MongoDB vs. Oracle Data Integrator (ODI)

If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.

Incentivized

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

• Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
• You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
• Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.

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

• An aggregate pipeline can be a bit overwhelming as a newcomer.
• There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
• Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.

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

I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.

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.

NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.

Incentivized

Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.

Incentivized

While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.

Incentivized

We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.

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

• Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
• You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB

• 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