Extract, Transform, Load technology sits between your data source and its destination in your data stack. It’s a useful way of delivering data from multiple applications, databases, and other sources to your CRM, data lake, or data warehouse for analysis and use. But how do you know that it’s time to add ETL to your organization’s data stack? This article covers data integration issues that ETL solves, the benefits of adding ETL, how this process works, and the types of ETL solutions available.
Table of Contents
- Problems with Data Integration in Data Stacks
- How You Know When to Make a Change
- The Change: Adding an ETL Tool to Your Data Stack
- ETL Options for Your Data Stack
- Your Best Option for ETL
Problems with Data Integration in Data Stacks
Your application data typically resides in online transactional processing, or OLTP, databases. These types of databases are not designed for data analysis, so your organization may struggle to understand your data and gain insights into your operations. Online analytical processing, or OLAP, data warehouses are optimized for analysis, but you can’t simply take data from business applications and drop it into your warehouses as-is. Data analysts and scientists are unable to get a full picture of the available data in this environment, which can impact business goals, decisions, and reporting. Data engineers may struggle with creating the right algorithms to surface actionable insights.
How You Know When to Make a Change
Manually processing and formatting data takes up a lot of resources and isn’t scalable to accommodate the sheer volume of data many companies deal with. If you can’t keep up with data ingestion and transformation, then you’re not able to use those insights to make better business decisions. This type of inefficient process impacts productivity and could lead to key data sources being overlooked.
If your data team is having a hard time using your organization's data and opportunities are being lost because of it, it's time to make a change to your data stack.
The Change: Adding an ETL Tool to Your Data Stack
ETL sits in the middle of your data sources and OLAP data warehouses and automatically takes the data through a process that prepares it for analysis. During the ingestion and transformation stages, you gain additional benefits for your data stack.
How ETL Technology in Your Data Stack Solves Your Problems
- Speeding up data reading and analysis: Your data flows more quickly through the pipeline, so you can more quickly access the insights and information from the sources.
- Standardizing data in your stack: How many types of data formats and structures do you deal with in your organization? When everyone is using their own options, it becomes difficult to eliminate data silos and encourage collaboration, and increases the difficulty of manually converting this information. The ETL tool automates this entire process based on your criteria, so you can quickly change data into a usable form.
- Cleaning data before it’s analyzed: Data quality issues have an impact at all levels of your organization. If information is inaccurate, outdated, incomplete, or duplicated, data-driven decision making is compromised. Your organization could make any number of bad moves that lead to lower revenue, insufficient cash flow, product stock issues, customer experience complaints, and more. You can clean data as part of the ETL process, so poor quality data never reaches the data warehouse.
- Reach compliance by masking or removing sensitive data: Avoid regulatory fines, reputation damage, and other issues that may arise from personally identifiable information and sensitive data being moved through a data pipeline without proper protection. ETL can mask this data or remove it from the data set entirely as needed. When regulatory rules change, you just need to update your data pipeline to adjust to the new rules.
- Scalable data processing: Since so much of the ETL process is automated based on data pipelines you configure, it’s capable of scaling with your data stack’s changing needs.
- Combining data sets: When you ingest data with ETL tools, you can combine multiple data sources into a data set for better structuring and analysis.
How ETL Works
The ETL process takes data through three steps:
- Extract: Your ETL tool extracts data from one or more sources, such as your business applications, relational databases, and CRMs. The data moves to a staging area for processing.
- Transform: The data gets transformed based on your requirements, which can include cleaning duplicate records, data formatting, and data integration.
- Load: This prepared data is sent to your data warehouse or data lake to power your analytics and Business Intelligence tools.
ETL Options for Your Data Stack
Your data team can create a hand-coded ETL solution by leveraging SQL, R, and Python, but it could take months before you see results for your data stack. Thankfully, ETL tools are available, so you don’t need to commit to a resource-intensive IT project. These solutions come in many forms, from dedicated ETL tools to end-to-end data integration platforms.
The right choice for your organization depends on your data sources, transformation requirements, IT budget, the solutions already in place, if you need support for business users, and whether you are working with cloud or hybrid environments.
Cloud-based ETL tools reduce your technical debt by offloading the maintenance and operations of the underlying ETL technology to the service provider. If you have a particularly narrow use case or a limited set of sources, look for solutions that have built-in integration out of the box. On the other hand, if you don’t have any data integration tools deployed in your organization, you may opt for one of the broad end-to-end platforms to cover data requirements beyond ETL.
Integrate.io: Your Best Option for ETL
Integrate.io's cloud-based ETL solution delivers the benefits of this technology without needing hand-coded pipelines. Your data team has access to many powerful features that automate data extraction, preparation, cleansing, and loading based on the parameters that fit your use cases. They can focus on maximizing the value of your data without needing to worry about how it's getting from point A to point B for analysis.
Leverage our user-friendly no-code and low-code data pipeline creation to quickly add them to your data stack. Contact our support team to schedule a demo and risk-free 14-day pilot and experience the Integrate.io platform for yourself.