Apache Spark Reviews

The only issue I faced with the tool was that I used to choose the compute device to support parallel processing, and it has to be more like scaling up horizontally. The tool should be more scalable, not in terms of increasing the CPU or something, but more in the area of units. If two units are not enough, the third or fourth unit should be able to come into the picture.

From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable. Sometimes, I get an error saying that it is an RDD-related error, and it becomes difficult to understand where it went wrong. When I deal with datasets using a library called Pandas in Python, I can actually apply functions on each column and get a transformation from the column. When I try to do the same thing with Apache Spark, it is okay and works, but it is not straightforward; I need to deal with it a little differently, and even after trying to do that differently, the problem I face there is, sometimes it will throw an error saying that it is looping back to the same, but I was not getting that kind of errors in Pandas.

In future updates, the tool should be made more user-friendly. I want to take fifty parallel processes rather than one, and I want to pick some particular columns to be split by partition, so if the tool is user-friendly and offers clarity and flexibility, then that will be good.

I have been using Apache Spark for four years.

Stability-wise, I rate the solution a nine out of ten. The only issues with the tool revolve around user interaction and user flexibility.

It is a scalable solution. Scalability-wise, I rate the solution an eight out of ten.

Around five people in my company use the tool.

The solution's technical support is helpful, but I faced some problems which were more of a generic issue. If I face any problems which are non- generic issues, I get help from the tool's team. For the generic issues, I get answers mainly from the forums where the problem was already resolved. When it comes to some unknown problem or specific problem with my work, then the support takes time. I rate the technical support a seven out of ten.

Neutral

I only work with Apache Spark.

The product's initial setup phase was easy.

I managed the product's installation phase, both locally and on the cloud.

The solution is deployed on the on-premises version.

The solution can be deployed in two to three hours.

Apache Spark has helped save 50 percent of the operational costs. Time was reduced with the use of the tool, but the computing part increased. Overall, I can see that the tool's use has led to a 50 percent reduction in costs.

I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten.

Previously, I was more of a Python full-stack developer, and I was happy dealing with PySpark libraries, which gave me an edge in continuing the work with Apache.

Speaking about Apache Spark's use in our company's data processing workflows, I would say that when we deal with large datasets of data, if we don't use Spark, then when we try to use a data frame consisting of one year of data, it used to take me 45 minutes to an hour. Moreover, sometimes I used to get the memory out of space errors, but such issues were avoided the moment I started using Apache Spark, as I was able to get the whole processing done in less than five minutes, and there were no memory issues.

For big data processing, the tool's parallel processing and time are areas that have been helpful. When I try to apply a function, I can directly data write one code. Basically, I used Apache Spark to forecast multiple units at the same time, and if not with Apache Spark, I would be doing that one by one, which is more of a serial processing process that used to take me around five hours. At the moment, we use Apache Spark in parallel processing, where computing happens parallelly, and all these computations are cut down by at least 90 percent. It helps me significantly to reduce the time needed for operations.

The tool's real-time processing is an area that I have not tried to use much. When it comes to real-time processing of my data, I use Kafka.

I am handling data governance using Databricks Unity Catalog.

When I try to apply an ML model, I am unable to get that model done on a table partitioned by a particular column; it makes me get the job done in a reduced number of partitions. If I go with five partitions, I am able to get at least three to four times the benefits in a lesser amount of time.

Regular maintenance exists, but it is not like I have to sit week by week and upgrade a patch or something like that. The maintenance is done mostly in about six months to a year.

I take care of the tool's maintenance.

I recommend the tool to others.

I rate the tool an eight out of ten.

Apache Spark is a programming language similar to Java or Python. In my most recent deployment, we used Apache Spark to build engineering pipelines to move data from sources into the data lake.

One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast.

Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors.

I have been using Apache Spark for approximately five years.

Apache Spark is stable.

I have found Apache Spark to be scalable.

Apache Spark is open-source, there is no team that will give you dedicated support, but you can post your queries on the community forums, and usually, you will receive a good response. Since it's open-source, you depend on freelance developers to respond to you, you cannot put a time limit there, but the response, on average, is pretty good.

If Apache Spark is in the cloud, setting it up will require only minutes. If it's on Amazon, GCP, or Microsoft cloud, it'll take minutes to set everything up. However, if you are using the on-premise version, then it might take some time to set up the environment.

I rate Apache Spark an eight out of ten.

We just finished a central front project called MFY for our in-house fraud team. In this project, we are using Spark along with Cloudera. In front of Spark, we are using Couchbase. 

Spark is mainly used for aggregations and AI (for future usage). It gathers stuff from Couchbase and does the calculations. We are not actively using Spark AI libraries at this time, but we are going to use them.  

This project is for classifying the transactions and finding suspicious activities, especially those suspicious activities that come from internet channels such as internet banking and mobile banking. It tries to find out suspicious activities and executes rules that are being developed or written by our business team. An example of a rule is that if the transaction count or transaction amount is greater than 10 million Turkish Liras and the user device is new, then raise an exception. The system sends an SMS to the user, and the user can choose to continue or not continue with the transaction.

Aggregations are very fast in our project since we started to use Spark. We can tell results in around 300 milliseconds. Before using Spark, the time was around 700 milliseconds. 

Before using Spark, we only used Couchbase. We needed fast results for this project because transactions come from various channels, and we need to decide and resolve them at the earliest because users are performing the transaction. If our result or process takes longer, users might stop or cancel their transactions, which means losing money. Therefore, fast results time is very important for us.

AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI. 

Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing.

I am a Java developer. I have been interested in Spark for around five years. We have been actively using it in our organization for almost a year.

It is the most stable platform. As compare to Flink, Spark is good, especially in terms of clusters and architecture. My colleagues who set up these clusters say that Spark is the easiest.

It is scalable, but we don't have the need to scale it. 

It is mainly used for reporting big data in our organization. All teams, especially the VR team, are using Spark for job execution and remote execution. I can say that 70% of users use Spark for reporting, calculations, and real-time operations. We are a very big company, and we have around a thousand people in IT.

We will continue its usage and develop more. We have kind of just started using it. We finished this project just three months ago. We are now trying to find out bottlenecks in our systems, and then we are ready to go.

We have not used Apache support. We have only used Cloudera support for this project, and they helped us a lot during the development cycle of this project. 

I don't have any idea about it. We are a big company, and we have another group for setting up Spark.

I would advise planning well before implementing this solution. In enterprise corporations like ours, there are a lot of policies. You should first find out your needs, and after that, you or your team should set it up based on your needs. If your needs change during development because of the business requirements, it will be very difficult. 

If you are clear about your needs, it is easier to set it up. If you know how Spark is used in your project, you have to define firewall rules and cluster needs. When you set up Spark, it should be ready for people's usage, especially for remote job execution. 

I would rate Apache Spark a nine out of ten.

Our primary use case is for interactively processing large volume of data.

We use Spark to process data from different data sources. 

In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, and do the transformation in a subsecond

I have been using Apache Spark for eight to nine years. 

It is a stable solution. The solution is ten out of ten on stability. 

The solution is highly scalable. All of the technical guys use Spark. Our product is used by many people within our customers' company.

The initial setup is straightforward. 

The solution is moderately priced. 

I rate the overall solution a ten out of ten. 

We use it as an ETL tool to gather information from different systems. The product is useful for analytics.

The product could improve the user interface and make it easier for new users. It has a steep learning curve.

I have been using the product for approximately three to four years. Currently, I am using the latest version.

The tool is stable. I rate the stability a ten out of ten.

The tool is very scalable. I rate the scalability a ten out of ten. Approximately 30 users are using Apache Spark in our organization.

We are using the free version of the product. So, we are not using any support.

Positive

The basic installation is easy. However, we are working in the security business and need a very secure installation. It has been quite difficult. I rate the basic installation a ten out of ten. I rate the ease of setup a two or three out of ten for a more secure installation with all the security features. The solution is deployed on-premises in our organization. The deployment process requires a couple of weeks.

We are using the free version of the solution.

I would recommend the product. I think it's a good solution for analytics. Overall, I rate the product an eight out of ten.

We use the product for real-time data analysis.

Apache Spark provides a very high-quality implementation of distributed data processing. I rate it 20 on a scale of one to ten.

There were some problems related to the product's compatibility with a few Python libraries. But I suppose they are fixed.

We have been using Apache Spark for the last two to three years.

I rate the product's stability a ten out of ten.

The product is enormously scalable.

The initial setup process is simple if you are a good professional. You have to select a few parameters and press enter. It is already integrated into Databricks platform. One person is enough to manage small and medium implementations.

It is an open-source platform. We do not pay for its subscription.

We are evaluating a few analytics engineering and DBT solutions. For now, Spark is in the secondary position.

I recommend Apache Spark for batch analytics features.

We are a software solutions company that serves a variety of industries, including banking, insurance, and industrial sectors. The product is specifically employed for managing data platforms for our customers.

The solution, as a package, excels across the board. I appreciate everything, not just one or two specific features.

The solution’s integration with other platforms should be improved.

I have been using the solution for the past eight years. Currently, I’m using the latest version of the solution.

The solution is highly stable. I rate it a perfect ten.

The solution is highly scalable. I rate it a perfect ten.

The initial setup was straightforward and was conducted on the cloud. The entire deployment process took just 15 minutes. The deployment process involves provisioning the computational part tool using Terraform.

The solution is affordable and there are no additional licensing costs.

I recommend using the solution. Overall, I rate the solution a perfect ten.

We use Spark for machine learning applications, clustering, and segmentation of customers.

Apache provides a lot of good documentation compared to other solutions. 

The migration of data between different versions could be improved. 

I've been using this solution for four years. 

The solution is stable. 

The solution is scalable. 

If you pay for customer support then you get a quick and efficient response, otherwise the community support offers good help. 

The initial setup has been simplified over the past few years and is now relatively straightforward. 

Licensing costs depend on where you source the solution. 

This is a good solution for big data use cases and I rate it eight out of 10.