Apache Spark Reviews

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. 

In AI deployment, a key step is aggregating data from various sources, such as customer websites, debt records, and asset information. Apache Spark plays a vital role in this process, efficiently handling continuous streams of data. Its capability enables seamless gathering and feeding of diverse data into the system, facilitating effective processing and analysis for generating alerts and insights, particularly in scenarios like banking. 

The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations.

It requires overcoming a significant learning curve due to its robust and feature-rich nature.

We have been using it for two years now.

It provides excellent stability. We never faced any issues with it.

It ensures outstanding scalability capabilities.

Opting for Apache Spark, an open-source solution, provides a distinct advantage by offering control over the code. This means you can identify issues, make necessary fixes, and determine what aspects to accept as they are. In contrast, dealing with a vendor may limit control, requiring you to submit requests and advocate for changes based on your business volume with them. This dependency on volume can potentially compromise control. To safeguard both your customers and your business, the choice of an open-source solution like Apache Spark allows for more autonomy and control over the technology stack.

The system's smooth operation relies on deploying a comprehensive container with Kubernetes clusters, configured with essential toolsets. Instrumentation data from the backend is fed back to a central framework equipped with specific tools for driving various processes. In a case involving a customer with Red Hat and Postini clusters, the OpenShift Container Platform, comprising Kubernetes clusters, is used. The tools manage onboarding, infrastructure provisioning, certificate management, authorization control, etc. The deployment spans multiple independent data centers, like telecom circles in India, requiring unique approaches for various tasks, including disaster recovery planning and central alerting, facilitated through SaaS. The deployment process typically takes approximately forty to forty-five days for six thousand servers.

It provides a dual advantage by saving both time and money while enhancing performance, particularly by leveraging my skill sets. 

It is an open-source solution, it is free of charge.

I would give it a rating of seven out of ten, which, by my standards, is quite high.

I use Spark to run automation processes driven by data.

Apache Spark helped us with horizontal scalability and cost optimizations.

The most valuable feature is the grid computing.

An area for improvement is that when we start the solution and declare the maximum number of nodes, the process is shared, which is a problem in some cases. It would be useful to be able to change this parameter in real-time rather than having to stop the solution and restart with a higher number of nodes.

I've been using Spark for around four years.

The initial setup was not easy, but we created a means of asking the user about their needs, making the setup much easier. We can now deploy the platform in thirty minutes using the public cloud or Kubernetes space.

Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term.

Spark is an open-source solution, so there are no licensing costs. 

I would rate Apache Spark eight out of ten.

We use this solution for it's anti-money laundering and direct marketing features within a banking environment.

This solution provides a clear and convenient syntax for our analytical tasks.

This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed.

There is also limited Python compatibility, which should be improved.

We have used this solution for around seven years, through several versions.

We have found this solution to be stable during our time using it.

This is a very scalable solution from our experience.

We implemented the solution using our in-house team, but the UI was developed using a third party contractor.

The deployment time of this solution is dependent on the requirements of an organization, and the compatibility of the systems they will be using alongside this solution. We would recommend that these are clearly defined when designing the product for the businesses needs.

I would rate this solution a nine out of ten.

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

Spark has been at the forefront of data processing engine. I have used Apache Spark for multiple projects for different clients. It is an excellent tool to process massive amount of data. 

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.

I have been using this solution for around 7 years.

There were bugs three to four years ago, which have been resolved. There were a couple of issues related to slowness when we did a lot of transformations using the Width columns. I was writing a POC on ETL for moving from Informatica to Spark SQL for the ETL pipeline. It required the use of hundreds of Width columns to change the column name or add some transformation, which made it slow. It happened in versions prior to version 1.6, and it seems that this issue has been fixed later on.

It is very scalable. You can scale it a lot.

I haven't contacted them.

The initial setup was a little complex when I was using open-source Spark. I was doing a POC in the on-premise environment, and the initial setup was a little cumbersome. It required a lot of set up on Unix systems. We also had to do a lot of configurations and install a lot of things. 

After I moved to the Cloudera CDH version, it was a little easy. It is a bundled product, so you just install whatever you want and use it.

Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.

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.

My main task is working on predictive analytics, and Apache Spark is one of the tools that I utilize in this role. Primarily, we work with the predictive analysis of very large amounts of data.

Apache Spark is also helpful for data pre-processing, including data cleaning.

This solution is cloud-agnostic. You can use it with an EC2 instance and you can even install it on-premises. Some environments have it installed in VMs.

One of the key features is that Apache Spark is a distributed computing framework. You can have multiple slaves and distribute the workload between them.

Another feature is memory-based computing. This is unlike Hadoop, which relies on storage. As it uses in-memory data processing, Spark is very fast.

When you are working with large, complex tasks, the garbage collection process is slow and affects performance. This is an area where they need to improve because your job may fail if it is stuck for a long time while memory garbage collection is happening. This is the main problem that we have.

I have been working with Apache Spark for the past four years.

This product is pretty stable. Companies like Facebook, Uber, and Netflix are all using Apache Spark. It's stable enough to be used all over the world.

In our team that works on this, we have approximately 10 people.

There is no official support for this solution. Because it's open-source and there is no cost involved, there is nobody to contact for support. Our own internal team of experts, which work on different problems, both support and contribute to the platform.

I work on several open-source frameworks including Python, Scikit-learn, TensorFlow, PyTorch, H20.ai, and R. We don't endorse proprietary tools so we aren't working with them.

With respect to the initial setup, it's neither easy nor very difficult. Our team has experience so it is not difficult for them. However, for a person that is new to using it, the setup might be very difficult.

We have a team of experts in my company, and they handle it very well.

This is an open-source tool, so it can be used free of charge. There is no cost involved.

We are not using the current version of this platform, Spark 3. However, we do know that it is used in the market and it has new features. We will eventually move to it.

My advice for anybody who wants to use Apache Spark is that they have two options. The first is Databricks, which are the creators of Apache Spark, and use their proprietary version. If you choose this option then you will have to pay for the product.

If instead, you use Apache Spark, then you can rely on your own expert in-house team for support, maintenance, and deployment. In this option, you don't have to pay anything to anybody outside of your company.

I would rate this solution an eight out of ten.

I mainly use Spark to prepare data for processing because it has APIs for data evaluation. 

The most valuable feature is that Spark uses Scala, which has good data evaluation functions. Spark also supports good distribution on the clusters and provides optimization on the APIs.

Spark could be improved by adding support for other open-source storage layers than Delta Lake. The UI could also be enhanced to give more data on resource management.

I've been using Spark for six years.

Generally, Spark works correctly without any errors. It may give out some errors if your data changes, but in that case, it's a problem with the configuration, not with Spark.

The cloud version of Spark is very easy to scale.

The initial setup is not complex, but it depends on the product's component on the architecture. For example, if you use Hadoop, setup may not be easy. Deployment takes about a week, but the Spark cluster can be installed in the virtual architecture in a day.

Spark can handle small to huge data and is suitable for any size of company. I would rate Spark as eight out of ten.