Modern organizations collect vast amounts of data from different systems, such as application servers, CRM and ERP systems, and databases. Getting access to this data and analyzing it can be a challenge. You can use data integration to resolve this challenge and generate a unified view of your company’s data. That’s why around 80% of business operations executives say that data integration is crucial to their current operations. For this purpose, you can use a data integration tool — a type of software that can move data from your source systems to destination systems.
With so many options in the market, choosing a data integration tool isn’t a straightforward process. If you select the wrong tool, it can affect how your data infrastructure works, which can have a direct impact on your business operations. That’s why you need to have a checklist of key technical considerations that can help you to pick the right data integration tool.
Data Connectors to Move Data From Sources to Destinations
Automation for Ease of Use
Flexible Replication Support to Copy Data in Multiple Ways
User Documentation to Get the Most Out of the Tool
Security Features for Data Protection
Compliance With Data Regulations
1- Data Connectors to Move Data From Sources to Destinations
The first step is to consider what data sources and destinations you have so you can look for data connectors that can move data between them.
Generally, data sources in an organization can include data sets in spreadsheets, accounting software, marketing tools, web tracking, customer relationship management systems (CRMs), enterprise resource planning systems (ERPs), databases, and so on. If you’re planning to aggregate data from different sources and load them into data repositories for storage or analysis, you need to look for destination coverage. This includes coverage for relational databases (e.g., Oracle), data warehouses (e.g., Snowflake), and data lakes (e.g., AWS S3).
List all your current and future potential sources and destination systems, and make sure your prospective tool offers coverage for all of them. These tools have different willingness to add new connectors.
Do keep in mind that data connectors vary from tool to tool. Just because a tool comes with a data connector of your preference doesn’t necessarily mean it’ll be user-friendly. Some data connectors are difficult to set up, which can make it hard for end users to move data. Therefore, compare the user-friendliness of connectors before deciding on a data integration tool.
2- Automation for Ease of Use
A data integration tool should minimize manual efforts that are required during data integration. Some things your tool should automate include:
Management of data types: Changes in schema can alter the type of a specific value, i.e., from float to integer. A data integration tool shouldn’t need manual intervention to reconcile data between the source and target system.
Automatic schema evolution: As applications change, they can alter the underlying schemas (e.g. adding/dropping columns, changing names). Your tool’s connectors should accommodate these changes automatically without deleting fields or tables. This ensures that your data engineers don’t have to perform fixes after the data integration process. Look for a tool that supports automatic schema evolution.
Continuous sync scheduling: Based on how often your organization needs data to be updated, choose a tool that offers continuous sync scheduling. This feature allows you to set fixed intervals to sync data at regular and short intervals. For instance, you can set your CRM system to sync data with your data warehouse every hour. If you want more convenience, you can look for a data integration tool that supports real-time integration, allowing you to move data within a few seconds.
3- Flexible Replication Support to Copy Data in Multiple Ways
Based on your needs, you might need to replicate data in more ways than one. That’s why your data integration should have flexible support on how you can replicate your data.
For example, full data replication copies all data — whether it’s new, updated, or existing — from source to destination. It’s a good option for small tables or tables that don’t have a primary key. However, it’s not efficient, as it can take more time and resources.
Alternatively, log-based incremental replication copies data by reading the data logs, tracking changes, and updating the target system accordingly. It’s more efficient as it minimizes load from the source since it only streams changes unlike full data replication, which streams all data.
Even if you feel you only need a specific type of replication right now, consider getting a tool that offers more flexibility, so you can adapt as your organization scales up.
4- User Documentation to Get the Most Out of the Tool
One thing that is often overlooked while choosing a data integration tool is the depth and quality of user documentation. Once you start using a data integration tool, you’ll need a guide that can explain how to install and use the tool as well as provide resources, such as tutorials, knowledge bases, user guides, and release notes.
Poor or incomplete documentation can lead to your team wasting time if they get stuck on a particular task. Therefore, make sure your prospective tool offers comprehensive documentation, enabling your users to get maximum value from their tool.
5- Security Features for Data Protection
On average, a cyber incident costs more than $9.05 million to U.S. companies. That’s why you need to prioritize data security and look for features in your tool that can help you protect sensitive data. Over the last few years, cyber-attacks have wreaked havoc across industries and compromised data security for many organizations. These attacks include ransomware, phishing, spyware, etc.
Not all users in your organization should have the authorization to create, edit, or remove data connectors, data transformations, or data warehouses or perform any other sensitive action. Get a tool that allows you to grant different access levels to your team members. For example, you can use read-only mode to ensure that an intern can only read information. Or you can grant administrative mode to a senior data architect, so they can use the features to transform data.
Your tool also needs to support encryption so you can mask data as it travels from one system to another. Some of the supported encryption algorithms that you need to be looking at for these tools include AES and RSA.
6- Compliance With Data Regulations
Regulatory compliance for data is getting stricter all the time, which means you need a tool that’s certified with the relevant regulatory bodies (e.g., SOC 2). You might have to meet a lot of requirements for compliance based on your company’s or user’s location. For example, if your customers live in the EU, then you need to adhere to GDPR requirements. Failure to do so can result in hefty penalties or damage to brand image.
There will be a greater need to prioritize compliance if you belong to an industry with strict regulatory requirements, such as healthcare (e.g., HIPAA). That’s why a data integration tool should also support column blocking and hashing — a feature that helps to omit or obscure private information from the synced tables.
Trial Your Preferred Data Integration Tool Before Making the Final Decision
Once you’ve narrowed down your search to the data integration tools that have the right features for your needs, you should test them for yourself. Most vendors provide a free trial that can last a week or more — enough time for you to connect it with your systems and assess it. Link data connectors with your operational sources and data repositories like a data lake or data warehouse and see for yourself how much time it takes to synchronize your data or how convenient your in-house users find your tool to be.
For starters, you can sign up for Striim’s demo, where our experts will engage you for 30 minutes and explain how Striim can improve real-time data integration in your organization.