Oracle Data Integrator (ODI) vs. Posit

Oracle Data Integrator is well suited in all the situations where you need to integrate data from and to different systems/technologies/environments or to schedule some tasks. I've used it on Oracle Database (Data Warehouses or Data Marts), with great loading and transforming performances to accomplish any kind of relational task. This is true for all Oracle applications (like Hyperion Planning, Hyperion Essbase, Hyperion Financial Management, and so on). I've also used it to manage files on different operating systems, to execute procedures in various languages and to read and write data from and to non-Oracle technologies, and I can confirm that its performances have always been very good. It can become less appropriate depending on the expenses that can be afforded by the customer since its license costs are quite high.

Incentivized

In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.

Incentivized

• Oracle Data Integrator nearly addresses every data issue that one can expect. Oracle Data Integrator is tightly integrated to the Oracle Suite of products. This is one of the major strengths of Oracle Data Integrator. Oracle Data Integrator is part of the Oracle Business Intelligence Applications Suite - which is highly used by various industries. This tool replaced Informatica ETL in Oracle Business Intelligence Applications Suite.
• Oracle Data Integrator comes with many pre-written data packages. If one has to load data from Excel to Oracle Database, there is a package that is ready available for them - cutting down lot of effort on writing the code. Similarly, there are packages for Oracle to SQL, SQL to Oracle and all other possible combinations. Developers love this feature.
• Oracle Data Integrator relies highly on the database for processing. This is actually an ELT tool rather than an ETL tool. It first loads all the data into target instance and then transforms it at the expense of database resources. This light footprint makes this tool very special.
• The other major advantage of Oracle Data Integrator, like any other Oracle products, is a readily available developer pool. As all Oracle products are free to download for demo environments, many organizations prefer to play around with a product before purchasing it. Also, Oracle support and community is a big advantage compared to other vendors.

Incentivized

• The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
• The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
• Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

Incentivized

• ODI does not have an intuitive user interface. It is powerful, but difficult to figure out at first. There is a significant learning curve between usability, proficiency, and mastery of the tool.
• ODI contains some frustrating bugs. It is Java based and has some caching issues, often requiring you to restart the program before you see your code changes stick.
• ODI does not have a strong versioning process. It is not intuitive to keep an up to date repository of versioned code packages. This can create versioning issues between environments if you do not have a strong external code versioning process.

Incentivized

• Python integration is newer and still can be rough, especially with when using virtual environments.
• RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
• Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.

Incentivized

It is maturing and over time will have a good pool of resources. Each new version has addressed the issues of the previous ones. Its getting better and bigger.

There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.

Incentivized

For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when

Incentivized

RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses

Incentivized

Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.

Incentivized

We did it at the individual level: anyone willing to code in R can use it. No real deployment involved.

I have used Trifacta Google Data Prep quite a bit. We use Google Cloud Platform across our organization. The tools are very comparable in what they offer. I would say Data Prep has a slight edge in usability and a cleaner UI, but both of the tools have comparable toolsets.

Incentivized

RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.

Incentivized

RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.

Incentivized

• From a business intelligence perspective, it allows us to provide users with the necessary data and information to make informed decisions.
• Compared with other Oracle products and licensing, I do not think the pricing was unreasonable.
• It is part of a larger install, so for ease of use, we purchased it with other Oracle products.

Incentivized

• Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
• Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
• What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).

Incentivized