ETL Or ELT: Build vs Buy

Updated November 2026. This was first published on 22 November 2022. The idea is the same. Any number below is what the original piece said then. It is not a new count.

Key takeaways

The significance of ETL in an company is directly proportional to how much they rely on data warehousing.
The ETL tools collect, read, and migrate vast volumes of raw data from multiple data sources and across different platforms.
They load that data into a single database, data warehouse, or data store for easy access.

The significance of ETL in an company is directly proportional to how much they rely on data warehousing. The ETL tools collect, read, and migrate vast volumes of raw data from multiple data sources and across different platforms. They load that data into a single database, data warehouse, or data store for easy access. They process the data to make it meaningful with operations such as sorting, filtering, joining, merging, reformation, and aggregation. Ultimately, they have graphical interfaces for quick and easier results compared to the traditional methods of moving data through hand-coded data pipelines.

This tool helps break down the data silos making it easier for a data scientist or engineer to access and analyze data and turn them into business intelligence (BI). These tools are the first essential step in the data warehousing process that helps you make more informed decisions in a quick time.

ETL tools are crucial for a data-driven business that helps extract and transform data from disparate sources before loading them into the main data repository.

Need For ETL Tools #

ETL tools can help your business grow in the following ways.

1. Time efficiency #

This tool allows you to collect, transform, and consolidate data in an automated way, which eventually helps to save plenty of time and effort, that’s otherwise spent on importing data manually.

2. Reduced error probability #

You are prone to making errors when handling data manually, even if you are careful with it. Moreover, a slight error in the early stages of the data processing can be dicey, as one error leads to another one, and the cycle continues. For instance, if you enter sales data incorrectly, your entire calculations can go wrong. ETL tools help automate several parts of a data process, and reduce manual intervention and error probability.

3. Easily handle complex data #

Over time, your business will have to work with a vast data volume that is diverse and intricate. For example, you can be a multi-national company with data coming in from three different countries with distinct product names, addresses, customer IDs, etc. So, if you have to manage a range of attributes, you may end up formatting data all day long. This is where an ETL tool can help to streamline things for you by simplifying data cleaning and processing.

4. Improves business intelligence and return on investment (ROI) #

ETL tool helps ensure that the data for analysis is of the finest quality. So, you can use this high-quality data to make better decisions and increase your ROI.

ETL software helps enterprises to get significant insights that support their business development with data from numerous sources in a practical arrangement. It helps to streamline and improve the process of blending raw data distributed across several systems into a data repository. So, selecting the right ETL plays a crucial role in your BI.

Understanding ELT & ETL #

ETL (Extract, Transform, Load) has been the traditional approach for data warehousing and analytics for the past couple of decades. The ELT (Extract, Load, Transform) approach changes the old paradigm as the transform and load are being switched.

Both the ETL and ELT solve the same need as they help to clean and manage the raw data by readying them to be analyzed. Billions of data and events are needed to be collected, processed, and analyzed by businesses by enriching, moulding, and transforming them to make them meaningful.

The working methodologies of both these tools are different, leading to new possibilities in many modern data projects. These differences include how raw data is managed when processing is done and how analysis is performed.

Let us discuss the technological differences between ETL and ELT, showing data engineering and analysis in these approaches of the three stages: E, T, L.

1. Extraction #

It involves retrieving raw data from an unstructured data pool and migrating it into a temporary, staging data repository.

2. Transformation #

This stage includes converting, structuring, and enriching the raw data to match the target source.

It involves loading the structured data into a data warehouse to be analyzed and used by BI tools.

ETL vs. ELT #

ETL needs raw data management, including extracting the needed information and running the right transformations to eventually serve the needs of the business. All three stages of E, T, and L needs interaction by data engineers and developers and deal with the capacity limitations of traditional data warehouses.

Using ETL tools, data analysts and other BI users have become accustomed to waiting because of the non-availability of simple access to the information until the completion of the whole process.

The transformation time for ETL increases as the data size grows. It uses the staging area and system, which requires more time to load the data. Since the ETL is based on multiple scripts for creating the views, so deleting it means deleting the data. It requires high maintenance as the data must be chosen before transformation and loading. Also, in case if it got deleted or you want to enhance the main data repository, you need to do it all again.

ETL needs less space at the early stage, and the result is clean. It is used primarily by IT and has fixed tables and timelines. ETL includes presuming and choosing the data a priori. It uses a prevalent legal model for the on-premises, relational, structured data. It is not a part of the data lake support approach. ETL is less cost-effective for SMEs (Small and Medium-sized Enterprises) and businesses, especially as the data size increases over the years.

Whereas, in the ELT approach, once you’ve extracted the data, you will immediately start with the loading phase that involves moving all the data sources into a single, centralized data repository. With modern infrastructure technologies using the cloud, now the systems can support huge storage and scalable computing. So, a large, expanding data pool and fast processing is virtually endless to maintain all the extracted raw data.

The ELT approach is a modern alternative to ETL. The ELT process is still evolving because the frameworks and tools to support it are not always fully developed to facilitate the load and processing of large volumes of data. But, it is quite promising as it enables unlimited access to all your data at any time and saves the efforts and time of developers for BI users and analysts.

ELT is an all-in-one system, so the time and speed of transformation are not dependent on the data size, and it will load the data only once. As it creates ad hoc or temporary views, so the cost of building and maintaining them is quite low. Also, ELT is low maintenance since the data is always available.

ELT needs an in-depth knowledge of the tools and the expert design of the main data repository. It is used by everyone, including developers and citizen integrators, as it is ad hoc, agile, and flexible. It is by HW (none) and data retention policy. ELT is customized to use in scalable cloud infrastructure that supports structured and unstructured big data sources. It enables using the data lake approach with support for the unstructured data. ELT is scalable and available for all company sizes that use online SaaS.

ETL Functions, Benefits, and Limitations #

ETL is outdated as it helped to cope with the limitation of the traditional rigid data center infrastructures. is no longer a barrier today with the cloud. For enterprises with large data sets of even a few terabytes, load time can take hours, depending on the complexity of the transformation rules.

ELT is a vital part of modern data warehousing. Using this approach, business companies of any size can capitalize on the current technologies. By analyzing larger pools of data with more agility and less maintenance, companies can gain key insights to create a real competitive advantage while excelling in their business.

Now that you know both the ETL and ELT tools, which one do you need and why. Also, whether you need to build your own tool or buy it. Here we’ll explore why buying tools might work best for some while hand-coding the ETL layer may work better for others. But before diving into that, here’s a quick overview of the function, benefits, and limitations of the ETL process.

The ETL process describes the infrastructure built to pull or extract the raw data from a source for analysis, transform it into some useful form for a business need, and deliver or load it to a data warehouse. This automated process is an integral part of the data pipeline of any business or company.

A traditional way to implement ETL is to process the data in batches once every 24 hours. Batch processing involves stopping at some point to store and collect the data while the system ‘forklifts’ the data over to your data warehouse. The loaded and transformed data is then available for BI tools to provide the insights and visualization layer to the enterprise. Powerful software frameworks like Hadoop MapReduce or Stitch have been designed for batch processing of huge datasets.

A more modern approach is to have ETL pipeline perform a stream of processing of data, especially where the real-time analytics are crucial, latency matters, and extracting and transforming large batches of data in bulk may be counterintuitive for the business needs of an company. The stream processing tools, such as Apache’s Spark of Kafka, process data in-memory, greatly reduces the time it takes for the data to become actionable.’.

Though the batch processing lets the data build up, stream processing renders the data more steadily accessible by spreading the processing over time. The batch processing involves moving data in bulk, validating, and cleaning the data, and then staging it in a database before loading it to the target tables within the data warehouse.

Whereas, steam processing involves moving data at speed, and should be viewed as preferable or mandatory when the caliber of insight and value possible from touching data immediately reduces as the time component increases.

Building the tool #

When deciding whether to build or buy, start at the beginning. This may sound counterintuitive and include scenarios where an company realizes it is way past the starting line and galloping along an inefficient trajectory concerning data.

1. Form a schematic defining the scope #

Putting a plan together helps to describe your project parameters and also understand your business needs. The top priority of your list involves making your data make sense and also count on clean and useful data delivery which is ready for them to derive insights.

To start building this plan, start with a few straightforward questions, such as.

• What is your top business needs to be addressed?

• What business needs do you want to address, but haven’t been able to do so?

• What data jobs would you like to incorporate, but didn’t think it is possible or viable?

• How much money will be saved hand-coding versus buying a tool and for how long?

• If you are building your own ETL, how much time will you need to correctly develop and test it?

• Will these pipelines scale as the company grows?

• If you are using hand-coding, how responsive will the in-house team be to a failure?

• Can you commit to long-term maintenance, not just a one-time investment in the setup?

• Conversely, if you are using outside tools for your ETL, how agile are those tools for your company’s current and changing needs?

Each company has different requirements, yet none of them will benefit from an unnecessarily sophisticated or poorly structured pipeline as it will underperform.

2. Describe the project guidelines for the desired ETL system #

Describing the project guideline by documenting everything concisely will help those who rely on it such as the DBAs, the IT team with easy reference. Whether you decide to hand-code the ETL or buy a tool, successful installation of data integration means everything is configured effectively. Further, it means that you have completed testing with representative data sources, including the intentional introduction of corrupted data at certain iterations to ensure that the system is functioning as desired.

Whenever possible, the tests should be automated and a support system should be in place, with any training available as needed. As each step is executed in constructing and implementing your company’s preferred ETL method, ensure that the initial questions are answered and that the acceptance testing is treated as though it had real-world significance. Adopt proactive measures such as a regular audit and profiling target tables of the system as a whole to avoid any potential data quality issues.

With the build scenario, you know the metrics that matter, have control over your data pipeline and how you want everything configured.

**1) Building a hand-coding system offer a level of flexibility that a tool can’t provide* #

Using hand-coding, you can build anything you want. Whereas, a tool-based approach may be limited for its available features. You can tailor a hand-coded system to directly manage any metadata and this increased flexibility might be desirable depending on your company’s needs.

• Before deciding to build, it is vital to ask yourself a few key questions.

• Is there a dedicated in-house data team for writing scripts and managing the workflows?

• If there is a single data scientist with no team, for whom this is their sole focus, is the task of hand-coding ETL relegated to someone’s second job?

• Can the ETL team handle the maintenance tasks such as documentation, testing, and optimization as well as a setup?

Even with the availability of the coding chops to build the pipeline, you may lack enough time or bandwidth to do the job properly for the future.

2) Total control, visibility, and responsibility #

Does the control of running data jobs whenever and wherever you determine to outperform the use of tools you could buy? Does a custom data integration solution render any present part of the ETL tool unnecessary overhead that needs trimming from the productivity costs? If so, then the hand-coding looks extremely viable. But, “With great power comes greater responsibility.”.

• So, how are you positioned to handle things when data fails to load, or loads only partially, with that failure potentially not even in the ETL layer but somewhere within the raw source data?

• Are the necessary checks built-in it swiftly handles the situation?

• Will the new data sources or connection changes be integrated smooth?

It is significant to understand that building the data pipelines means figuring out how to manage all the issues with the internal resources.

3) Cost-effectiveness #

If there are budgetary concerns, and your current data pipeline needs do not need everything offered by an ETL tool, then building a solution may be advantageous.

But, along with budgeting for maintenance, it is imperative that the ETL testing is done seriously. You should place checks throughout your ETL, with regular monitoring and troubleshooting to address any data quality issues head-on and ensure optimal performance.

Also, remember that the investment in the tech infrastructure is required to build your system, including the direct costs to keep its operation. Will the money saved now with hand-coding continuously pay off in the long run?

So, if the type of data integration needs for your company is highly unique, building your ETL may the best and cheaper option.

It has scalability limitations as your business may grow further and have a large volume of data that needs to be analyzed for BI. Your company may not have a compatible and dedicated team of engineers who meets the demand for constant maintenance, documentation, and testing of the data integration tool.

Buying the tool #

Your enterprise wants the most strong data pipeline and when it comes to data visualization you are determined to create an environment that allows for ownership and autonomy. But, you don’t have the desire, time, or expertise to manage your own pipeline. This is where a pre-built solution could be the best fit for your company.

1) You do not have to build anything #

The tool vendor has put their energy and best people to build an ETL tool that they hope your company can’t live without. As the vendor focuses to build an ETL tool, you and your company can now focus on other business problems.

Another consideration to buy the tool is that as teams change and people move on, the vendor from whom you have purchased the tool will have everything about your ETL documented clearly. Also, your data scientists and engineers have ensured that tomorrow’s developers have everything they required for the future. Using ETL tools means not wasting time over documentation, eliminating a potential, and counterproductive lack of clarity in case, new hires are responsible for a customized data integration solution not built by them.

2) Completely justified price #

Even if you and your data team are comfortable writing scripts, is it the most efficient use of the company time?

Though building an ETL tool is simple and faster with the initial outlay of funds than making up for itself for large or complex projects in the future, its true potential may only be revealed after you start working with it. The speed of insight depends on the velocity towards its accessibility.

So, using a tool relieves your responsibility for some aspects of data security and data integrity, with a data lineage documentation auto-generated from a metadata repository.

3) ELT is scalable and data flow is managed #

Using a tool that facilitated teamwork, makes it easier to share and transfer knowledge throughout an enterprise. Managed automation transforms multiple data flows into an actionable visualization layer that allows the end-users to query data without having to understand how any of it works.

Also, when opting to buy, any professional tool should have no problem scaling up as your company grows. Also, handing additional data sources and increased data volumes without compromising performance can be achieved by purchasing the tool.

Other advantages include reduced potential errors while using the tool, which helps shorten the development cycle. It is user-friendly with smart features such as drag-and-drop. But, it may need regular updates and their out-of-the-box features can sometimes restrict them.

Conclusion #

When it comes to data integration, defining the unique business needs of an enterprise or company helps to bring the focus on the decision of build vs. There is convenience in buying as you have it assembled for you by the pros who know what they are doing compared to the control inherent in building it yourself. includes the native knowledge of how everything works, as you are the one who oversaw the construction.

Eventually, you should choose an ETL process based on your end goal, which is getting data into a state where users can derive insights. Having this in place is a critical step for your company’s growth based on business intelligence.

Your company can benefit from replacing ETL with ELT as it is intended for analysts, data engineers, and data scientists whose work directly involves or depends on the data pipelines.

If ELT simplified data engineering, why should you buy a tool instead of building one from scratch to perfectly suit your needs? The reason is the time needed for building the tool from scratch would demand 45 person-weeks. would cost a whole year’s salary of a full-time data engineer and its downtime related to maintenance. Also, the data engineer is unlikely to be an expert in the idiosyncrasies of each specific data source. Some APIs may be extremely complex, poorly documented, or ignore best practices.

You should also consider the effect of such work on the morale of analysts and data scientists as the data pipelines are considered tedious and thankless to build but end up among people who would rather do other things. So, buying the tool can help to solve all these problems with a standardized solution that performs its job with minimal supervision. Buying the tool helps you manage the processing requirements better, save time, and increase developer efficiency.

Diyotta is an ELT tool that provides effortless data integration solutions for analytics teams. It has code-free data integration that everyone can use. This tool allows you to have all your data in one reliable place and in real-time at an unbeatable price and customer service. It can be deployed in minutes, is easy to use with no code or engineering required, and is flexible to connect all your data at an affordable cost.

Questions and answers #

What is the main point? #

The significance of ETL in an company is directly proportional to how much they rely on data warehousing.

What else did the original piece say? #

The ETL tools collect, read, and migrate vast volumes of raw data from multiple data sources and across different platforms.