Apache Hadoop Reviews

As an example of a use case, when I was a contractor for Cisco, we were processing mobile network data and the volume was too big. RDBMS was not supporting anything. We started using the Hadoop framework to improve the process and get the results faster.

The data is stored in micro-partitions which makes the processes very fast compared to other RDBMS systems. Apache Spark is in the memory process, and it's much better than MapReduce.

Micro-partitions and the HDFS are both excellent features.

I'm not sure if I have any ideas as to how to improve the product.

Every year, the solution comes out with new features. Spark is one new feature, for example. If they could continue to release new helpful features, it will continue to increase the value of the solution.

The solution could always improve performance. This is a consistent requirement. Whenever you run it, there is always room for improvement in terms of performance.

The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning.

We would prefer it if users didn't just get pushed through to certification-based learning, as certifications are expensive. Maybe if they could arrange it so that the certification was at a lesser cost. The certification cost is currently around $2,500 or thereabout. 

I've been using the solution for four years.

We haven't had too many problems with stability. For the POC we used a small amount of data and we started with 10 nodes. We're gradually increasing in now to 40 nodes. We haven't seen any issues after the small teething period in the beginning. The configuration issues and the performance issues have subsided. Once we learned how to stack everything, it has been much better.

The solution is easy to expand. We haven't seen any issues with it in that sense. We've added 10 servers, and we've added two nodes. We've been expanding since we started using it since we started out so small. Companies that need to scale shouldn't have a problem doing so.

We are supporting a multitenancy model and we get the data on supporting the users. I would say, per organization, we have eight to 10 users and probably have a total of around 40 users across the board.

We started on the solution as a POC. Once we got into production, we had some minor issues. We get great support. They share advice and helped us tweak some things in terms of the configurations. We've been satisfied with the level of service we've been provided.

We have only ever used Apache Hadoop, or a version of it. When we looked for the commercial tier, there was Cloudera and Hortonworks. We started with the Hortonworks due to the fact that at that time we felt it was cost-effective. However, Cloudera bought Hadoop and Hortonworks and now it's all basically the same solution.  

The initial setup was a little complex the first time around. We were new to the system, and we didn't have any expertise at that time. Once we get some support and insights into how to work everything properly it went more smoothly.

First, we started with a POC - proof of concept. It takes a couple of days in terms of understanding and configuring everything, etc. When we went to production, it was a couple of hours for deployment and we put into practice everything we learned from the POC.

There's definitely a learning curve. It's stable for us now. 

We have a team of developers doing multiple tasks on the solution and few of them are taking care of Hadoop, so we do have a few people handling maintenance.

As we were new to the solution, we found we needed some outside assistance to guide us. However, that was for the POC. In the end, I did it myself. 

We're just a customer. We don't have a business relationship with Hadoop. 

My day-to-day job is data modeling and architecting.

Originally we used it as an open-source solution. We downloaded it, then we went for a commercial version of it.

In terms of advice, I'd tell other potential users that whether the solution is right for them depends on a few items. If the data volume is too big, it's IoT data, or the stream of data is too much, this solution can handle it and I would definitely recommend Apache Hadoop. 

Recently, in the last 18 months, I've been working with the Snowflake, it's a Data Lake project, and I am really impressed with that one. I got a certification so that we started using Snowflake set for our Data Lake environment.

I'd rate the solution eight out of ten.

We use it as a data lake for streaming analytical dashboards.

There is a lot of difference. I think the best case is that we are able to drill down to transactional records and really build a root-cause analysis for various issues that might arise, on demand. Because we're able to process in parallel, we don't have to wait for the big data warehouse engine. We process down what the data is and then build it up to an answer, and we can have an answer in an hour rather than 10 hours.

• Scalability
• Parallel processing

There are jobs that cannot be done unless you have massively parallel processing; for instance, processing call-detail records for telecom.

In general, Hadoop has as lot of different component parts to the platform - things like Hive and HBase - and they're all moving somewhat independently and somewhat in parallel. I think as you look to platforms in the cloud or into walled-garden concepts, like Cloudera or Azure, you see that the third-party can make sure all the components work together before they are used for business purposes. That reduces a layer of administration configuration and technical support.

I would like to see more direct integration of visualization applications.

In general, stability can be a challenge. It's hard to say what stability means. You're in an environment that's before production-line manufacturing, where none of the parts relate together exactly as they should. So that can create some instability.

To realize the benefit of these kinds of open-source, big-data environments, you want to use as many different tools as you can get. That brings with it all this overhead of making them work together. It's kind of a blessing and a curse, at the same time: There's a tool for everything.

Apache is the open-source foundation that Cloudera and Hortonworks contribute code and some work to. I don't know that there is actually support and structure, per se, for Apache.

We have had premium, at various times with various companies. From the three dominant companies I've worked with - Cloudera, Hortonworks, and MapR - there is a premium support package but that still only covers their base. Distribution is not necessarily all the add-ons that are on top of it, which is really a big challenge: to get everything to work together.

There are the older relational database technologies: Netezza, SQL Server, MySQL, Oracle, Teradata. All have some advantages and some disadvantages. Most notably, they are all significantly more expensive in terms of the capital expense, rather than the operational expense. They are "walled-garden," so to speak, that are curated and have a distinct set of tools that work with them, and not the bleeding-edge ingenuity that comes with an open-source platform.

Data warehousing is 30 years old, at least. Big data is, in its current form, has only been around for four or five years old.

There are capacities in which I have been responsible for setup, administration, and building the applications on those environments. Each of the components is relatively straightforward. The complexity comes from all the different components.

Implement for defined use cases. Don't expect it to all just work very easily.

I would rate this platform a seven out of 10. On the one hand, it's the only place you can use certain functions, and on the other hand, it's not going to put any of the other ones out of business. It's really more of a complement. There is no fundamental battle between relational databases and Hadoop.

The product's distributed computing capability is the most effective. It allows us to distribute data processing tasks across multiple nodes, significantly speeding up processing time.

The product's availability of comprehensive training materials could be improved for faster onboarding and skill development among team members.

I've been working with Apache Hadoop for about four years now.

We encounter occasional issues like memory constraints during extensive data processing. They have been manageable through scaling adjustments. 

I rate the stability an eight or nine. 

Hadoop's scalability can be rated a nine out of ten due to its exceptional flexibility, allowing horizontal scaling by adding or removing nodes as required.

Technical support has been decent, although there can be delays in resolving new or complex issues.

Neutral

The product deployment process was quite straightforward, taking only a few minutes for installation.

The platform has resulted in significant cost savings on compute resources by optimizing usage based on workload demands.

I would rate the product's subscription-based pricing a six out of ten. It's reasonable, but there's room for improvement in cost-effectiveness.

The platform's quick data processing capabilities have been instrumental in supporting our AI-driven projects. I would recommend it, especially for organizations dealing with large-scale data processing and needing robust distributed computing capabilities. 

I would rate Apache Hadoop an eight out of ten.

I use Hadoop as a data lake in an AIML solution, where it connects to various data sources and ingests data into Hadoop. It is utilized for processing large data volumes with various data sources such as RDBMS, file systems, Kafka for real-time streaming data, IoT, web sockets, and API metadata.

Hadoop provides a robust data lake functionality, allowing for the ingestion and processing of varied data types. It ensures no data loss through data replication and efficient transformation jobs handled in parallel, enhancing data analytics.

Hadoop can store any kind of data—structured, unstructured, and semi-structured—and presents it using the relational model through Hive. The combination with Spark enhances data analytics capabilities.

Hadoop lacks OLAP capabilities. I recommend adding a Delta Lake feature to make the data compatible with ACID properties. Also, video and audio streaming import issues could be improved to ensure proper data validation.

I have been working with Apache Hadoop for the last ten years.

The stability of Hadoop is good. I have been working with it for the last ten years and have not encountered significant stability issues.

Hadoop offers good scalability with horizontal scaling, especially when deployed on cloud platforms like Cloudera, which takes care of scaling the infrastructure. On-premises requires the maintenance of data nodes.

I rate the customer support at eight out of ten. It is satisfactory.

Positive

I have worked with traditional RDBMS which do not support analytics, querying, and data replication as efficiently as Hadoop.

Setting up Apache Hadoop requires some learning curve and is of medium complexity. I rate the setup a seven out of ten.

We need provision for the cluster deployment, including a master node, coordinator node, and the setup of Spark and Hive for development.

I have also worked with HDFS, Hive, and Apache Spark with Scala and Java.

As cloud technology is emerging, it is advisable to transition from traditional Hadoop to cloud-based solutions like AWS EMR and Azure, which offer better maintenance-free infrastructure management.

I'd rate the solution seven out of ten.

Big Data analytics, customer incubation. 

We host our Big Data analytics "lab" on Amazon EC2. Customers are new to Big Data analytics so we do proofs of concept for them in this lab. Customers bring historical, structured data, or IoT data, or a blend of both. We ingest data from these sources into the Hadoop environment, build the analytics solution on top, and prove the value and define the roadmap for customers.

Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges. 

We were using MySQL and PostgreSQL for these engagements, and scaling and processing were not as easy when compared to Hadoop. Also, customers who are embarking on a big journey with semi-structured information prefer to use Hadoop rather than a RDBMS stack. This gives them clarity on the requirements.

In addition, since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done.

Flexibility, ease of data processing, reduced cost and efforts are the three key improvements for us.

HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect.

Based on our needs, we would like to see a tool for data visualization and enhanced Ambari for management, plus a pre-built IoT hub/model. These would reduce our efforts and the time needed to prove to a customer that this will help them.

We have a three-node cluster running on cloud by default, and it has been stable so far without any stoppages due to Hadoop or other ecosystem components.

Since this is primarily for customer incubation, there is a need to process huge volumes of data, based on the proof of value engagement. During these processes, we scale the number of instances on demand (using Amazon spot instances), use them for a defined period, and scale down when the PoC is done. This gives us good flexibility and we pay only for usage.

Since this is mostly community driven, we get a lot of input from the forums and our in-house staff who are skilled in doing the job. So far, most of the issues we have had during setup or scaling have primarily been on the infrastructure side and not on the stack. For most of the problems we get answers from the community forums.

We didn't have any major issues except for knowledge, so we hired the right person who had hands-on experience with this stack, and worked with the cloud provider to get the right mechanism for handling the stack.

General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error. In addition, the old PoCs which were migrated had issues in directly connecting to Hive. We had to build some user functions to handle that.

We normally do not suggest any specific distributions. When it comes to cloud, our suggestion would be to choose different types of instances offered by Amazon cloud, as we are technology partners of Amazon for cost savings. For all our PoCs, we stick to the default distribution.

None, as this stack is familiar to us and we were sure it could be used for such engagements without much hassle. Our primary criteria were the ability to migrate our existing RDBMS-based PoC and connectivity via our ETL and visualization tool. On top of that, support for semi-structured data for ELT. All three of these criteria were a fit with this stack.

Our general suggestion to any customer is not to blindly look and compare different options. Rather, list the exact business needs - current and future - and then prepare a matrix to see product capabilities and evaluate costs and other compliance factors for that specific enterprise.

This solution is used for a variety of purposes, including managing enterprise data hubs, monitoring network quality, implementing an AntiFraud system, and establishing a conveyor system.

The most valuable feature is scalability and the possibility to work with major information and open source capability.

The solution is not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data.

I have been using Apache Hadoop for ten years. Initially, we worked directly, but now we use Cloudera and Bigtop. We are the solution provider.

The tool's stability is good. 

We may have 15 people working on this solution.

I rate the solution’s scalability a ten out of ten.

The setup is not easy for a financial or telecom company.

It takes around one month for basic development and around three to four months for enterprise. We require more than 50 engineers to do the engineering stuff and more than 20 If for the data engineering team.

In terms of production, the most significant aspects are security and staging, with a focus on either a one-month or three-month timeframe for security considerations.

The best advice is not to start a project based on Apache Hadoop alone. It is based on technology, and needs a skilled team.

Overall, I rate the solution an eight out of ten.

We use the Apache Hadoop environment for use cases involving big data engineering. We have many applications, such as collecting, transforming, loading, and storing lag event data for big organizations.

Its integration is Hadoop's best feature because that allows us to support different tools in a big data platform. Hadoop can integrate all of these features in various environments and have use cases beyond all of the tools in the environment.

It could be more user-friendly. Other platforms, such as Cloudera, used for big data, are more user-friendly and presented in a more straightforward way. They are also more flexible than Hadoop. Hadoop's scrollback is not easy to use, either.

I have used Apache Hadoop for three years, and I use Hadoop's open-source version.

Hadoop is stable because it's run on a cluster. And if some issues occur for a range of servers, Hadoop could continue its activity.

Apache Hadoop is very good for scalability because one of its main features is its scalability tool. For all the big data infrastructure, we have about ten employees working in the Hadoop environment as engineers and developers. One of our clients is a bank, and the Hadoop environment can retrieve a lot of data, so we could have an unlimited number of end users.

The initial setup is, to some extent, difficult because additional skills are required, specifically knowledge of the operating system at installation. We need someone with professional skills to install the Hadoop environment. With one engineer with those skills, Hadoop takes ten days to two weeks to deploy the solution.

Two or three people are needed to maintain the solution. At least two people are required to maintain the Hadoop stack, in case of unexpected situations, like when something gets corrupted, and they need to solve the problem as fast as possible. Hadoop is easy to maintain because of its governance feature, which helps maintain all the Hadoop stacks.

Some competitors include Kibana from Elasticsearch, Splunk, and Cloudera. Each of them has some advantages and disadvantages, but Hadoop is more flexible when working in a big data environment. Compared to Splunk and Cloudera, Apache Hadoop is platform-independent and works on any platform. It is also open-source.

We use Hadoop's open-source version and do not receive direct support from Apache. There are good resources on the web, though, so we have no problem getting help, but not directly from the company.

If you want to use big data on a larger scale, you should use Hadoop. But you could use alternatives if you're going to use big data to analyze data in the short term and don't need cybersecurity. You could use your cloud's features. For example, if you are on Google or Amazon Cloud, you could use in-built features instead of Apache Hadoop. If you are, like us, working with banks that don't want to use the cloud or some commercial clouds or have large-scale data, Hadoop is a good choice for you.

I rate Apache Hadoop an eight out of ten because it could be more user-friendly and easier to install. Also, Hadoop has changed some features in the commercial version.

The primary use case of this solution is data engineering and data files.

The deployment model we are using is private, on-premises.

We don't use many of the Hadoop features, like Pig, or Sqoop, but what I like most is using the Ambari feature. You have to use Ambari otherwise it is very difficult to configure.

What comes with the standard setup is what we mostly use, but Ambari is the most important.

Hadoop itself is quite complex, especially if you want it running on a single machine, so to get it set up is a big mission.

It seems that Hadoop is on it's way out and Spark is the way to go. You can run Spark on a single machine and it's easier to setup.

In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency. I don't think that this is viable, but if it is possible, then latency on smaller guide queries for analysis and analytics.

I would like a smaller version that can be run on a local machine. There are installations that do that but are quite difficult, so I would say a smaller version that is easy to install and explore would be an improvement.

This solution is stable but sometimes starting up can be quite a mission. With a full proper setup, it's fine, but it's a lot of work to look after, and to startup and shutdown.

This solution is scalable, and I can scale it almost indefinitely.

We have approximately two thousand users, half of the users are using it directly and another thousand using the products and systems running on it. Fifty are data engineers, fifteen direct appliances, and the rest are business users.

There are several forums on the web, and Google search works fine. There is a lot of information available and it often works.

They also have good support in regards to the implementation.

I am satisfied with the support. Generally, there is good support.

We used the more traditional database solutions such as SAP IQ  and Data Marks, but now it's changing more towards Data Science and Big Data.

We are a smaller infrastructure, so that's how we are set up.

The initial setup is quite complex if you have to set it up yourself. Ambari makes it much easier, but on the cloud or local machines, it's quite a process.

It took at least a day to set it up.

I did not use a vendor. I implemented it myself on the cloud with my local machine.

There was an evaluation, but it was a decision to implement with Data Lake and Hortonworks data platform.

It's good for what is meant to do, a lot of big data, but it's not as good for low latency applications.

If you have to perform quick queries on naive or analytics it can be frustrating.

It can be useful for what it was intended to be used for.

I would rate this solution a seven out of ten.