Data Virtuality Platform vs. Presto vs. RAW Graphs

Data Virtuality Platform's best and most unique feature is
that it is SQL-based, giving us flexibility when working with our data that
other marketing integration pipeline tools couldn't provide. Our main benefits
are the short time it takes to connect to our data sources and the flexibility
of the virtual SQL layer to meet our end users' data needs. It paves the way
for us to tap into various data repositories, extract the data contained
within, and examine it. It allows us to access information, generate actionable
reports, and make data-driven decisions. However, inadequate data governance
rules and a complex configuration process make data connectors challenging.

Incentivized

Presto is for interactive simple queries, where Hive is for reliable processing. If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. Presto is a great replacement for proprietary technology like Vertica

Incentivized

• ETL/ELT model building is flexible.
• The pipes' setup and use are intuitive.
• It lets us access various data sources, extract, and analyze data.

Incentivized

• Linking, embedding links and adding images is easy enough.
• Once you have become familiar with the interface, Presto becomes very quick & easy to use (but, you have to practice & repeat to know what you are doing - it is not as intuitive as one would hope).
• Organizing & design is fairly simple with click & drag parameters.

Incentivized

• Configuring data connectors can be tricky.
• Price could be more flexible and adapt to the use case.
• The ETL builder's user interface could be prettier.

Incentivized

• Presto was not designed for large fact fact joins. This is by design as presto does not leverage disk and used memory for processing which in turn makes it fast.. However, this is a tradeoff..in an ideal world, people would like to use one system for all their use cases, and presto should get exhaustive by solving this problem.
• Resource allocation is not similar to YARN and presto has a priority queue based query resource allocation..so a query that takes long takes longer...this might be alleviated by giving some more control back to the user to define priority/override.
• UDF Support is not available in presto. You will have to write your own functions..while this is good for performance, it comes at a huge overhead of building exclusively for presto and not being interoperable with other systems like Hive, SparkSQL etc.

Incentivized

Data Virtuality Platform has powerful transformation and
scheduling capabilities combined with the best support team. A virtual data
layer that supports SQL procedures is a game changer. Data Virtuality has made
data management more efficient. It lets us access various data sources, extract,
and analyze data. The short time it takes to connect to our data sources and
the flexibility of the virtual SQL layer allows us to meet our end users' data
needs.

Incentivized

Presto is good for a templated design appeal. You cannot be too creative via this interface - but, the layout and options make the finalized visual product appealing to customers. The other design products I use are for different purposes and not really comparable to Presto.

Incentivized

• It has the capability of integrating data from a variety of different sources.
• It improved development and user interface to offer a robust MDM repository and strategy.
• It offers a comprehensive solution for processing and retrieving data, which is one of the ways it assists.

Incentivized

• Presto has helped scale Uber's interactive data needs. We have migrated a lot out of proprietary tech like Vertica.
• Presto has helped build data driven applications on its stack than maintain a separate online/offline stack.
• Presto has helped us build data exploration tools by leveraging it's power of interactive and is immensely valuable for data scientists.

Incentivized