Nearly all data virtualization providers charge based on the compute and query volume being run through their systems. Looking for an affordable data virtualization tool with a flat licensing fee? Conduit is the answer.
At Blueprint Technologies, one of our core products is Conduit. Because it is still new, we find that many do not understand the basics of Conduit, nor do they accurately estimate the benefits to be gained from investing in a good data virtualization product and partner. In part one, Conduit expert Bobby Huang covered the flexibility and cost benefits that Conduit provides. In part 2 Bobby is back to answer some frequently asked questions.
What are some signs it’s time to invest in a data virtualization tool?
Any company that has disparate data sets and unanswered questions about their business, no matter the industry, will benefit from a data virtualization tool like Conduit.
What are some of the challenges of data virtualization that Conduit addresses?
One major challenge of data virtualization is the cost structure most providers use. Nearly all data virtualization providers charge based on the compute and query volume being run through their systems. Conduit is markedly different in that the sole cost involved is a flat licensing fee, meaning that organizations can freely explore their data to find new correlations without worrying about incurring what are often significant incremental costs.
The other main challenge organizations face when utilizing data virtualization is that many platforms enable the unification and viewing of large volumes of disparate data, but they don’t include the powerful querying capabilities that enable the manipulation and analysis of that data. Customers of those platforms often must buy another tool and try to integrate it into their data virtualization tool. Conduit includes a native query engine that allows customers to easily and efficiently query that aggregated, virtualized data, further eliminating inefficiencies and operational costs.
How do customers install and set up Conduit?
The first step is to assess the current data environment and determine if the difficulty of managing and analyzing that data is a significant hinderance to the organization’s long-term goals and strategy. Once the need for data virtualization and aggregation has been established, it is important to verify the hardware that is going to be used to run Conduit meets the minimum system requirements of having four cores, 16 GB of RAM and a Linux Ubuntu OS.
Once those boxes have been checked, it’s time to install Conduit. Conduit can be easily and quickly downloaded via the Azure Marketplace, and for those customers who use AWS or run a machine on prem rather than virtually, Blueprint will deliver the scripts necessary to download and install Conduit through a white-glove service. Conduit also comes with Blueprint’s commitment to work with individual customers to set up their Conduit experience and train employees on the tool.
Once Conduit is installed and configured in their environment and their staff has been trained, the real work (and fun) begins. Conduit includes a web interface that allows users to quickly and easily begin setting up connectors between various data sets and types, including Oracle, Elasticsearch, Azure SQL and PostreSQL.
When Conduit is up and running, the results are truly remarkable and transformational. Data consumers receive new reports run on a massively expanded and aggregated data universe, but to their eyes it will be business as usual. No heavy engineering work is needed to produce impactful, near real-time results that decision-makers need to effectively run their business.
What are the coolest things customers can do with Conduit?
From a practical perspective, the coolest thing Conduit does is easily and seamlessly connect as much disparate data as needed. Conduit enables the connection of sources as diverse as SQL tables that sit on prem and a CSV from Azure Blob storage that is in the cloud and connects them without the need for data transformation work. All the work-arounds organizations are often forced to use, such as saving that CSV to a local machine or transforming and copying the two data sets into one tool, become unnecessary.
From a sheer “whiz-bang” perspective, I think one of the coolest capabilities of Conduit is that it can consume deeply nested, non-relational data and, utilizing its GPU engine, query those complex data sets at high speed and at scale. While many analysts and decision-makers would like to query their non-relational data, many of the query engines on the market today cannot do that, because they are built off a SQL platform that only works on relational data. With its built-in Spark query engine, Conduit functionally transforms non-relational data into relational data while it runs queries at high velocity. With Conduit, we’ve found a way to use code to set up and run processes that would normally have required manual effort, getting vital data into consumers’ hands quicker than has ever been possible.