Apache Hive vs. Apache Spark vs. Talend Open Studio (discontinued)

Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.

Incentivized

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.

Incentivized

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.

Incentivized

• Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
• Relatively easy to set up and start using.
• Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.

Incentivized

• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

Incentivized

• Your developers will be able to design SOA services graphically, and it is very easy to document and implement the code.
• Talend Open Studio is based on Eclipse IDE, so your developers will be very comfy using it
• Open is Key in Talend Open Studio = Open Source

Incentivized

• Some queries, particularly complex joins, are still quite slow and can take hours
• Previous jobs and queries are not stored sometimes
• Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
• Sometimes, directories and tables don't load properly which causes confusion

Incentivized

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

Incentivized

• 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.

Incentivized

Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.

Capacity of computing data in cluster and fast speed.

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.

Incentivized

Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.

Incentivized

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

Incentivized

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.

Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.

Incentivized

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.

Incentivized

Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed

Incentivized

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

Incentivized

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.

Incentivized

• Apache hive is secured and scalable solution that helps in increasing the overall organization productivity.
• Apache hive can handle and process large amount of data in a sufficient time manner.
• It simplifies writing SQL queries, hence helping the organization as most companies use SQL for all query jobs.

Incentivized

• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

Incentivized

• 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.

Incentivized