Apache Spark Reviews

You can do a lot of things in terms of the transformation of data. You can store and transform and stream data. It's very useful and has many use cases.

Overall, it's a very nice tool.

It is great for transforming data and doing micro-streamings or micro-batching.

The product offers an open-source version.

The solution has been very stable.

The scalability is good.

Apache Spark is a huge tool. It has many use cases and is very flexible. You can use it with so many other platforms. 

Spark, as a tool, is easy to work with as you can work with Python, Scala, and Java.

If you are developing projects, and you need to not put them in a production scenario, you might need more than a cluster of servers, as it requires distributed computing.

It's not easy to install. You are typically dealing with a big data system.

It's not a simple, straightforward architecture. 

I've been using the solution for three years.

The stability is very good. There are no bugs or glitches and it doesn't crash or freeze. It's a reliable solution. 

We have found the scalability to be good. If your company needs to expand it, it can do so.

We have five people working on the solution currently.

There isn't really technical support for open source. You need to do your own studying. There are lots of places to find information. You can find details online, or in books, et cetera. There are even courses you can take that can help you understand Spark.

I also use Databricks, which I use in the cloud.

When handling big data systems, the installation is a bit difficult. When you need to deploy the systems, it's better to use services like Databricks.

I am not a professional admin. I am a developer for and design architecture.

You can use it in your standalone system, however, it's not the best way. It would be okay for little branch codes, not for production.

We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud. 

I have the solution installed on my computer and on our servers. You can use it on-premises or as a SaaS.

I'd rate the solution at a nine out of ten. I've been very pleased with its capabilities. 

I would recommend the solution for the people who need to deploy projects with streaming. If you have many different sources or different types of data, and you need to put everything in the same place - like a data lake - Spark, at this moment, has the right tools. It's an important solution for data science, for data detectors. You can put all of the information in one place with Spark.

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. 

The solution can be deployed on the cloud or on-premise.

We are using Apache Spark, for large volume interactive data analysis.

MechBot is an enterprise, one-click installation, trusted data excellence platform. Underneath, I am using Apache Spark, Kafka, Hadoop HDFS, and Elasticsearch.

Apache Spark can do large volume interactive data analysis.

Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn.

I have been using Apache Spark for approximately 11 years.

The solution is stable.

Apache Spark is scalable. However, it needs enormous technical skills to make it scalable. It is not a simple task.

We have approximately 20 people using this solution.

If you want to distribute Apache Spark in a certain way, it is simple. Not every engineer can do it. You need DevOps specialized skills on Spark is what is required.

If we are going to deploy the solution in a one-layer laptop installation, it is very straightforward, but this is not what someone is going to deploy in the production site.

Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free.

We are well versed in Spark, the version, the internal structure of Spark, and we know what exactly Spark is doing. 

The solution cannot be easier. Everything cannot be made simpler because it involves core data, computer science, pro-engineering, and not many people are actually aware of it.

I rate Apache Spark a six out of ten.

We have built a product called "NetBot." We take any form of data, large email data, image,  videos or transactional data and we transform unstructured textual data videos in their structured form into reading into transactional data and we create an enterprise-wide smart data grid. That smart data grid is being used by the downstream analytics tool. We also provide machine-building for people to get faster insight into their data. 

We use all the features. We use it for end-to-end. All of our data analysis and execution happens through Spark.

The features we find most valuable are the: 

• Machine learning
• Data learning
• Spark Analytics.

We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.

I have been using Apache Spark for more than five years. 

We haven't had any issues with stability so far. 

As long as you do it correctly, it is scalable.

Our users mostly consist of data analysts, engineers, data scientists, and DB admins.

Before using this solution we used Apache Storm. 

The initial setup is complex. 

We installed it ourselves. 

I would rate it a nine 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.

Our customers configure their software applications, and I use Apache to check them. We use it for data processing.

The data processing framework is good. The product is very useful.

The solution must improve its performance.

I have been using the solution for four to five years.

The tool is stable. I rate the stability more than nine out of ten.

We have a small business. Around four people in my organization use the solution.

The deployment was easy.

The solution was deployed with the help of third-party consultants.

Overall, I rate the product more than eight out of ten.

Ingesting billions of rows of data all day.

Spark on AWS is not that cost-effective as memory is expensive and you cannot customize hardware in AWS. If you want more memory, you have to pay for more CPUs too in AWS.

Powerful language.

It is like going back to the '80s for the complicated coding that is required to write efficient programs.

Predominantly, I use Spark for data analysis on top of datasets containing tens of millions of records.

I have an example. We had a single-threaded application that used to run for about four to five hours, but with Spark, it got reduced to under one hour.

The distribution of tasks, like the seamless map-reduce functionality, is quite impressive. For the user, it appears as simple single-line data manipulations, but behind the scenes, the executor pool intelligently distributes the map and reduce functions.

The visualization could be improved.

I have been working with Apache Spark for only a few months, not too long.

I haven't faced any stability issues. It has been stable in my experience.

When it comes to the scalability of Spark, it's primarily a processing engine, not a database engine. I haven't tested it extensively with large record sizes.

In my organization, quite a few people are using Spark. In my smaller team, there are only two users.

In terms of maintenance, when the load hits around 95%, we need to prioritize scripts and analysis within the team. 

We coordinate and prioritize based on the available resources. If there were self-service tools or better hand-holding for such situations, it would make things easier.

Currently, we extensively use pandas and Polaris. We are leveraging Docker and Kubernetes as a framework, along with AWS Batch for distribution. This is the closest substitute we have for Spark Distribution.

Both Docker and Kubernetes are more general-purpose solutions. If someone is already using Kubernetes and it's provided as a service, it can be used for special-purpose utilization, similar to Docker and Kubernetes.

In such cases, users may need to write the parallelization logic themselves, but it's relatively easy to onboard and start with a distributed load. Spark, on the other hand, is primarily used for special-purpose utilization. Users typically choose Spark when they have data-intensive tasks.

Another significant issue with Spark is its syntactics. For instance, if we have libraries like Panda or Polaris, we can run them single-threaded on a single core, or we can distribute them leveraging Kubernetes.

We don't need to rewrite that code base for Spark. However, if we are writing code specifically for Spark Executors, it will not be amenable to running it locally.

I would recommend understanding the use case better. Only if it fits your use case, then go for it. But it is a great tool.

Overall, I would rate Apache Spark an eight out of ten.