Fivetran Acquires HVR: You’re in for a Treat
Fivetran’s acquisition of HVR recently made waves across the data world. Both technologies have been staples of modern data stack design: Fivetran is a leader in automated data integration and HVR has been a major player in large-scale data replication. If their merger is executed successfully — and, knowing both companies, there’s no reason to believe it won’t be — businesses looking to optimize their data pipelines will be looking at a very compelling offering.
In this post, we will be looking at the individual strengths and weaknesses of these two technologies, how they complement each other — and why you should be excited about them joining forces.
There’s a variety of reasons why an enterprise would want to replicate their data. Notably, it can reduce load on source databases by moving users’ or ETLs’ queries over to a replica data set. This process is also essential when trying to ensure high availability of data for multiple use cases — for example, when you want provide customers a snappy online banking platform while also ensuring that your data scientists get fast enough query times on the same data to effectively work their magic. Similarly, data replication helps quickly move data to cloud environments from on-premises enterprise databases to enable self-service BI models.
HVR is very good at this. Thanks to utilizing a change data capture (CDC) approach, HVR can replicate and sync incremental changes in real-time — which is useful for when you want to keep your source and target data in sync without the massive overhead of having to copy the former over every time. This, combined with its great customizability to unique environments and use cases, is the primary reason why we, as a system integrator, tend to recommend HVR when a customer needs to keep a large volume of data from a database in sync constantly.
On the flipside, HVR has a steep learning curve. It takes considerable expertise to deploy and support, and the advantages laid out above also necessitate great precision when working with the tool. If your CDC jobs are set up on an imperfectly configured source database or with an incorrect HVR setup, the benefits may go out the window.
Fivetran: Quick and Easy — within Limits
In light of HVR’s pros and cons, it’s easy to see it as a good fit when Fivetran is looking to create a well-rounded stable of offerings to cover a larger swathe of the market under the umbrella of a single vendor.
Fivetran has developed a reputation for its ease of use. Customers access this software as a service (SaaS) as a webpage. Once there, even non-technical users can simply run through a wizard and select what source type they want to get data from, through what host, and which specific tables they want to make accessible for which users — and Fivetran starts pumping the data right away.
With Fivetran, connectors are preconfigured — there’s no need for expertise and effort to figure how to structure the stored data, and by loading data into a data warehouse prior to transforming it, it unlocks faster query times by eliminating the lag caused by formatting. And finally, as a SaaS-type offering that simplifies configuration, enabling a handful of people to do work of dozens of engineers, Fivetran is a perfect fit for organizations trying to navigate a shortage of dedicated experts and looking to keep operations lean.
For these reasons, if a company wants to start getting data fast, fuss-free, from newer types of sources, Salesforce or through APIs, Fivetran is a safe bet.
However, Fivetran is not especially well-suited to moving large amounts of data. When dealing with legacy database management systems with complex data storage like SAP ERP, Fivetran is either simply not an option, or not a very potent one. But, as we’ve established, HVR shines when it comes to accommodating large data sets and finicky use cases.
HVR + Fivetran: A Comprehensive, Future-fit Data Offering
HVR has been a proven solution for on-premises data movement, while Fivetran has maintained a cloud-first approach. Customers looking to add additional security to their data transfers will benefit from both technologies, as Fivetran now offers a so-called Business Critical version that, when paired with AWS DirectLink, will not expose any data through the public internet. Meanwhile, HVR can be configured for two-way SSL and IP range allowlisting — all while using the product’s robust in-flight data compression.
With Fivetran and HVR as one, you’ll be able to leverage a single vendor to easily integrate data from over 300 platforms and replicate data from most enterprise-grade databases. It’s also a smart business move, Fivetran will now single-handedly cover the needs of a significant chunk of the data-replication market, while also having a good track record supporting up-and-coming data sources.
Lastly, with the currently-in-the-works combination of the two technologies, organizations will be able to benefit from both worlds and leverage HVR’s unparalleled ability to incrementally replicate mission-critical enterprise data leveraging Fivetran’s convenient UI.
You Next Steps
Fivetran — now more than ever — has immense potential for augmenting or even fully replacing your existing ingestion framework. CIOs and technical directors should take note and evaluate Fivetran and HVR as an update to legacy data pipelines.
In the age of data, resilience and streamlined operations represent a massive competitive advantage — take the opportunity to test-drive Fivetran and HVR, explore the list of connectors and see what value the company can bring to your unique use cases.
About the authors:
Márton Berta is Data Replication Team Lead at Starschema. Márton draws on nearly a decade of experience in consultation and operation of data replication solutions between heterogeneous on-premises, cloud-hosted and cloud-based DBMS environments. Since joining Starschema in 2014, he has implemented various data replication pipelines, while leading a team of specialists in the field.
Connect with Márton on LinkedIn.
Péter Fehér is a Technical Lead at Starschema. Peter gained experience in data warehousing and database design as a former business intelligence developer at financial institutions. Since joining Starschema in 2015, his main focus has been data replication and incident management.
Connect with Péter on LinkedIn.
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