Apache Spark Reviews

I use the solution for data lakes and big data solutions. I can combine it with the other program languages.

One of the reasons we use Spark is so we can use parallelism in data lakes. So in our case, we can get many data nodes, and the main power of Hadoop and big data solutions is the number of nodes usable for different operations. It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance. Also, Spark has an option for near real-time loading and processing. We use micro batches of Spark.

If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation. In combination with other tools, many sessions remain, even if you think they've stopped. This is the main problem with big data sessions, where zombie sessions reside that you have to take care of. Otherwise, they spend resources and cause problems.

I've been using Apache Spark for more than two years. I'm using the latest version.

The solution is stable, but not completely. For example, we use Exadata as an extremely stable data warehouse, but that's not possible with big data. There are things that you have to fix sometimes. The stability is similar to the cloud solution, but that depends on the solution you need.

The solution is scalable, but adding new nodes is not easy. It will take some time to do that, but it's scalable. We have about 20 users using Apache Spark. We regularly use the solution.

We use Cloudera distribution, so we ask Cloudera for support, which is not open-source.

When you install the complete environment, you install Spark as a part of this solution. The setup can be tricky when introducing security, such as connecting Spark using Kerberos. It can be tricky because when you use it, you have to distribute your architecture with many servers, and even then, you have to prepare Kerberos on every server. It's not possible to do this in one place.

Deploying Apache Spark is pretty complex. But that is a problem with the security approach. Our security guys requested this security, so we use Kerberos authentication mandatorily, which can be complex. We had five people for maintenance and deployment, not to mention deployment or other roles.

We had an external integrator, but we also had in-house knowledge. Sometimes, we need to change or install something, and it's not good to ask the integrator for everything because of availability and planning. We had more freedom thanks to our internal knowledge.

Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera. But in that case, you don't have any support. If you face a problem, you might find something in the community, but you cannot ask Cloudera about it. If you have open source, you don't have support, but you have a community. Cloudera has different packages, which are licensed versions of products like Apache Spark. In this case, you can ask Cloudera for everything.

Spark was written in Scala. Scala is a programming language fundamentally in Java and useful for data lakes.

We thought about using Flink instead, but it wasn't useful for us and wouldn't gain any additional value. Besides, Spark's community is much wider, so information is available and is better than Flink's.

I rate Apache Spark an eight out of ten.

If you plan to implement Apache Spark on a large-scale system, you should learn to use parallelism, partitioning, and everything from the physical level to get the best performance from Spark. And it will be good to know Python, especially for data scientists using PySpark for analysis. Likewise, it's good to know Scala because you can be very efficient in preparing some datasets since it is Spark's native language.

On-premises

I use Apache Spark for any data engineering part. I handle some computation processes where it is necessary to process big data.

Apache Spark's ability to handle both batch and streaming data is the most valuable feature for me. It is beneficial for consuming real-time data. It offers solid real-time processing capability, making it more efficient in managing data analytics. It is beneficial as it allows processing of both batch and streaming data seamlessly.

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, worker nodes, stages, and tasks. Additionally, clustering may be a bit complex to set up.

I have been using Apache Spark for about two and a half years now.

Clustering may be a bit complex to set up, but it depends on the experience that the person involved has.

I find Apache Spark to be fine and stable.

The scalability of Apache Spark depends on the number of machines being used. By adjusting the worker and driver nodes, scaling can be leveraged.

I haven't tried Apache Spark's official support. I mostly use ChatGPT for assistance.

Neutral

Before Spark, I used solutions like Storm and Flume. In AWS, it is Kinesis. Both Kinesis and Spark have their own ways of managing data injection and compute.

In the public cloud, it comes with built-in services, but for on-premises, I have to spin up my own cluster using my ecosystem.

A single person can handle installation if they are capable enough.

Timing depends on the cluster being used, such as how many compute nodes and the kind of data there is. So, it's not straightforward to specify the percentage of time and money saved.

Apache Spark is open-source, so it doesn't incur any charges.

I used Storm, Flume, and AWS Kinesis.

I rate my overall experience with Apache Spark as eight out of ten. I suggest leveraging AI capabilities to enhance performance or check for anomalies.

Public Cloud

Other

The primary use case for Apache Spark is to process data in memory, using big data, and distributing the engine to process said data. It is used for various tasks such as running the association rules algorithm in ML Spark ML, running XGBoost in parallel using the Spark engine, and preparing data for online machine learning using Spark Streaming mode.

The most significant cost savings come from the operational side because Spark is very typical in operations. There are many experts available in the market to operate Spark, making it easier to find the right personnel. It is quite mature, which reduces operation costs.

The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features. This allows running Pandas code distributed by using the Spark engine, which is a crucial feature. The integration with Pandas syntax in distributed mode, along with the user-defined functions in PySpark, is particularly valuable.

The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Consequently, alternatives like Doc DB are more preferable. Additionally, performance in some cases is slower, making alternatives two to five times faster.

I have more than ten years of experience using Spark, starting from when it was first introduced.

Spark is very stable for our needs. It offers amazing stability.

Scalability depends on how infrastructure is organized. Better balance and network considerations are necessary. However, Spark is very stable when scaled appropriately.

Customer support for Apache Spark is very good. There is a lot of documentation and forums available, making it easier to find solutions. The Databricks team also does a lot to support Spark.

Positive

The initial setup of Spark can take about a week, assuming the right infrastructure is already in place.

A few technicians are typically required for installation and configuration. SRE engineers or operational guys handle the setup, as they need to understand the details about installation and configuration. Maintenance usually requires just an SRE engineer or operational guy.

The main benefit in terms of ROI comes from the operation side. Spark’s operational costs are lower due to the availability of experts and its maturity. However, performance costs might be higher due to the need for more memory and infrastructure.

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud solutions like Databricks can simplify the process, they may also be less cost-efficient.

I'd rate the solution eight out of ten. 

Hybrid Cloud

Other

We use the solution to extract data from our sensors.  We have lots of data streaming into our system, which used to get overwhelmed. We use Apache Spark to handle real-time streaming and do machine learning to predict supply and demand in the market and adjust operations.

I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems.

The tool's real-time processing has had a big impact. We used to get data from sensors after a month. We get it in less than 10 minutes, which helps us take quick action.

We use Apache Spark to map our data pipelines using MapReduce technology. We're also working on integrating tools like Hive with Apache Spark to distribute our data processing. We can also integrate other tools like Apache Kafka and Hadoop.

We faced some challenges when integrating the solution into our existing system, but good documentation helped solve them.

For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial.

I have been working with the product for five years. 

Apache Spark is stable. 

We're a big company with about 4 million consumers. We handle huge amounts of data—around 30,000 sensors send data every 15 minutes, which adds up to 5-10 terabytes per day.

Before Apache Spark, we had a different solution - a traditional system with one server handling everything, more like a data warehouse. We switched to Apache Spark because we needed real-time visibility in our operations.

The initial setup process was challenging. We tried to do it ourselves at first, but we weren't used to distributed computing systems, creating nodes, and distributing data. Later, we engaged consulting groups that specialized in it. This is why there's a specific learning curve—it would be challenging for a company to start alone.

The initial deployment took us about six to eight months. We started with three people involved in the deployment process and later increased to five. From a maintenance point of view, it's pretty smooth now. It's not difficult to maintain and doesn't require much maintenance.

The tool has helped us reduce costs that run into billions of dollars yearly. The ROI is very significant for us.

We did evaluate other options. We started by looking at open-source Hadoop deployment, thinking we'd bring data into HDFS and do machine learning separately. But that would have been a hassle, so Apache Spark was a better fit.

I rate the overall solution a seven out of ten. I would recommend Apache Spark to other users, but it depends on their use cases. I advise new users to get an expert involved from the start.

On-premises

We're using Apache Spark primarily to build ETL pipelines. This involves transforming data and loading it into our data warehouse. Additionally, we're working with Delta Lake file formats to manage the contents.

The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily.

Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial.

I have been using the product for six years. 

Apache Spark is generally considered a stable product, with rare instances of breaking down. Issues may arise in sudden increases in data volume, leading to memory errors, but these can typically be managed with autoscaling clusters. Additionally, schema changes or irregularities in streaming data may pose challenges, but these could be addressed in future software versions.

About 70-80 percent of employees in my company use the product. 

We haven't contacted Apache Spark support directly because it's an open-source tool. However, when using it as a product within Databricks, we've contacted Databricks support for assistance.

The main reason our company opted for the product is its capability to process large volumes of data. While other options like Snowflake offer some advantages, they may have limitations regarding custom logic or modifications.

The solution's setup and installation of Apache Spark can vary in complexity depending on whether it's done in a standalone or cluster environment. The process is generally more straightforward in a standalone setup, especially if you're familiar with the concepts involved. However, setting up in a cluster environment may require more knowledge about clusters and networking, making it potentially more complex.

The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks.

If you're new to Apache Spark, the best way to learn is by using the Databricks Community Edition. It provides a cluster for Apache Spark where you can learn and test. I rate the product an eight out of ten.

Amazon Web Services (AWS)

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.

In my company, the solution is used for batch processing or real-time processing.

The product has matured at the moment. The product's interoperability is an area of concern where improvements are required.

Apache Spark can be integrated with high-tech tools like Informatica. Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users.

I have been using Apache Spark for three years.

Stability-wise, I rate the solution a nine out of ten.

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

There are no different numbers of uses for Apache Spark in my company since it is used as a processing engine.

Apache Spark is an open-source tool, so the only support users can get for the tool is from different vendors like Cloudera or HPE.

In the past, my company has used certain ETL tools, like Informatica, based on the performance levels offered.

The deployment of the product is easy.

Apache Spark's cluster deployment process is very easy.

There is only a deployment process required for an application to run on Apache Spark. Apache Spark itself is a setup tool. Deploying an application using Apache Spark is easy as a user since you just need to submit the code in Scala and submit it to the cluster, and then the deployment process can be done in one step.

The solution is deployed on an on-premises model.

Apache Spark is an open-source tool. It is not an expensive product.

The tool is used for real-time data analytics as it is very powerful and reliable. The code that you write with Apache Spark provides stability. There are many bugs that can appear according to the code that you use, which could be Java or Scala. So this is amazing. Apache Spark is very reliable, powerful, and fast as an engine. When compared with another competitor like MapReduce, Apache Spark performs 100 times better than MapReduce.

The monitoring part of the product is good.

The product offers clusters that are resilient and can run into multiple nodes.

The tool can run with multiple clusters.

The integration capabilities of the product with other platforms to improve our company's workflow are good.

In terms of the improvements in the product in the data analysis area, new libraries have been launched to support AI and machine learning.

My company is able to process huge datasets with Apache Spark. There is a huge value added to the organization because of the tool's ability to process huge datasets.

I rate the overall solution a nine out of ten.

On-premises

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.

Hybrid Cloud