• ETL platforms extract, transform, and load data from a source to a destination.
  • The ETL process can pull information from multiple databases.
  • The ETL process can search databases to find specific types of information.
  • Transforming data often includes formatting, duplicating, integrating, splitting, and other actions that make information more useful.
  • The final step of the ETL process can include batch incremental loading, small-batch incremental loading, or streaming incremental loading.

The ETL process involves moving data from a source, transforming the data in some way, and loading the information to the same or a different source. You may feel a little confused the first time you encounter an ETL process. With the right platform, though, you can adjust quickly and learn how to manipulate data to make it more valuable.

Use these links to navigate the guide:

  1. What Is ETL?
  2. Why Do You Need ETL?
  3. ETL Process in Data Warehouses
  4. ETL Tools
  5. ETL Best Practices

What Is ETL?

ETL stands for “Extract, Transform, and Load.” The acronym applies to platforms that can take data from one or more sources, transform the data into different formats, and load the processed data to one or more sources.

For a more detailed explanation, visit the sections below in ETL Process in Data Warehouses. They explain how ETL platforms perform each step.

Why Do You Need ETL?

Data has become essential for business success and decision-making. Data’s growing importance means that many types of organizations use ETL. Some uses within a variety of industries include:

  • Marketing companies that want to extract data from multiple client relationship management (CRM) solutions, transform all of the data into one format and load the formatted data into business intelligence applications that can reveal trends in consumer behaviors.
  • Hospitals that want to extract patient data from legacy systems, transform the data into a format that the new system recognizes, and load the reformatted patient data into a single system that helps healthcare professionals make decisions that improve health outcomes.
  • Online retailers that serve customers through websites, apps, and other tools can pull data from all points of sale, transform the data into a standard format, and load the transformed data into applications that help them forecast demand and minimize churn.
  • Federal agencies that want to pull statistics from hundreds of local databases, separate the data into specific categories, and load to a national database so authorities can get an overhead view of common challenges in communities across the country.

The ETL process saves time and enhances data. Any time someone needs to move, categorize, or standardize data, they could benefit from an ETL solution.

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ETL Process in Data Warehouses

Data warehouses can hold information from multiple data sources. Organizations use data warehouses because they want to store, aggregate, and process information that they can use in conjunction with business intelligence tools.

Popular data warehouse platforms include:

Some organizations use more than one data warehouse or partner with organizations that use different data warehouse services. Even when using a single data warehouse, organizations often need the ETL process because they want to:

  • Locate specific types of data.
  • Separate data into a variety of categories.
  • Transform multiple data formats into a common format.
  • Add data to data analysis tools that use algorithms and artificial intelligence to identify trends.
  • Load data to visualization tools that turn enormous amounts of information into graphs that most people can understand.

Regardless of what an organization wants to do with its data warehouse, the ETL process takes three steps to help them reach their goals.

Step 1 – Extract

The extract step lets an ETL platform pull data from a source or multiple sources. Sophisticated ETL platforms can target specific types of data to extract from sources. Xplenty, for example, can identify data in non-relational NoSQL databases like MongoDB as well as relational SQL databases like Amazon RDS.

Step 2 – Transform

Xplenty gives you no-code and low-code options for transforming data before loading it to a destination. When building a data pipeline that connects the extraction source and the load destination, you can use transformations like:

  • Duplication that identifies and deletes duplicate data.
  • Format revision that reformats data into a consistent format.
  • Cleansing that deletes old and incomplete data that doesn’t add value to the data set.
  • Joining that combines data from more than one source.
  • Splitting that divides a column into multiple columns.
  • Integration that standardizes data elements throughout the data warehouse.
  • Validation that lets users create unique rules for the ETL to follow when it encounters specific instances, such as reporting an alert when it encounters a blank row.

Transformation is arguably the most important part of the ETL process because it makes changes to data before the final step, loading the processed data to a target destination.

Step 3 – Load

Data loading is the process of moving data from the ETL platform to a destination or multiple destinations. Data loading usually happens in batch increment or streaming incremental loads.

  • Batch incremental loading moves data to the target repository in a batch or batches. Many organizations use batch incremental loading outside of peak hours. Batch incremental loading can take several minutes or hours, depending on how much data gets moved. Letting the ETL platform work during off-peak hours helps prevent system overloads that slow down other processes.
  • Small batch incremental loading works slightly differently. Instead of trying to load all of the data in a single batch, it breaks the transformed data into sections and loads then minute by minute. This approach creates a smaller burden on the system and behaves much like real-time updates.
  • Streaming incremental loading moves processed data in real-time. When new data gets processed, the ETL immediately sends it to the target repository. Some organizations prefer streaming incremental loading because they want access to real-time data. Streaming incremental loading, however, can only move tiny amounts of data at a time. In real-world situations, it doesn’t look very different from small-batch incremental loading.

ETL Tools

While all ETL tools perform the same basic functions of extracting, transforming, and loading data, individual ETL solutions have unique pros and cons. Organizations should consider the differences carefully to make sure they invest in a platform that matches their needs.

Unfortunately, comparing ETL tools can take quite a bit of time. Learn about the overall advantages and disadvantages so you can focus on the options most likely to work well for your organization.

Xplenty

G2 user rating: 4.4 out of 5 stars

Xplenty’s cloud-based ETL data integration platform gives you a straightforward way to combine, transform, and load information from multiple data sources. The ETL process uses a low-code and no-code environment with a short learning curve. Even new users can learn how to build an effective ETL process within minutes.

Xplenty integrates with over 100 databases, cloud services, BI tools, advertising platforms, and analytics software options. You can use it to quickly move data from databases, including Snowflake, MongoDB, Google BigQuery, and AWS.

With Xplenty, your ETL process also benefits from Field Level Encryption and features that comply with regulations established by HIPPA, GDPR, and CCPA.

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AWS Glue

G2 user rating: 4.1 out of 5 stars

AWS Glue often works well for organizations that rely on Amazon data warehouses and other services in the Amazon ecosystem. AWS Glue can automatically generate scripts in Python and Scala, which helps people without tech backgrounds to create efficient ETL processes. The application also includes a scheduler that lets you establish trigger events that will prompt an ETL process.

Unfortunately, even the automatically generated scripts don’t make AWS Glue simple for people without ETL and database experience. Many users complain that it has a steep learning curve that makes it impractical for marketers and other non-tech professionals.

Talend Data Integration

G2 user rating: 4.0 out of 5 stars

Talend is an open-source ETL with enterprise features, excellent features for data governance, and an on-premises hosting option. Building an ETL process in Talend will take quite a bit of technical experience. The interface feels awkward and outdated to most users. Unless you know database programming languages like SQL, PHP, Swift, and Python, you will face an unfamiliar environment that makes it nearly impossible to move or transform data.

Stitch

G2 user rating: 4.7 out of 5 stars

Stitch suits casual users and database professionals. The open-source platform benefits from features like automated data pipelines and self-service ELT. Most people find it relatively easy to create ETL processes in Stitch.

While Stitch excels in many areas, it doesn’t allow arbitrary data transformations. Instead of building an ETL process with simple data pipeline components, users should add transformations on top of raw data inside of warehouses. Stitch works well, but this offbeat characteristic will throw some people off.

Alooma

G2 user rating: 4.0 out of 5 stars

Alooma can automate much of the ETL process needed to build efficient pipelines that extract, transform, and load data. The automation helps inexperienced users get the results they need without a lot of training. It doesn’t, however, offer code-free data transformations.

Historically, people like Alooma. Unfortunately, things changed when Google acquired the company in 2019. Now, only Google Cloud Platform users can sign up for Alooma. If you want to use databases other than Google BigQuery, you’re out of luck.

ETL Best Practices

ETL best practices can vary somewhat depending on the ETL platform that you choose. Generally, though, you should use the following practices to optimize results, save time, and avoid errors.

1) Know Your Data

Not all data is equal. Know where your data comes from before you add it to your ETL process. Ideally, you should know the data’s lineage, including:

  • Any previous transformations and formats.
  • What database it's stored in.
  • Where the data originated.
  • Whether other data from the set is missing.

The more you know about your data’s history, the more confidence you can have in it.

2) Log Data Within the ETL Pipeline

You may not discover errors until your transformed data reaches its destination. Logging within the ETL process can help you audit data after it gets loaded. Without a log, you will likely waste a lot of time redoing work or tracking down problems.

3) Make ETL Processes as Small as Possible to Get the Correct Results

ETL processes should include as few steps as possible. When you review an ETL pipeline, look for redundancies and unnecessary transformations. If a step doesn’t help you get the results you need, remove it. By keeping ETL processes small, you make them more manageable. When you need to troubleshoot an issue, you won’t have as many layers to comb through.

4) Build Reusable ETL Processes

You probably use similar ETL pipelines often. For example, you may pull data from a handful of hospital databases, organize them by patient outcomes, and load them to a different database. Don’t create unnecessary work by rebuilding pipelines that you can use daily, weekly, or monthly.

Also, consider the possibility that you can add a few steps to existing ETL pipelines. You don’t necessarily have to build a new process just because you need to include a new database.

5) Restrict User Access to Data

Corrupted data can ruin hours of work. Restricting user access to data is one of the best ways to maintain data integrity. If someone doesn’t need access to the information, their account should not have the privilege to view it. Controlling access improves data quality and security.

Set Up Your ETL Process with Xplenty

Xplenty makes it easy for you to set up your ETL process. You don’t need experience writing code to build an effective ETL process. The low-code and no-code environment lets you choose data sources, transformations, and load destinations easily. Creating unique transformations only requires basic coding knowledge.

Xplenty also makes it easy to follow ETL best practices. The platform takes a visual approach that shows you your data sources and targets. When mistakes get made, the software will post an alert that makes it simple for you to correct problems. You can even create and save efficient ETL pipelines to reuse over and over.

Schedule a call with Xplenty to discuss your needs and requirements. A representative will help you determine whether the Xplenty platform has the right features for your ETL process. You can also get a free trial to see how no-code data pipelines can help you move and transform the data that matters most to you.