Informatica Big Data Parser Competitors and Alternatives

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Read reviews of Informatica Big Data Parser competitors and alternatives

NitinKumar
Engineering Manager at Sigmoid
Real User
Top 5Leaderboard
Easy to code, fast, open-source, very scalable, and great for big data

What is our primary use case?

I use it mostly for ETL transformations and data processing. I have used Spark on-premises as well as on the cloud.

Pros and Cons

  • "Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
  • "Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

What other advice do I have?

I would definitely recommend Spark. It is a great product. I like Spark a lot, and most of the features have been quite good. Its initial learning curve is a bit high, but as you learn it, it becomes very easy. I would rate Apache Spark an eight out of ten.
RS
AD - Associate Director at a financial services firm with 10,001+ employees
Real User
Top 10
Feature rich and scalable with good support, but there are performance issues and the security could be improved

What is our primary use case?

We are using this solution for storing Big Data in one centralized location.

Pros and Cons

  • "The main advantage is the storage is less expensive."
  • "Currently, we are using many other tools such as Spark and Blade Job to improve the performance."

What other advice do I have?

I am a part of security and software development. We are currently considering migrating to the cloud, and planning on using Microsoft Azure, mainly for the Big Data component. I would rate this solution a five out of ten.
SS
Analytics and Reporting Manager at a financial services firm with 1,001-5,000 employees
Real User
GUI could be improved. Useful for speedily processing big data.

What is our primary use case?

We do have some use cases, like analysis and risk-based use cases, that we've provided and prepared for companies in order to evaluate, but not many. The business units have so many things that we don't know how to help formulate into another tool and utilize as a use case. They also have so many requirements and costs. I work for a financial institution, so every solution that they need to consider has to be on-premise. I'm actually just evaluating and up scaling my skill sets with this solution right now.

Pros and Cons

  • "The speed of getting data."
  • "Anything to improve the GUI would be helpful."

What other advice do I have?

We will have a lot of big data, which is why we need it. Otherwise, the solution is not needed. The solution really depends on the size of your data, its complexity, and the analysis that you are doing. Spark is good, but it is not mandatory. Since I don't have experience in production with the solution, the best I can rate it now is a five (out of 10).
Get our free report covering Apache, Cloudera, Cloudera, and other competitors of Informatica Big Data Parser. Updated: June 2021.
511,307 professionals have used our research since 2012.