Apache Spark Reviews

We use the product in our environment for data processing and performing Data Definition Language (DDL) operations.

The product’s most valuable features are lazy evaluation and workload distribution.

They could improve the issues related to programming language for the platform. 

We have been using Apache Spark for around two and a half years.

The platform’s stability depends on how effectively we write the code. We encountered a few issues related to programming languages.

We have more than 100 Apache Spark users in our organization.

Before choosing Apache Spark for processing big data, we evaluated another option, Hadoop. However, Spark emerged as a superior choice comparatively.

The initial setup complexity depends on whether it's on the cloud or on-premise. For cloud deployments, especially using platforms like Databricks, the process is straightforward and can be configured with ease. However, if the deployment is on-premise, the setup tends to be more time-consuming, although not overly complex.

They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.

Apache Spark is a good product for processing large volumes of data compared to other distributed systems. It provides efficient integration with Hadoop and other platforms.

I rate it a ten out of ten.

It's a root product that we use in our pipeline.

We have some input data. For example, we have one system that supplies some data to MongoDB, for example, and we pull this data from MongoDB, enrich this data from other systems - with some additional fields - and write to S3 for other systems. Since we have a lot of data, we need a parallel process that runs hourly.

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. 

In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000.

The solution is scalable.

It's a stable product.

The primary language for developers on Spark is Scala. Now it's also about Java. I prefer Java versus Scala, and since they are supported, it is good. I know there is always discussion about which language to write applications in, and some people do love Scala. However, I don't like it.

They use currently have a JDK version which is a little bit old. Not all features are on it. Maybe they should pull support of the JDK version.

I've used the solution for a year and a half. 

The solution is stable. There are no bugs or glitches. It doesn't crash or freeze. 

The product scales well. It's fine to expand if needed. 

Many teams use Spark. For example, we have a few kinds of pipelines, huge pipelines. One of them processes 300 billion events each day. It's our core technology currently.

We do not plan to increase usage. We keep our legacy system on Spark, and we are now discussing Flink and Spark and what we would prefer. However, most of the people are already migrating new systems to Flink. We will keep Spark for a few more years still. 

We have an internal team, and they participate in process of developing Spark. They are Spark contributors, and if we have some problems, we turn to them. It's our own people, yet they work with Spark. Generally, if the problem is more minor, we look at some sites or have some discussion about Spark or internal guys who have experience with Spark. 

We also use Flink.

Before Spark, I worked with another company that we used some different technology, including Kafka, Radius, Postgres SQL, S3, and Spring. 

I didn't handle the initial setup. We were using this pipeline and clusters already. I just installed it on my local server. However, in terms of difficulty, I didn't see any problem. The deployment might only take a few hours. 

I found some documentation. I got the documentation from the site and downloaded the archive and unzipped it, and installed it. I can't say that I installed something from a special configuration. I just installed a few nodes for debugging and for running locally, and that's all. Also, in one case I used, for example, a Docker configuration with Spark. It all worked fine.

It's an open-source product. I don't know much about the licensing aspect. 

We have compared Flink and Spark as two possible options. 

I can recommend the product. It's a nice system for batch processing huge data.

I'd rate the solution eight out of ten. 

Apache Spark is used for data computation, building data pipelines, or building analytics on top of batch data. Apache Spark is used to handle big data efficiently.

The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast.

Apache Spark should add some resource management improvements to the algorithms. Thereby, the solution can manage SKUs more efficiently with a physical and logical plan over the different data sets when you are joining it.

I have been working with Apache Spark for six to seven years.

Apache Spark is a very stable solution. The community is still working on other parts, like performance and removing bottlenecks. However, from a stipulative point of view, the solution's stability is very good.

I rate Apache Spark a nine out of ten for stability.

Apache Spark is a scalable solution. More than 50 to 100 users are using the solution in our organization.

Apache Spark's technical support team responds on time.

Positive

The solution’s initial setup is very easy.

Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises.

I would recommend Apache Spark to users doing analytics, data computation, or pipelines.

Overall, I rate Apache Spark ten out of ten.

We use it for data engineering and analytics to process and examine extensive datasets.

It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained.

One limitation is that not all machine learning libraries and models support it. While libraries like Scikit-learn may work with some Spark-compatible models, not all machine-learning tools are compatible with Spark. In such cases, you may need to extract data from Spark and train your models on smaller datasets instead of directly using Spark for training.

I have been using it for four years.

I have not encountered any significant stability issues and it has proven to be a robust and reliable platform without major crashes. However, there have been instances where I needed to address query optimization and similar tasks to ensure optimal performance. I would rate it nine out of ten.

To rate my overall experience, I would give it an eight out of ten, leaving room for potential improvements in terms of technical support.

Positive

We used Pandas data frames and SQL-type queries for smaller datasets, but we haven't worked with anything on the scale of Spark SQL.

I haven't handled the deployment process, but setting it up on the cloud seems relatively straightforward.

Setting it up on-premises might take longer, potentially a couple of days. However, when deploying it on the cloud, the process can be significantly quicker, possibly taking only a few hours.

On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing. Managing costs in a cloud environment can be challenging due to the cumulative expenses associated with running and maintaining Spark. Licensing costs may not be the primary concern, but operational costs in the cloud can add up. For on-premises deployments, maintenance costs include cluster management, job optimization, and upgrades. In the cloud, maintenance costs are relatively lower, especially with managed database clusters, but they still exist and primarily revolve around cluster upkeep.

We evaluated Microsoft Synapse, which offers similar analytics functionality but not quite at the same scale as Apache Spark and Spark as a whole. While some tasks can be accomplished with Synapse on AWS, there are certain features and capabilities, such as micro-batching and scalability, that Spark excels at and remains unmatched.

Additional skill requirements are crucial to use the solution and its related features effectively. Training costs and efforts may be necessary to ensure individuals are proficient in using these technologies. Overall, I would rate it nine 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 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. 

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.