Can ELT/ETL Be a DIY Project? — Build Vs. Buy
To find out more about whether ETL or ELT is best for refer to the complete ebook available for download at https://start.diyotta.com/dont-get-fired
Data warehousing and ETL go hand-in-hand. In fact, the importance of ETL in an organization 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. The data is loaded into a single database (data warehouse or data store) for easier access, where it is processed through sorting, filtering, joining, merging, reformation, and aggregation. Ultimately, they have graphical interfaces for quick and easier results than traditional moving data methods 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 quickly makes informed decisions.
Need for ELT or ETL tools:
ELT or ETL tools can help your business grow in the following ways:
ETL vs. ELT
Over the last couple of decades, ETL (Extract, Transform, Load) has been the traditional approach for data warehousing and analytics. The ELT (Extract, Load, Transform) approach changes the old paradigm as the transform and load are being switched.
Both the tools are used to extract raw data from different sources, transform them, and load them into the target system. Both these tools’ working methodologies 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.
So we know what an ETL and ELT tool is, which one you need, and why. It’s time to investigate now whether to outsource the required tool or build it from scratch?
Building the tool
With the build scenario, you know the metrics that matter, have control over your data pipeline and how you want everything configured. If you are leaning toward this solution, you should consider the following finer points:
- Build what you want:
Building a hand-coding system offers a level of flexibility that a tool can’t provide. Using hand-coding, you can build anything you want. At the same time, a tool-based approach may be limited for its available features. You can tailor a hand-coded system to manage any metadata directly, and this increased flexibility might be desirable depending on your organization’s needs. But before deciding to build, it is vital to ponder over a few points:
- 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?
- Can the ETL team handle the maintenance tasks such as documentation, testing, optimization, and setup?
It’s, however, important to note that depending on the complexity of data, one might suffer from time management and efficiency in the long run.
2. Total control, visibility, and responsibility
Having control is fantastic. Does the control of running data jobs whenever and wherever you determine to outperform the use of tools you could buy? Hand-coding looks exceptionally viable if a custom data integration solution executes any present part of the ETL tool and trims the productivity costs? But remember, “With great power comes great responsibility.”
- So, how are you positioned to handle things when data fails to load, or only load 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 seamlessly?
It is significant to realize that committing to building the data pipelines means figuring out how to manage all those messes with the internal resources, even when inconvenient.
- Cost-effectiveness
If there are budgetary concerns and your current data pipeline needs, do not need everything offered by an ETL tool, building a solution may be worthwhile. However, along with budgeting for maintenance, it is necessary that the ETL testing is done thoughtfully. It would be best if you placed checks throughout your ETL, with regular monitoring and troubleshooting to address any data quality issues head-on and ensure optimal performance.
Remember that the tech infrastructure investment 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?
If your organization’s data integration needs are highly unique, building your ETL may be the best and cheaper option. Nevertheless, it may not cover all your needs. 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 robust data pipeline, and when it comes to data visualization, you are determined to create an environment that allows for ownership and autonomy. However, 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.
- Get a tool built as per your need:
The tool providers invest the best of their resources to build an ETL tool that they hope your organization can’t live without. As the vendor focuses on building an ETL tool, you and your organization 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. Your data scientists and engineers have also ensured that tomorrow’s developers have everything they required for the future. Using ETL tools means not wasting time over documentation, eliminating potential and counterproductive lack of clarity if new hires are responsible for a customized data integration solution not built by them.
2. Completely justified cost
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. Data flow is managed and scalable
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. It 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 organization 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. However, it may need regular updates, and their out-of-the-box features can sometimes restrict them.
Decision Parameters
Before deciding whether to build or buy, never forget to run your option against the following parameters:
- How much time do you have?
- What is the priority business needs to be addressed?
- What kind of data jobs do you want to run?
- Which option will be more cost-efficient, and for how long?
- If you are building your tool, how much time will you need to develop and test it correctly?
- 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?
- Which one is more feasible — long-term maintenance or a one-time investment in the setup?
- Conversely, if you are using outside tools for your ETL/ELT, how agile are those tools for your company’s current and changing needs?
Conclusion
When it comes to data integration, defining an enterprise’s unique business needs helps to focus on building vs. buying. 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, which includes the native knowledge of how everything works, as you are the one who oversees the construction.
Eventually, you should choose an ETL/ELT process based on your end goal, i.e., getting data into an analysis-ready format to derive insights. Whether you are looking to make things easier for your analysts or creating a culture of data democratization, having the right tool in place is a massive step in the right direction for your company’s growth based on business intelligence. Refer to the most comprehensive guide to choosing the right tool to help you make a better decision.
Your organization 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 simplifies data engineering, why should you buy a tool instead of building one from scratch to perfectly suit your needs? The simple answer is the time needed for building the tool from scratch would demand 45 person-weeks that would cost a whole year salary of a full-time data engineer and its downtime related to maintenance. Additionally, the data engineer is unlikely to be an expert in each specific data source’s peculiarities. Some APIs may be extremely complex, poorly documented, or ignore best practices. Hence, building limits you!
It would help if you also considered the effect of such work on analysts’ and data scientists’ morale 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. Purchasing the tool allows you to manage the processing requirements better, save more time, and increase developer efficiency.
Download the ebook: https://start.diyotta.com/dont-get-fired.
