Oracle Database vs. TensorFlow

We migrated from NoSQL to an Oracle database. One of the reasons was robust backup and recovery options available in the Oracle database, which provide zero data loss. A transactional database like Oracle is a better fit for our use case than NoSQL. On a large scale, deployment was evaluated as a cheaper option than the NoSQL engine. This conclusion came even after considering Oracle license is expensive.

Incentivized

1. Whenever the problem has the demand for a neural networks based solution, Tensorflow (TF) is a great fit.
2. The tf.dataset API makes it really simple to create complex data pipelines in a few lines of code.
3. tf.estimators API abstracts all the complex computation graph creation logic making it very simple to get started.
4. Eager execution makes it simple to develop a TF graph as debugging the code would be like any other imperative Python program.
5. TF abstracts all the complexities of scaling it to multiple machines. It has various code and data distribution algorithms ready to use.
6. Projects like TensorBoard make monitoring the training process really easy. It also gives the ability to view embeddings without any extra code. Their What-If is extremely useful for poking and understanding a black box model. It also has tools to visualize data to quickly check for anomalies.
7. TF Autograph aims to covert any normal Python code into a distributed program which is quite handy to scale an existing code base.

Incentivized

• Best thing about it is that it supports PL/SQL which is helpful in writing complex quarries easily.
• Its storage capacity , backup and recovery features make it the best database storage tool available.
• Other thing I like about this software is its interface is so good.

Incentivized

• Data pipeline implementation is quite good, loading large amounts of data and pre-process it in an efficient way is no more issue for us
• It supports all major DL algorithms and network layouts such as ConvNets, RNN, LSTMs, Word2Vec, and even the latest transformer architecture
• The abstraction for the device is perfectly done and its support seamlessly for multiple GPU and even TPU will bring a lot of performance gain for enterprise scoped solution while still keep the flexibility
• The TensorBoard is amazing. I haven't seen a similar thing in other frameworks on the market. It allows us to quickly understand and debug the model with the info visualization which makes understanding much better
• A very supportive community, which is the key for sharing the ideas and find the quick and best solutions

Incentivized

• The memory demand and management makes it impossible to run it in a container.
• It is hard to perform local unit testing with Oracle even using the personal edition (aggressive all the available memory grab for itself).
• Lack of built in database migrations (e.g. as Flyway).
• The need to install the Oracle client in addition to its drivers.
• The cost of running it, especially in the Cloud.
• Comes with very spartan community grade client/management tools whereas the commercial offerings tend to demand a premium price.

Incentivized

• It would be much better if they could provide good documentation and easy ways to understand concepts.
• It is difficult to understand the concept behind for example, Tensor Graph, which takes a lot of time.
• As you have to write everything, it is time consuming to write the implementation of whole neural network. It would be better if they can provide some wrapper library to make things easier.

Incentivized

It is very likely to use this 12c (or next version) of Oracle Database. Nothing close to it in the marketplace in terms of performance, reliability and overall database management efficiency. If Oracle did one thing really good - it is it's OLTP Database I must say.

Many of the powerful options can be auto-configured but there are still many things to take into account at the moment of installing and configuring an Oracle Database, compared with SQL Server or other databases. At the same time, that extra complexity allows for detailed configuration and guarantees performance, scalability, availability and security.

Incentivized

Support of multiple components and ease of development.

Incentivized

1. I have very good experience with Oracle Database support team. Oracle support team has pool of talented Oracle Analyst resources in different regions. To name a few regions - EMEA, Asia, USA(EST, MST, PST), Australia. Their support staffs are very supportive, well trained, and customer focused. Whenever I open Oracle Sev1 SR(service request), I always get prompt update on my case timely. 2. Oracle has zoom call and chat session option linked to Oracle SR. Whenever you are in Oracle portal - you can chat with the Oracle Analyst who is working on your case. You can request for Oracle zoom call thru which you can share the your problem server screen in no time. This is very nice as it saves lot of time and energy in case you have to follow up with oracle support for your case. 3.Oracle has excellent knowledge base in which all the customer databases critical problems and their solutions are well documented. It is very easy to follow without consulting to support team at first.

Incentivized

Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.

Incentivized

Overall the implementation went very well and after that everything came out as expected - in terms of performance and scalability. People should always install and upgrade a stable version for production with the latest patch set updates, test properly as much as possible, and should have a backup plan if anything unexpected happens

Use of cloud for better execution power is recommended.

Incentivized

Because of a rich user base and support for any critical issue, this is one of the best options to choose. In case the project has a TCO issue, it can compromise and choose Postgres as the best alternative. SQL server is also good and easy to code and maintain but performance is not as good as the Oracle

Incentivized

Can't seem to choose any deep learning platform in the above, so I'll list it here: 1. Apache MXNet: this has been used for one of our main algorithms for search as an end-to-end pipeline. We chose this because of the Scala bindings, which makes it easier to integrate with out JVM backend. MXNet seems comparable to TensorFlow, although community support is not as good as TensorFlow, and there are issues with memory leaks that are being worked on. TensorFlow in general is easier to use, but MXNet isn't too far behind. 2. Keras: still a favorite. Often I use this when paired with TensorFlow. TensorFlow 2.0 will make it even easier. 3. PyTorch: only used it a little, so it's hard to provide a good opinion. 4. DL4J: used it initially in an early days project because it has good JVM support. Harder to used not because of poor API design, but because community support is lacking and features don't come out as fast as TensorFlow.

Incentivized

• Our product's ability to monitor the Oracle database has generated significant revenue from only a few customers, totaling $1mn.
• An Oracle account helps analyze the patches and changes going into it, enabling the team to enhance the product and update it more quickly. It reduced the effort by more than 50%.
• Being a closed-source code DBMS, it is difficult to ascertain the exact exploits of the vulnerability to provide patches to customers and requires reverse engineering.

Incentivized

• Positive Impact- As I mentioned before its open source. Very easy to learn for average programmer/ developer. We were able to design a POC model for understanding the patient appointment cancellation snd reasons behind it in 3 week time frame.
• Negative Impact- If you are using tensor flow for small project it works fine. If you are trying to build a model for face recognition it will be hard to program and train the system. It needs data to be processed before hand cannot learn on the go.