you can use to build ETL pipelines in Python. There are tools and frameworks you can leverage for GO and Hadoop. Really, there is an open-source ETL tool out there for almost any unique ETL need. The downside, of course, is that you'll need lots of custom coding, setup, and manhours getting the ETL operational. Even then, you may find that you need to tweak your ETL stack whenever you introduce additional tasks.
The Benefits of ETL Tools
Why use ETL tools in the first place? After all, you can certainly hand-code each ETL process if you were so inclined. So, why bother?
- Scalability: Trying to scale-out hand-coded ETL solutions is difficult. As schema complexity rises and your tasks grow more complex and resource-hungry, establishing solid pipelines and deploying the necessary ETL resources can become impossible. With cloud-based ETL tools like Xplenty, you have unlimited scalability at the click of a button.
- Simplicity: Going from a hand-coded ETL solution using SQLAlchemy and pandas with rpy2 and parse to something as simple as a cloud-based ETL can be lifechanging. The benefits of having all of your needs layered into one tool saves you time, resources, and lots of headaches.
- Out-of-the-box: While open source ETL tools like Apache Airflow require some customization, cloud-based ETL tools like Xplenty work out-of-the-box.
- Compliance: The overwhelming nature of modern data compliance can be frightening. Between GDPR, CCPA, HIPAA, and all of the other compliance and privacy nets, using an ETL tool that bakes compliance into its framework is an easy way to skip difficult and risky compliance setups.
- Long-term costs: Hand-coded solutions may be cheaper up-front, but they will cost you in the long run. The same thing could be said about open source ETL tools. Since you have to spend time and energy on modification, you're forced to onboard early or risk delaying project launches. Cloud-based ETL tools handle maintenance and back-end caretaking for you.
Xplenty gives you the power to create rapid, hyper-visualized data pipelines that are infinitely scalable and incredibly intuitive. With tons of integrations, service hooks for your existing monitoring systems, and untouched elasticity and scalability, Xplenty has the features you need to create a successful data warehouse.
Do you want:
- An incredibly dynamic interface that lets you build scalable pipelines without code?
- A pipeline tool with a REST API connector for Rest Web Services?
- The ability to ETL to major platforms like Salesforce?
- A G2 certified Leader in the ETL space?
If so, Xplenty may be for you! Contact the Xplenty team now to learn how to accomplish all of the above.
ETL (Extract, Transform, Load) is a process that loads data from one system to the next and is typically used for analytics and queries. There are plenty of ETL tools on the market. Most businesses will have to choose between hand-coding their ETL process, coding with an open-source tool, or using an out-of-the-box cloud-based ETL tool. The ETL tool that you choose will significantly impact your daily workflows, so we recommend doing the research and heavily vetting your tools pre-adoption.