As your company is scaling up, business analytics becomes increasingly important. And one of the biggest challenges is managing the vast amounts of data that a large business tends to create — sales records with dozens of data points, customer information, etc.
The first thing that comes to mind is the classic ETL protocol that first transforms raw data and then stores it for easier analytics. However, the new way of storing data, ELT, provides more opportunities for BI. This scheme stores vast amounts of raw data that can later be used for data mining or machine learning with potentially better results.
What should you choose for your organization? Follow this guide to find out all the pros and cons of the two and see examples of both systems in use.
Key differences between ETL vs ELT
| ETL | ELT | |
|---|---|---|
| Data is first transformed and then loaded into the data storage solution | Working order | Data is loaded into the data storage solution and then transformed |
| Highly developed technology | Ease of adoption | New technology, may require more investment to adopt |
| Requires a staging area outside of the warehouse | Data transformation | Happens within the warehouse or a lake |
| Can be used to transform unstructured data and load it into structured form | Support of unstructured data | Can be used to store unstructured data |
| Slow | Data loading speed | Fast |
| Initially takes longer, but later on, analysis becomes faster | Data transformation speed | Can be faster since you can run smaller queries on the raw data |
| Best for on-premise data warehouses. | Data storage | Best for data lakes, lakehouses, or marts. Works well with cloud-based storage as well. |
| Low | Storage size | High |
| Structured data | Source data | Structured, unstructured, or semi-structured data |
| Best suited for small amounts of data | Amounts of data | Works well with large data pools |
| Only transformations selected by the user are available | Availability of data | All raw data is available |
| High | Latency | Low |
| Low | Flexibility | High |
| Low | Scalability | High |
| Legacy on-site solutions require expensive maintenance | Maintenance | Cloud-based solutions do not require maintenance |
| Can be extremely costly upfront if you’re using an on-site solution. Cloud-based solutions are much cheaper. | Cost | The initial cost for loading data is low, but most cloud-based solutions charge per query. Running large queries may be very expensive. |
How ETL works?
ETL stands for “Extract, Transform, Load.” When you’re using ETL tools for data manipulation, you first need to transform the data pulled from various sources (webhooks, databases, or files), and then load it into your database, whether it’s on-site or cloud-based.
Naturally, you need a staging area for data transformation before it can be loaded into the storage. This can often become a bottleneck, especially if you’re working with large amounts of data. On the plus side, the structured data that is going to end up in your storage will be instantly ready for analytics.
How ELT works?
As the name suggests, ELT turns this concept around. Instead of transforming the data prior to loading, it first loads the extracted data, and only then transforms it. It eliminates the need for the staging area and the bottleneck that it can create in your system.
The downside is that you’ll need to store loads of raw unstructured data and transform it before it can be analyzed. This can be great if you need historical reporting or want to experiment with data mining, but the price per query can prove to be too high for smaller companies.
ETL vs ELT pros and cons
Even though ELT is the newer development in data science, it doesn’t mean it’s better by default. Both systems have their advantages and disadvantages. So let’s take a look before going deeper into how they can be implemented.
ETL pros:
1. Fast analytics
While transforming data prior to loading it into your storage solution can be a rather slow process, it does come with its advantages. Since the data is already pre-structured when it’s loaded, analyzing it is fast and straightforward.
2. Safety compliance
Another benefit is compliance with safety protocols, especially GDPR. When using ELT, you inevitably load raw data that can reveal your customers. This can include email addresses or IP addresses that must be masked as per the EU guidelines. If your storage is based outside of the EU, you’ll be breaking the law by uploading this type of data without transformation.
With ETL, this problem is non-existent as you transform the data prior to loading and do not include sensitive pieces of data.
3. Low storage requirements
When it comes to data storage, ETL also has a great benefit. As you’re not storing large amounts of raw data, you don’t need as much storage capacity. This is cost-effective if the storage solution you use charges for storage.
4. Wide adoption
The last of the major benefits of ETL is the fact that it’s been out there for over twenty years. As a result, there’s a lot of infrastructure and tools surrounding it. So, you will have little to no problems implementing it in your organization.
ETL cons:
1. Initial cost
One of the most obvious disadvantages of ETL is the high initial cost. The cost will be measured in hundreds of thousands of dollars if you plan to have on-site data storage. Even if your storage is cloud-based, the initial cost is going to be rather high because you need to start with creating a transformation algorithm. With ELT, you can start uploading data and then run small transformations before settling on an advanced custom solution.
2. Lack of flexibility
ETL isn’t as flexible as ELT. Since you need to have a large transformation algorithm that needs to export data and transform it prior to loading, it would take a lot of work to change it. If you’re changing data sources or need to add a new one, this could mean rewriting parts of the large algorithm, and dealing with legacy is never easy.
3. Slow processing
A minor but an important drawback is that working with large amounts of data becomes increasingly difficult with ETL. The transformation stage may become a bottleneck in the system and cause it to process the data slower.
ELT pros:
1. Fast loading
One of the main advantages of ELT, as opposed to ETL, is a much faster processing speed when dealing with Big Data. A BigQuery or Amazon Redshift pipeline will load all your raw data much faster than any ETL solution would.
2. Lots of analytics options
Since you’re storing large amounts of raw data, ELT is ideal for broad scope analytics. You can both run very limited transformations to get reports about specific things, or a large analysis of historical data. ETL doesn’t allow this as it doesn’t store raw data.
3. Low maintenance requirements
Maintenance will be out of the picture with most ELT systems as you won’t have to use on-site storage, and companies that offer ELT services do all the maintenance themselves.
ELT cons:
1. Hard to implement
The price for that ease of maintenance is that setting up an ELT pipeline can be hard and expensive. In addition, it’s a relatively new technology and finding an employee or a contractor who possesses a high level of expertise can be a challenge.
2. Price per query
Running such a system can also incur a high cost on your business. Most ELT tools charge business owners for each query depending on the amount of data being transformed. With larger queries, it can be rather expensive and smaller businesses may not have the resources to be running analytics constantly.
ETL vs ELT example
Now, let’s look at some practical examples of how these pros and cons can play out in real life.
ETL example
A classic example of an ETL pipeline is a daily reporting solution. A small to medium ecommerce company using a single tool or a small arsenal of tools that are not bound to change in the near future does not need a complex and expensive solution like ELT. It also does not have the resources to handle the storage overhead that inevitably comes with ELT.
The first step towards a simple ETL pipeline for reporting would be finding an importing and transformation solution. Coupler.io is a great choice for that.
This is a data analytics and data automation platform that can natively integrate with 70+ business apps, including plenty of standard ecommerce tools, and can draw raw data from most platforms via API or CSV and JSON files.
Coupler.io allows you to transform the extracted data in various ways – filter, sort, restructure, blend into one dataset from several apps, and more. In addition, you can fine-tune data imports by picking only those data entities that you need to extract for your reporting needs.
Afterward, Coupler.io can load transformed and ready-for-analysis data to BigQuery, Excel, Google Sheets, or Looker Studio.
Another handy feature is Automatic data refresh. You can set up a flexible schedule for data imports to run daily or monthly with set time intervals. If needed, you can have your data in the destination app updated as often as every 15 minutes.
With the data you’ve selected being uploaded automatically, you won’t have any trouble accessing it for quick analytics. Try Coupler.io for your project.
The obvious drawback is that this company would have limited access to historical data, but this can be sacrificed to reduce overhead and still have a decent reporting solution.
ELT example
A good example of an ELT pipeline would be a large company or one that is scaling up fast and has multiple, often changing data sources. The main goal of this company is not to run simple reports but to obtain useful insights from huge pools of data. So naturally, this company has the resources to afford the cost of running multiple large queries.
It would be a massive inconvenience to create and then constantly improve the transformation stage of an ETL pipeline, so this company will benefit from an ELT solution a lot more.
Loading data is the easiest part of the system, though. To make the most of ELT, this large company would have to develop multiple transformations and execute them. This comes at a high price for most ELT tools, but since companies that use this solution know that having an edge on the competition makes or breaks their company, it’s worth it.
When to use ETL or ELT?
Every company and organization is different. If you want to decide whether you want to use ETL or ELT at yours, refer to these points.
ETL use cases
Typical ETL use cases include automating the reporting process, collecting customer data or marketing data for analysis, integrating financial data from different data sources, and so on.
When you need ETL – checklist:
- Your company has its own storage
- Data storage cost is crucial
- You’re working with small to medium amounts of data
- Your data is consistent
- You need standard analytics
ELT use cases
The most typical use cases would be big data processing, storing large datasets to track historical changes, and integrating data from multiple sources for various data science tasks.
When you need ELT – checklist:
- You deal with a lot of data
- Data needs fast storage
- You need historical data analysis
- Data sources change often
- You run different types of analytics
- Have the resources to process
Making the final choice
The final choice between what to use, ETL or ELT, is always on your shoulders. Only the owner of a business knows what their business model needs. Feel free to use this article as a useful resource and use the checklist above to decide whether you want to go for a cheaper, standardized solution or a more expensive one that allows for more flexibility.
