Data warehouses host data replicated from many sources and are often the foundation of a data-driven organization's analytics stack. For this reason, organizations require effective data consolidation tools to support data warehousing.
Data pipelines help data teams operate with an aggregated view of their company operations by optimizing data transit from various applications and data sources into the data warehouse. While a variety of data transportation technologies are available, ETL is the most common method of data integration (Extract Transfer Load).
Consider ETL software to be the plumbing within your company's walls. Data pipelines are critical to the seamless operation of any company.
What should you look for in an ETL tool, and how should you compare them?
When looking for the proper ETL solution for your company, consider the following:
Data sources supported - breadth of data connectivity
Documentation and support
Batch and stream processing
Security & compliance
Reliability & stability
Extensibility and future-proofing
Compatibility with third-party tools
Data transformation capabilities
Look for an ETL solution that works with as many of your key tools as possible.
You may need to create a unique solution for some of the remaining integrations, depending on the limitations of the ETL tool you use. Of course, from many viewpoints, this is not ideal, but it may be unavoidable.
Because connectivity is so important, the first thing you should do is choose a universal data platform with a large library of supported data sources.
As your data volumes increase, you'll need a solution that can adapt to meet your needs without compromising service. Analyze how the data pipeline tool you're evaluating is built to handle high data volumes.
Additional data sources should be supported by your ETL supplier, but it would be much better if you had the option to add data sources yourself.
The user interface should be straightforward to use, making it quick to set up integrations, schedule replication activities, and monitor them.
Are the error messages clear if problems arise? Are those issues simple to resolve, or do you need to contact the vendor's support team for assistance?
When it comes to the support crew, ensure you do your research. To assess each vendor's expertise, contact their support team and ask multiple questions. Are they capable of dealing with problems? Do they respond quickly? What options do they provide for customer service, such as email, phone, or online chat?
Finally, ensure the vendor's documentation is clear, complete, and written at a technical level appropriate for those who will use the tool.
Since security is so important for any IT system, there are a few things to think about while building a cloud-based data pipeline.
Within the application, the vendor encrypts data in motion and at rest.
Are there user-configurable security controls?
What connectivity options to data sources and destinations are available? Can it defend your firewall by supporting secure DMZ access?
Is it capable of providing strong and secure authentication?
Does the vendor create copies of your data? You'll need a secure solution that can flow data into and out of your databases without duplicating it into theirs.
Is GDPR compliance and file transfer governance supported?
Many ETL software companies have distinct pricing structures. They may charge according to the volume of replicated data, the number of data sources (or connections), or the number of authorized users. By far, the preferred option is a solution that licenses by connections and users, rather than by data volumes, so you can scale up your data integrations without paying costs at scale. It's crucial to think about scalability and how increased data volumes would affect your costs (ideally, they shouldn’t).
It’s also always a solid choice to select platforms offering a full-featured free trial to get a no-risk feel for the platform.
Pre-load transformation actions within the data pipeline were once required for data warehouses, which were expensive in-house appliances. But things have changed rapidly in the last few years.
Data teams can now efficiently perform data transformations after the data has entered the system. You may want to employ the processing capabilities of the data warehouse or database where you're piping your data in some cases. Modern data replication solutions will allow you to follow a faster exchange, load, and convert process, allowing your data transportation pipelines to flow much faster.
Learn more about ETL vs. ELT processing.
Always test ETL solutions in your own environment with your own data for the following reasons:
Usability: Test all kinds of functions; even if you think you don't need them right now, they might be useful in the future.
Synchronization and Integration: Assess how simple it is to set up a data source and whether the ETL tool can send data at the appropriate frequency.
Timeliness: Ensure all data arrives on time and meets the requirements of your data analysts.
Accuracy: Set up a few data sets from various sources and double-check that the information sent is correct.
It's mission-critical to have a straightforward way of synchronizing data between on-premise and cloud data sources with a wide range of traditional and emerging databases. Organizations need an ETL data pipeline solution that can replicate data to facilitate operational reporting, support GDPR compliance and file transfer governance, and offers secure DMZ access to protect the company firewall. To learn more about ETL and data synchronization solutions, visit CData, one of the industry’s fastest growing ETL solutions providers.