Data is a treasure trove of valuable information in the modern business world. Data management is like being the guardian of that treasure, ensuring data is organized, accessible, and secure. There are a lot of practices to manage data, but here, we’ll talk about data integration in general and ETL as a part of it and the difference between them.
Data integration and ETL are the critical dynamic duo of data management. Together, they help companies harness the full power of data to improve decision-making, boost efficiency, or enhance collaboration. So, mastering data integration and ETL is not just a game-changer; it’s a key to unlocking the full potential of data.
Why Data Integration is Important
- Integrated data means less time spent searching for information and more time focusing on what matters.
- Collaboration becomes smoother and more effective when everyone has access to the same up-to-date data.
Why ETL is Important
- Automating the ETL process saves time and reduces the risk of errors compared to manual data handling.
- ETL tools can handle large volumes of data, making it easier to scale data operations as businesses grow.
In this article, we’ll examine each term and discover its difference. To do this, we’ll compare data integration and ETL processes, find their pivotal moments, and consider both integration methods’ most popular use cases.
Table of Contents
- What is ETL?
- What is Data Integration?
- Key differences between Data Integration and ETL
- Popular Data Integration Tools for 2024
- Use Cases for Data Integration and ETL
- Conclusion
What is ETL?
Imagine data integration as a round dining table with a set of chairs to sit behind, like ETL, ELT, reverse ETL, data replication, synchronization, etc… In that case, ETL is just one chair. However, it’s the most popular and useful one.
Extract, Transform, and Load (ETL) is a process that prepares and moves data from various sources into a centralized data warehouse. This process is the backbone of effective data management. It automates moving and preparing data, making it easier for businesses to get valuable insights and make smarter decisions.
For instance, John Smith manages a retail business, and data comes from multiple sources, such as online sales, in-store sales, and customer feedback. ETL helps him extract all this data, clean and standardize, and load it into a central data warehouse. This way, Mr. Smith can easily analyze sales trends, customer behavior, and inventory levels all in one place.
What is Data Integration?
Data integration is the term describing a process that combines data from various sources (CRMs, databases, spreadsheets, cloud services, etc.) into a unified view.
Imagine an online store with customer data in CRM, sales data in an e-commerce platform, and inventory data in a spreadsheet. Data integration is the first step in collecting all this info into a single dashboard to understand what’s going on in the company. So users can see how trends affect inventory levels and adjust their marketing, sales, services strategies, etc., based on customer behavior.
Key Differences between Data Integration and ETL
As we have considered, ETL is just a part of the data integration process. Understanding the differences between the whole data integration and ETL is crucial for choosing the right approach for data management needs. Here’s a quick overview to help users understand what sets them apart.
| Criteria | Data Integration | ETL |
|---|---|---|
Scope and Application | Broader scope, includes various methods to combine data from different sources into a unified view. | Specific process focused on extracting, transforming, and loading data into a data warehouse. |
| Processes and Methods | Involves data synchronization, replication, federation, and consolidation. | Involves three main steps: extraction of data, transformation of data to meet requirements, and loading of data into a target system. |
| Tools and Technologies | Uses tools like Skyvia, Informatica, Talend, and MuleSoft to connect and merge data from diverse systems, ensuring a unified data view. | Uses tools like Skyvia, Talend, Informatica PowerCenter, Apache Nifi, and Microsoft SSIS to handle the extraction, transformation, and loading of data into data warehouses. |
| Output and Data Volume | Outputs can be varied including integrated applications, dashboards, and unified data views. | Typically outputs structured, cleansed data into a data warehouse, handling large volumes of data. |
| Frequency and Timing | Can be real-time, near real-time, or batch processing depending on the method used. | Generally involves scheduled batch processing, but can also support near real-time data loading. |
Common Scenarios | – Real-Time Integration. Useful for scenarios where immediate data access is required, such as in live dashboards or monitoring systems. – Data Consolidation. Common for creating a single source of truth by merging data from various systems, helping in comprehensive reporting and analysis. – Application Integration. Helps in ensuring different software applications can communicate and work together seamlessly, enhancing workflow efficiency. | – Data Warehousing. Commonly used for preparing and loading data into a centralized data warehouse where it can be analyzed. – Data Migration. Includes transferring data from legacy systems to new systems, ensuring data consistency and accuracy. – Data Cleaning. Crucial for transforming raw data into a clean, consistent format that is ready for analysis. |
| Best Practices | – Plan and Design. Define clear integration goals and design a flexible architecture to accommodate future changes. – Use Automation. Leverage automated tools to minimize manual intervention, which reduces errors and increases efficiency. – Monitor and Manage. Regularly monitor data flows and manage integration processes to ensure data accuracy and reliability. | – Data Quality. Focus on ensuring data is cleaned and transformed accurately to maintain high quality. – Efficiency. Optimize ETL workflows to handle large volumes of data efficiently, avoiding bottlenecks. – Documentation. Keep detailed documentation of ETL processes to facilitate maintenance, troubleshooting, and future enhancements. |
Popular Data Integration Tools for 2024
Let’s walk through the popular data integration tools that make data work for your business.
Skyvia
Skyvia data integration platform is perfect for beginners and pros. According to the last G2 rate, it’s second in the top 20 easiest-to-use ETL tools. The solution offers seamless cloud integration, ETL, ELT, Reverse ETL, migration, sync, the visual building of advanced data pipelines, orchestration, etc. With its intuitive interface, you can easily connect 190+ data sources like Salesforce, Google Sheets, and databases without coding. Plus, it supports scheduled data sync and automated workflows.
Informatica
Informatica is a vital tool in the world of data integration, known for its strong performance and scalability. The platform is ideal for large enterprises. With a wide range of features, including ETL, data quality, and master data management, Informatica helps organizations easily handle complex data integration tasks.However, despite its powerful functionality, the solution might not be user-friendly for non-techs.
Talend
Talend is a good choice for businesses of all sizes, especially those who love open-source solutions. It offers a comprehensive suite of tools for ETL, data integration, data quality, and more. Talend offers users a drag-and-drop interface to easily design data workflows and strong community support.
Microsoft Power BI
Power BI is perfect for pulling data from various sources and creating stunning dashboards and reports. The tool smoothly integrates with other Microsoft products like Azure, Excel, and SQL Server, so it’s a good selection for companies already using the Microsoft ecosystem. However, the solution may be too complicated for beginners.
Fivetran
Fivetran is about automated data pipelines. It’s easy to set up and use and requires minimal maintenance. So, the solution is a favorite among data teams who want to focus more on analysis and less on data wrangling. The platform also supports a wide range of connectors and ensures that data is always up-to-date and ready for work.
Stitch
Stitch offers a simple, straightforward approach to data integration. It perfectly fits startups and small businesses looking to quickly and efficiently get their data into a data warehouse. Like Fivetran, Stitch is known for its easy setup and usability. It scales with organizations’ business needs and might also be a good choice for growing companies.
Use Cases for Data Integration and ETL
Every business wants to reach success and lead in the race. Data integration tools are the ones helping to become and stay the leader. Let’s review a few real-life scenarios, showing how it works in various business areas.
How the American Health Care Association Improved its Reporting
The American Health Care Association (AHCA) needed a solution to integrate on-prem databases with Dynamics 365. Even with transitioning to a cloud database, they still had to build reports and dashboards on-prem, applying SSRS (SQL Server Reporting Services) for creating advanced analytics and building reports. For that, the data from Dynamics 365 should be replicated to SQL Server (served as a data warehouse).
They selected Skyvia to solve this problem. Such a decision allowed AHCA to avoid changing the current data stack and offered a variety of data connectors to cover possible future requests. Skyvia’s data integration abilities allowed AHCA to overcome the data streamlining challenge. So, the team can configure the system with just a few clicks and automatically capture data updates from source to destination, ensuring that their reports remain up-to-date and accurate.
How NISO Automated Data Flows for Financial Operations
NISO, an outsourced CFO company that provides services to the MCA industry (merchant cash advance), needed to see a broad picture of the financial aspects of its customers. They used self-made scripts to extract data from MySQL to Excel Spreadsheets and, afterward, QuickBooks Online. With the company’s growth, this manual approach quickly became a bottleneck, so they searched for a tool to improve it and selected Skyvia to connect services in the cloud directly instead of developing its own APIs. The selection was because the NISO team can directly connect such services in the cloud with Skyvia. The company also has many necessities regarding SQL and MySQL servers, and Skyvia was perfect for integrating with other products with their specific developing features.
With Skyvia’s integration features, NISO can gain a broader reach for customer info and scale more confidently.
How Cirrus Insight Enhanced its Salesforce Data Integration
Cirrus Insight, a sales enablement platform, needed to automate data integration between various systems like Salesforce, QuickBooks, and Stripe. They considered Skyvia to facilitate data migration between Salesforce accounts and synchronize Stripe data with Salesforce.
- For the Salesforce to Salesforce integration, Skyvia migrated data from one instance to another so that all operations are in one instance but still have all customer data in the other. The implementation costs are 20 times less than usual with such integration.
- For the Stripe integration scenario, data was transferred automatically into Salesforce. With Skyvia, they can handle bringing over automated data updates and integration, providing customer records synchronization between these systems.
This automation saved time and significantly reduced implementation costs.
Conclusion
Data integration is an essential process that combines data from multiple sources into a unified view using methods like data virtualization, replication, federation, CDC, API integration, and streaming beyond just ETL.
- Data Integration is flexible and perfect for merging data from multiple applications or creating comprehensive dashboards.
- ETL is ideal for preparing data for in-depth analysis and reporting.
By understanding these capabilities, users can choose the right approach for their needs. No matter what the company is looking for, the flexibility of data integration or the structured process of ETL. Both points are crucial for making data work for it.
The best way to cover data integration processes in general and ETL is to select a universal solution like Skyvia that can do both and even more.
