The five critical elements of ETL methods for analyzing data:

  • Extract, transform, load — ETL — pulls data from each business tool, transforms it into a new, universally readable “language,” and loads it into a single repository known as a data warehouse.
  • Multi-stage and in-warehouse are two ways you can load data.
  • The less data you burden the ETL process with, the cleaner — and faster — the entire process will be.
  • offers a new ETL tool with reverse ETL capability and a blazing-fast CDC platform for real-time change data capture, making the whole ETL process incredibly easy and efficient.
  • Understanding your data, your most important metrics, and how you’ll track them can inform your decision regarding the best ETL method for your business’s data analysis.

ETL is a critical process necessary to discover the nuggets of truth locked inside a business’s vast ocean of data. Different data points, once analyzed as a whole, help businesses make smarter, more informed decisions. Companies that create substantial data figures, all coming from different, separate sources, find that ETL impacts the business’s decisions considerably.

But what is ETL? And which ETL method is the best to find the data you need, present it in the best format for analysis, and provide the answers to your business’s questions?

Table of Contents

What’s ETL?

Extract, transform, load — ETL — pulls data from each business tool, transforms it into a new, universally readable “language,” and loads it into a single repository known as a data warehouse. ETL integrates data from various unconnected solutions and creates a new, consolidated data store from which a business can see a unified view of its status at any given time.

Database popularity grew in the 1970s. Even then, tying that data to results was difficult. When ETL was first introduced, it seemed novel: could integrating this data actually allow for deep and meaningful analysis? Fast forward to the evolution of Big Data. Eventually, ETL became the most popular method of processing vast data pipelines into a streamlined, singular source of truth for all types of businesses.

ETL is the base for analyzing data and creating machine learning (ML) workflows. Various business rules are applied, then this process cleanses entries and organizes each so this data can also handle advanced analysis and address multiple business intelligence (BI) requirements. For instance, data that’s gone through ETL can be used for average intelligence needs, such as monthly reports, but can also handle advanced analysis and improve other processes within a workflow, such as an end user’s experience.

The typical stream of ETL looks something like this:

  • Extracting data from a legacy system
  • Cleansing data for improved quality and uniformity
  • Loading the cleaned data into a new, separate database

Real-World Examples of Practical ETL Use Cases

The cleansed and loaded data offers an insight into multiple business processes in one location, ultimately driving improved business decision-making. In a general sense, ETL is especially helpful when:

  • Several sources must be integrated into one
  • Each source is formatted differently
  • A process needs to be repeatable, whether every day, hour or other agreed-upon frequency

ETL is also helpful for obtaining the most use out of a pre-built warehouse. Some real-world examples of practical ETL use cases include:

  • A wholesale clothing company distributes to various retail brands, all of which have a different platform and/or system. Data arrives to the wholesaler in multiple formats but is made usable for business intelligence (BI) tools and purposes using ETL.
  • A worldwide shipping company processing thousands of shipments every day. Data comes from online and physical retailers, shopping platforms, and other sources that make analyzing the data impossible. Using ETL eliminates redundancy and inconsistency, meaning the data quality isn’t just usable but improved.
  • A financial services company is converting to a new enterprise resource planning (ERP) tool. The company’s existing tools — none of which currently share data — are an overhead headache and don’t offer the insights the company needs. This existing data must be migrated to the ERP tool successfully. An ETL tool like can efficiently clean and move this data to a warehouse better than other methods.

Shoot us an email or schedule an intro call to learn how no-code can help with your next ETL project.

Steps in the ETL Process

Although it’s called ETL, there’s a lot more that goes into the process than merely extracting, transforming, and loading real-time data. There are parameters your business might need to take into account. ETL can be tedious and requires careful planning. Thankfully, uses APIs — API-based ETL solutions take all the data processing, coding, and monitoring off your plate.

Here's a list of the typical steps in the average ETL process:

  1. Identify the source. The first and most important step is knowing what data you need extracted, where it’s currently stored, and what kind of data it is — such as schema, business process, domains, legacy source, cloud data, etc., which plays an important role in ETL consideration. Keep in mind — it doesn’t matter the originating language of the raw data, whether JSON, MySQL, Amazon AWS or Redshift, Microsoft Azure, Java, Hadoop, Snowflake, or something else. ETL takes all amounts of data and its languages and transforms them into one coherent database.
  2. Build connections. Once you know the data type and its source, identify the correct data extraction connectors. This step requires customized coding or a pre-built ETL tool. A connector helps extract data flow smoothly without losing any quality. If a connector doesn’t exist for your specific dataset, email We build connectors with scalability so you don’t have to.
  3. Extract the data. After the connector is in place, the process of data extraction begins. The code continues extracting data (regardless of file format) and dumps it into a warehouse or database. At this point, though the data is being extracted and placed in a warehouse, it is still in its original form. From here, it’ll be converted, or transformed, into the format that all the data will share to standardized future processing.
  4. Clean the data. When it’s first dumped into the warehouse, all the data is unclean in the sense that it’s not standardized and it has yet to be validated. Validating and summarizing the data is called data profiling. Data profiling offers insights into the contents and informs the best way to clean the data to prepare it for transformation.
  5. Build data references. Data references are the specific parameters the data must adhere to. These references also help when transforming from the data source to the target database, but it’s a step that isn’t necessary if your ETL tool doesn’t require it.
  6. Set up logs. Logging your activities is important. Logging frameworks help you log job status, record counts, and executions. Logs help detail process behavior and unearth any data bottlenecks or errors.
  7. Data validation. After data extraction, it’s absolutely crucial to validate your data. You’re checking to ensure each data point is within your established ranges and, if not, the data gets rejected. For instance, if you’re extracting data from the past day, any data that’s older than one day gets automatically rejected.
  8. Data transformation. After data validation, data transformation begins. Transformation includes checking the data for duplicate entries — customer name and address information in your customer relationship management (CRM) and ERP tools, for example, get reduced to one sole entry for that customer — further cleansing, standardizing, application of business rules, and a solid check for data integrity, among others.
  9. Data staging. Data staging is the step in which you prepare your transformed data for the final load. Data should always be staged before your final load in your target database. Staging is a chance to ensure you retain data (as in, you can roll it back) in the event of a catastrophic error. The staging step also lets you run reports to ensure your data complies with regulations.
  10. Loading. After you’ve ensured that all data is viable at the staging level, it’s time to load that data to its target warehouse. Depending on your business’s needs, you can either overwrite this initial data when new data batches are loaded or simply append or amend data each time new data gets loaded to the warehouse.
  11. Create checkpoints. Creating checkpoints is always advised when using ETL on your datasets. Errors are commonplace when migrating data, but creating checkpoints allows you to validate smaller data groups and initiate rollback in the event of errors. Plus, if you have checkpoints set up, you can resume the loading from the point of error rather than having to start all over from the beginning.
  12. Schedule upcoming loads. Scheduling is the final yet most important step in ETL automation. Loads can be scheduled for every day, week, or month. You can even customize your load schedule — whatever fits your organization. Data that’s loaded according to a schedule can be programmed with timestamps to further assist in the event of rollbacks. Schedule and program task dependencies with care, as improperly dependencies or scheduling can lead to memory or performance problems.

The T in ETL — Transforming the Data

After the data is cleansed, it needs to be moved into the target warehouse or to a staging warehouse. The transform step of ETL helps standardize the data and structure it so it’s ready for the final destination: the target warehouse. There are two different ways you can transform data:

  • Multi-stage: As mentioned above, staging (though not necessary in all instances) is an ETL method in which data gets moved to a staging area after extraction. Transformation occurs after staging and before loading. This is true ETL.
  • In-warehouse: If there’s no staging area, this method becomes ELT as opposed to ETL — Extract, Load, Transform. After the data is extracted, it gets loaded into the target warehouse and is then transformed in-warehouse.

Let's look at the basic transformation method and an advanced transformation method.

Basic transformation

In basic transformation:

  • Standardizing of formats. Data types and lengths are standardized according to field formats for easier retrieval.
  • Data cleansing. Data cleansing occurs after standardization; values are mapped. For instance, Male might become M, or if a field is empty it might become 0, or n/a, whatever mapping values make sense for the organization. Any duplicate entries are removed.
  • Establish data constraints. Define relationships between data values.

Advanced transformation

In advanced transformation, the following steps are added:

  • Field decoding. When data is extracted from multiple tools in a business’s suite, sometimes those various tools have different naming conventions for the same types of data. Legacy or outdated systems may have entirely cryptic codes that represent different data fields. This necessitates reviewing field naming conventions and contents to remove repetitious information and to rename fields with titles more apropos of business sense.
  • Information merge. Merging related fields is common. This lets related fields be viewed as a single entry, such as product, price, type, etc.
  • Single field splits. The opposite of information merging, a single field split is when a large field of text (such as full name) is split into first, middle, and last.
  • Calculated or derived value. Sometimes, aggregations are required before the data can be loaded into the warehouse, such as figuring out total costs or profit margins.
  • Summary. Values can be summarized so that several fields can be calculated to offer a total figure. These calculations can be stored in various levels for future reporting.

Effective Loading Considerations

Extracting and transformation can go off without a hitch, but if your loading process is bogged down at any step, your results will be equally underwhelming. Some things to consider for effective loading include:

  • Remove indexes prior to loading data into the warehouse. You can always rebuild indexes later.
  • Manage your partitions by time intervals. The greatest strategy for partitioning is by year, month, or quarter. Depending on your needs, you can also partition by department or some other interval that makes sense to your unique business processes.
  • When incrementally loading, your database should sync with the source. Incremental loads are a bit more complex when compared to full loads.

So, now you understand some of the ways you can use ETL to prepare your data. But which of these methods is right for your business when it comes to data analysis? Which method will offer the greatest insight into your business’s unique data? To come to a conclusion, it’s necessary to review best ETL practices for integration.

Need help deciding the best ETL process for your organization? is one of the best ETL tools. This new ETL platform offers both ETL and reverse ETL (ELT) capabilities, with a lightning-fast change data capture (CDC) platform perfect for Ecommerce and other businesses. Reach out to schedule an intro call.

What are ETL Best Practices?

How well ETL works for your business’s data analytics relies heavily on how the process is undertaken and the data integration tools used. How efficiently your ETL process works will set you up for optimal data warehousing management — or a total breakdown of workflows. To get the most out of your ETL process:

Minimize input

What data do you need, and what data isn’t important? How many different sources are you working with? The less data you burden the ETL process with, the cleaner — and faster — the entire process will be. Strip out all unnecessary datasets in the very beginning. For instance, if you know you’ve got duplicate entries amongst your datasets, strip those out before beginning. It negates having to clean data as thoroughly later in the process.

Incrementally update

While you’re choosing your datasets, consider setting your updates to only add new information rather than sending all information through with every update. Only new datasets are sent to the ETL pipeline instead of starting from scratch every subsequent update. Incrementally updating data can be tough to implement, but it’s well worth it in the long run.

Your ETL mantra should also include:

  • Maximizing the quality of your data
  • Consider parallel processing
  • Maintain small databases
  • Always cache data
  • Establish your key performance metrics
  • Track those metrics’s ETL tool can help. In addition to offering best practices for ETL projects, we specialize in almost every integration you need — and if we don’t already, we’ll build it.

Final Tips and Tricks

It might seem like a lot to take in, which is one of the reasons having an ETL expert on your side can help you choose the best ETL method for data analysis. Establishing your KPIs is key — knowing what you want to track makes it much easier to determine the answers you need. We'll leave you with these final tips and strategies.

For effective ETL processes:

  • Include integrity constraints. These double-check for values in proper columns. Constraints are applied when foreign entries are present as new rows are loaded and the foreign entries get updated.
  • Don't try cleaning all the data. Is it best to have super clean data? Of course. But cleaning every single data set means waiting for the data to be cleansed or paying for the cleansing of the data — either way, most businesses just want to get to the point where they’re up and running with the warehouse.
  • But clean the most important data. While you shouldn’t try cleansing your entire database, not cleansing any data at all could mean the job takes much longer than the time it would have taken to at least cleanse the most important datasets. Clean data is reliable data.

The Best ETL Method for Data Analysis with

The best method for your organization relies on such factors as:

  • How much time you have for the ETL process
  • What metrics you’re tracking
  • How many tools you’re attempting to pull information from’s lightning-fast CDC platform makes subsequent loads take virtually no time at all. Let us help you discover the best ETL method for your organization. Schedule an intro call today so you can get running those reports and start analyzing your data tomorrow.