It’s 2024. In the last decade, we’ve seen manual file sorting become ETL, then ETL became ELT and ELT became dbt. But we still use spreadsheets in our boardrooms, and flat files are still filling critical business workflows.

Why is it that some of our business practices and processes leaped forward while others seemingly languished? The answer is not so simple, but it’s partly because we’ve lumped every problem into becoming a data problem solved by a dedicated data team--and we’ve promoted the idea that these data problems are all solvable with a massive data warehouse/lake (hi Snowflake!) and future tools to somehow tame that data lake (or is it now a data lakehouse?). In a future post, I’ll share my thoughts on why this is proving to be a terrible approach where cost, security, and team sanity are concerned. Today, I want to share our thoughts on one slice of data problems that really deserves their own toolset--and a good, operational name.

Let’s talk about Operational ETL!

Operational ETL Defined

With the data space as a whole being one of the hottest markets over the last couple of years, there has been an abundance of new companies founded, category fragmentation, buzzwords, and $$$ spent on marketing all of these. 

The majority of this time and money has been spent trying to progress every executive’s dream and claim of being a ‘data-driven company.’ While the end goal remains the same, getting dashboards into the hands of those who need them, the process of preparing the data for these dashboards has been where the main ‘innovation’ has occurred. This sector of the ETL market focused on creating a centralized single source of truth for reporting and analytics, can be referred to as Analytical ETL.

While companies have spent inordinate amounts of money hiring the best teams and building the most modern data stacks, I would estimate that 90% of these businesses do not see a positive ROI on their ‘become data-driven’ initiatives. The 10% who do have a clear understanding of the business problems they need data to solve, successfully narrow these initiatives down to a handful of core dashboards, and realize that they don’t need to spend millions of dollars to power these dashboards with the required data.

A part of the ETL market that’s innovation has lagged behind its more trendy Analytical ETL counterpart yet drives massive, quantifiable ROI for companies is the automating of business processes and streamlining of manual data preparation. These are business-critical processes that are often started off as employee-executed workflows, are inefficient and susceptible to human error, and always become a bottleneck as a company starts to scale.

Operational ETL focuses on this part of the ETL market that automates business workflows and simplifies manual data management, enabling companies to scale with ease.

Operational ETL Use Cases

While there are many different use cases that Operational ETL can and does cover, there are three core use cases that companies use our platform for:

  1. Preparing & Loading Data into CRM & ERP

Companies that need to prepare and load lead/customer/partner data into their CRM or ERP. The most common destinations we see being used by our customers here are Salesforce, HubSpot, and NetSuite.

  1. B2B Data Sharing

It’s 2024, and for companies, file sharing is still the most common method of sharing data both internally and externally! Typically, these are CSV/Excel/JSON/XML files that are in varying states of cleanliness and need data transformation work to clean and standardize them before being used in business processes. SFTP and FTP are the most common methods of sharing these files among teams and companies.

  1. Powering Data Products

The term Data Products is a relatively new term. While we don’t love it, given its vagueness and ambiguity, it’s a term that’s used and seems to be sticking around! Data Products are essentially any business application that uses data from a company’s data warehouse to power it. For these business applications, data freshness is critical and a requirement. These data products are typically powered with data from the data warehouse, and in order to get data from the production databases to the data warehouse, CDC-based database replication is required.

powering data products

Analytical ETL vs Operational ETL

Analytical ETL is the trendiest and most discussed part of the ETL market, where companies want to become data-driven by equipping their teams with dashboards. It's ironic that this is where the majority of data spending goes in a company, but when you ask these companies to share examples of the business impact gained from these initiatives, they struggle. This is not to say that companies don’t need dashboards; they do, but there is a major misalignment in the ROI of these initiatives, and that’s why data teams are being hit hard in recent company restructuring and layoffs.    

In the world of Analytical ETL, there are two main approaches: ETL and ELT. For the supporters of ETL, they prefer to transform their data before it is loaded into their data warehouse. On the other hand, ELT followers choose to transform their data after it has been loaded into the data warehouse, typically using tools like dbt to do the transformations.

So Analytical ETL is all about creating a single source of truth to power company decision-making, Operational ETL is all about the automating of business processes and manual data preparation in order to allow businesses to scale and teams to focus on higher-value work. 

Each of these subsets of the ETL market serves a critical but different purpose in a company, and it’s important to understand the differences as you plan your data architecture.

analytical etl vs operational etl

Why Choose For Operational ETL?

Our platform’s rich product offering does all us to support both Operational ETL and Analytical ETL use cases, but our real strengths and expertise are focused on Operational ETL. If you have Operational ETL use cases, then will be high on your evaluation list. While our overall platform features listed below are great:

  • Ease of Use

Simple yet powerful point-and-click, drag-and-drop platform. We built our platform for ultimate ease of use so that non-technical users can build and manage data pipelines without the support of their Engineering team. 

  • Time to Value

Companies get their data pipelines into production in days, not weeks or months, when using No more missed deadlines or long and drawn-out implementation times.

  • Industry-leading Support

Delivering the best customer experience is our company’s north star. Support for us is not just when you need to open a support ticket. Support is every interaction you have with us. From your very first call, you are assigned a dedicated Solution Engineer who works with you through your evaluation, onboarding, and post-implementation.

What really sets us apart is the deep level of functionality we have built out for the three core Operation ETL use cases that we see our clients use our platform for:

  1. Why For Preparing & Loading Data into CRM & ERP?

This use case is a customer favorite due to’s flexibility in data ingestion (REST APIs, files, databases, etc) and low-code data transformations before loading to Salesforce/HubSpot/NetSuite.

For Salesforce use cases in particular, we’re your tool when Dataloader is not enough, and MuleSoft is too much.

Use case-specific advantages include:

  • Bi-directional connectors for Salesforce, HubSpot, and NetSuite

  • Ingest data from anywhere

    • REST APIs, SOAP APIs, Databases, SaaS Apps, Files, Data Warehouses, anywhere!

  • Orchestration layer for job execution logic

    • For example:

      • Pipeline 1 must be completed before Pipeline 2 can start

      • Execute this SQL statement before pipeline execution

  • Advanced Salesforce functionality:

    • Bulk (1.0 and 2.0) and SOAP API connectors

    • Load to all your objects regardless if they’re standard or custom

    • Choose data loading type: Insert/Upsert/Update/Delete/Hard Delete

    • Customize batch size

    • Define the maximum number of errors allowed

    • Output errors to error log files

  1. Why for B2B Data Sharing?’s robust file handling and data transformation capabilities make this use case one of our most popular among clients. Regardless of how exotic your files and transformation requirements are, we’ve likely handled more! 

Use case-specific advantages include:

  • Bi-directional connector for SFTP

  • Support for all file types: Excel, CSV, Text, XML, BAI - you name it, we support it!

  • File data ingestion for ingesting and preparing data in files

  • File mover option for file transfer without any data transformations

  • Destination format - Delimited / Line Delimited JSON / Parquet

  • Compress output file as needed - None/Gzip/Bzip2

  • Customize destination file names

  • Customize destination action based on file and directory states

  • Merge output to a single file

  1. Why For Powering Data Products?’s 60-second CDC data replication gives companies the data freshness and reliability they need to power their data applications. 

Use case-specific advantages include:

  • Fastest (60-second) data replication on the market

  • CDC-based replication to ensure no performance impact to your database

  • Guaranteed pipeline uptime - your data products need reliable data. We actively monitor your pipelines and resolve or alert to any issues we see

  • No lag data replication, regardless of data volumes

  • Instant API generation for exposing the data from your data warehouse

Doesn’t Do Analytical ETL, too?

Yes, we can (and do!) handle Analytical ETL use cases for companies, but there are specific Analytical ETL use cases where we are a great fit and others where different platforms in our space are a better fit.’s Strong Fit Analytical Use Cases

  • Companies that wish to do data transformations in a low-code manner before loading the data to their data warehouse

  • Companies that have large amounts of REST APIs or files that they would like to get into their data warehouse

  • Companies that need 60-second CDC database replication to their data warehouse’s Not-So-Strong Fit Analytical Use Cases

  • Companies that are looking for a platform with a large number of out-of-the-box pre-built SaaS connectors. We have the Tier 1 native connectors, but we don’t focus on Tier 2 or 3 longer-tail connectors

  • Companies that are looking to do their data transformations in their data warehouse using something like dbt. For our replication, we focus on database replication, so if you need to replicate a lot of other sources and then do your transformations in your data warehouse, we’re not a fit.


I hope this article has been helpful for you in understanding what Operational ETL is, the differences between Operational ETL and Analytical ETL, and where fits in. If your use case includes any of Salesforce, files, REST APIs, or real-time data replication, there’s a high chance that we’ll be a good fit for you!

If you’d like to discuss your use case in more detail, schedule a time with one of our Solution Engineers. Once they have an understanding of your requirements, they can let you know if we would be a good fit, and if we’re not, then point you in the right direction for what might be a better fit.