Apache Spark vs. Asana

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

The usability of Asana is broad since it's available in a variety of platforms that are widely used nowadays. I think that it would be great for people who are constantly on the move and switching devices, since it has allowed me to work from my phone, too. I also think that Asana has proven itself to handle a large quantity of work

• 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

• Through it, we were able to communicate and cooperate with the rest of the team to complete the work in the required manner and at the appropriate time.
• Available for free on Android and iPhone.
• Asana has a simple and easy user interface

• 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

• Asana is not really a project management tool, it is good for simple projects only
• UI has some glitches (doesn't save preferences, etc.)
• Integration with other tools (Office 365, MS Project)
• No reminders if the working period you entered is on holidays

Incentivized

Capacity of computing data in cluster and fast speed.

• Mobile + Desktop capabilities - Able to use it anywhere, anytime.
• Task management - Easy to view projects and their progress.
• Time management - Easy to see what elements require time / how they interrelate and manage the project on an overall basis.
• Company management - Be able to see from a master dashboard perspective where each project is in its stages of completion.
• Clear communication - It's visually documented who is doing what at what time so that the whole project gets done.

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

It is very user-friendly. Takes a new employee an hour to start figuring out how the system works. That's an important factor. You don't want to encounter the issue where employees need a week to understand how the system works. For example, JIRA, I tried using it for a week and I still don't understand the complicated layout. Asana has a simple interface. Once you see it, you get it type of program.

Incentivized

Working with Asana on a daily basis

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.

I haven't had to use their support so I can't rate it. The fact that I haven't needed them reflects the ease of use of the product. I would recommend that any new users schedule a complete demo of the product to ensure that they are using it to it's fullest (there's a lot of useful features).

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

Asana is a top-tier project management software that helps us organize and track projects from start to finish. It allows us to apply tasks/to-dos to multiple projects without duplication, divide complex projects into smaller tasks, and track project progress. It also helps us organize work on Kanban boards or linear lists. It stands out from the crowd in a big way compared to the competition.

I used this tool on a daily basis at work and it runs as a solid rock

• 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

• Productivity - Most of all, it allows me to be more productive and have a good sense of where I am with projects and deadlines.
• Easy to use - I can make the most of my time to get up (and stay) up and running.
• Project coordination - Allows me to assign tasks to my interns and communicate directly within the platform to keep the work train moving.

Incentivized