Talend Open Studio is an open source integration software, used to build basic data pipelines or execute simple ETL and data integration tasks, get graphical profiles of data, and manage files from a locally installed, open-source environment.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
For quick daily integrations Talend is a very good tool and it makes development time so short and easy. Citizen developers who are not great programmers can pick up and start using Talend Open Studio within weeks. It's well suited for all kinds of data migration between various systems. It is less appropriate for smaller synchronous services where you need to trace the complete transaction and how data moved between them. It's also less appropriate for small data movements where other tools can be easier to use and manage.
The community is not that up to date and forum is not that great in response. Probably we should make people aware of the tool more on how to use and its implementations.
Talend crashes when transforming a lot of data (millions of rows).
Proper training documentation is a must for talend which is currently lagging. This will help users to learn more about Talend and use it effectively.
There is no licence requirement for Talend Open Studio. So, this is not relevant question. However, if you are asking whether we will use Talend in future. Yes. We will continue to use it. It's very powerful free tool which caters to all our extra, transform, load capabilities. We just love Talend for it's great functionality and ease of use.
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
Talend Open Studio is based on Eclipse and is full of redundant procedures to do one thing, like when installing libraries. Sometimes I cannot manually download the libraries that it can't find.
Many times, Talend freezes. When you give a cancel command, it takes several minutes to stop. It also takes a great toll on our PC with 16 GB of ram and I7 CPU, even in idle status. If you are downloading Maven Jar/Libraries, you cannot do anything and have to wait until the task is finished.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Talend Open Studio is free and we are not using the enterprise version which comes with licence and support. So, mostly depend on the open source community for any issues that we face. The document is good and we didn't have to use any support so far. We did evaluate the enterprise version and so far sticking to the free version.
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
Informatica has a limited number of components that you can use. This places a heavy limitation on the capabilities of Informatica. On the other hand, Talend allows you to create your own custom components using Java. For businesses that need to perform a wide variety of data operations, it can be quite useful to have the option of creating your own custom components to satisfy business needs.
Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
I delivered projects the client did not believe were possible, and I provided intermediate value by providing visibility to hidden data problems in their systems they could not detect before.
I was able to work 3 projects at a time, pausing gracefully in one while switching to the other, with minimal effort.
