Apache Hadoop Reviews

We used the product primarily for data analysis and storage. It helps handle large data sets, performing tasks like filtering, sorting, and joining. The platform is useful for data warehousing and provides distributed coordination and synchronization functionalities.

The solution has effectively supported our operations primarily due to its cost efficiency. It enables us to manage large data sets without incurring excessive subscription costs, resulting in more efficient data handling and operations.

The platform's most valuable feature is its low cost and open-source nature. It runs efficiently on commodity hardware and supports a large ecosystem of tools. Its flexibility in handling and storing large volumes of data is particularly beneficial, as is its resilience, which ensures data redundancy and fault tolerance.

Improvements in security measures would be beneficial, given the large volumes of data handled. Robust security features are essential to prevent data leaks or breaches. Additionally, integrating advanced capabilities similar to those other solutions would enhance the platform's functionality.

I have worked with Apache Hadoop for about six to seven months. The duration varied based on the projects I was involved in, as we often switched to different projects with different applications.

Although I have encountered some performance issues, the platform has proven to be stable. 

I would rate its stability as eight or nine.

This platform's scalability significantly impacts data management capabilities. It allows for simultaneously handling large data volumes, which other applications might struggle with.

I have contacted Apache tech support when encountering issues that could not be resolved internally. Their support is reliable and responsive.

Positive

The initial setup can be complex due to the need for precise coding and configuration. Setting up the required components and ensuring all dependencies are correctly configured is crucial for a successful deployment. Depending on network capability and system specifications, the setup typically takes 30 minutes to one hour if prerequisites are met. Maintenance involves regular updates to ensure the platform runs with the latest features and security patches.

The product is open-source, but some associated licensing fees depend on the subscription level. While it might be free for students, organizations typically need to pay for their subscriptions. The fees were reasonable for my usage, though I am not aware of recent changes to the pricing.

The product is highly effective for processing and managing large data sets. Integrating it with other solutions like AWS can provide additional functionalities, but the cost benefits of using this platform remain significant. I have also used the solution in AI-driven projects with machine learning models, and its integration with Apache Spark has been advantageous. I recommend it to organizations needing to handle large data sets due to its cost-effectiveness and robust capabilities.

I rate it a nine out of ten. 

I have been using the latest version of Apache Hadoop. It is a file system for data collection. There are nodes in this cluster that contain all the information, directories, and other files. The nodes are based on the MySQL database.

Hadoop isn't so problematic. It deals with file storage and maintenance. It is a network of file operations.

The stability of the solution needs improvement.

I have been using Apache Hadoop for more than three to four years.

There are some issues with file retention and its stability but they can be worked through. There are a lot of things that are based on disk space that require the preparation of different and sophisticated controls. The software itself is not unstable, but sometimes its options can cause stability issues.

The scalability includes adding nodes and it is not so easy to do. It is a detailed process that requires precision. 

There are almost 25 users, including data engineers and others, but no specialists. We plan to increase endpoint users and introduce running reports, automated reports, or reports based on some tools. 

Apache is an open source software and only has a community, instead of customer support. There is Cloudera which provides Apache Hadoop on license and offers support. In Cloudera, there are some consultants with less knowledge who offer support for small issues. As the case escalates, they provide more support with better technical expertise.

Positive

We checked a few solutions and tried solutions from Azure. There were pros and cons but this solution was more acceptable.

The setup depends on the data. Vast data can be hard to set up. You might have some issues with the setup, but it depends on the number of nodes. More nodes can cause issues and more time to resolve. The reshuffling is also complex and can cause problems.

The ROI is very hard to calculate. The source of data for the company can help to run different technologies and make many decisions based on the data, but it's very hard to calculate the return on investment.

I am not updated with the licensing cost, but you need to pay for a license if purchased from Cloudera.

If you plan to use Apache Hadoop, purchase the license from Cloudera because they provide you with technical support.

I rate the overall solution an eight out of ten. 

I use the solution in my company for security purposes.

In my company, we have intranet portals that we need to ensure are not accessible by outsiders. All the data that are within the internal applications is only accessible with valid credentials within the domain. In general, my company uses Apache Hadoop to secure our internal applications.

Tools like Apache Hadoop are knowledge-intensive in nature. Unlike other tools in the market currently, we cannot understand knowledge-intensive products straight away. To use Apache Hadoop, a person needs intensive knowledge, which is something that not everybody can get familiarized with in a straightforward manner. It would be beneficial if navigating through tools like Apache Hadoop is made user-friendly. For non-technical users, if the tool is made easy to navigate, it will be easier to use, and one may not have to depend on experts.

The load optimization capabilities of the product are an area of concern where improvements are required.

The complex setup phase can be made easier in the future.

I have four years of experience with Apache Hadoop.

The tool's stability is good.

I am not sure about the scalability features of the product.

There are around 500 users of the product in my company.

When there is a huge load or a huge number of people accessing the product simultaneously, there is a visible delay in the loading of pages.

The product's initial setup phase is complex.

I have not dealt with the setup phase straight away. I always like to rely on the infra person in my company who knows Apache Hadoop.

The solution is deployed on the cloud.

The product can be deployed with the help of the in-house infra team at my company.

There was a scenario when the product was essential for my company's data analytics needs. Before my company makes any web solution available in production, we have prototypes and replicas of the application in lower environments. My company uses Apache Hadoop to ensure that the lower environments in which we operate are secure and accessible only by those people in our company with valid credentials.

I suggest that those planning to use the product first understand the tool's features and capabilities and then choose the right configuration to avoid misconfigurations.

The product's integration capabilities are good since I see that we have not faced any time outs or downtime in our company when using the tool.

My company uses the tool to have security and the right availability, which means availability to the right people at the right time. So I think our expectation was met. The value we got from the tool was what we wanted in our company.

My company started to use the tool expecting that it would offer security and ensure its availability to the right people at the right time. I believe that the tool was able to meet our company's expectations, so we got the value that we expected the product to deliver.

I rate the tool a seven out of ten.

I'm from the data governance team, and this is how my team uses Apache Hadoop: there's a GUI called Apache Atlas, then there's an option called the "business glossary". My team uses the business glossary from Apache Atlas and also uses Apache Ranger. Apache Ranger is another GUI where you can check who is using which data source through the Apache Hadoop platform. My team also uses the Apache Hadoop platform for AI-related use cases and relevant data, so the data required from any kind of AI use case, that data is processed with ETL, specifically with the Talend tool. My team then loads the data in Apache Hadoop, uses that data by making some clusters, and uses the data for AI/ML cases.

What I like about Apache Hadoop is that it's for big data, in particular big data analysis, and it's the easier solution. I like the data processing feature for AI/ML use cases the most because some solutions allow me to collect data from relational databases, while Hadoop provides me with more options for newer technologies.

What could be improved in Apache Hadoop is its user-friendliness. It's not that user-friendly, but maybe it's because I'm new to it. Sometimes it feels so tough to use, but it could be because of two aspects: one is my incompetency, for example, I don't know about all the features of Apache Hadoop, or maybe it's because of the limitations of the platform. For example, my team is maintaining the business glossary in Apache Atlas, but if you want to change any settings at the GUI level, an advanced level of coding or programming needs to be done in the back end, so it's not user-friendly.

Apache Hadoop has good stability.

I'm not sure how scalable Apache Hadoop is.

In terms of technical support from Apache Hadoop, we are working with an external vendor and they are the ones helping us in every case. They are helpful.

We used Oracle Exadata before using Apache Hadoop. It was one or two years ago when we started using the Apache Hadoop platform. We're still thinking about using both platforms in parallel or choosing one of the two. We're still looking into the benefits of each platform, but currently, we're using both Oracle Exadata and Apache Hadoop.

I wasn't part of the team that set up Apache Hadoop, but using it after it was set up was very easy. The solution was ready immediately, and the GUI was smooth and fast, with no issues.

Apache Hadoop was implemented by the IT team, so it was an in-house implementation.

If my company can use the cloud version of Apache Hadoop, particularly the cloud storage feature, it would be easier and would cost less because an on-premises deployment has a higher cost during storage, for example, though I don't know exactly how much Apache Hadoop costs.

My company is using both Apache Hadoop and Oracle Exadata.

I'm unsure which version of Apache Hadoop I'm using, but it could be the latest version.

Currently, the solution is deployed on-premises because here in Bangladesh, there's a limitation with transferring data outside of the country. As far as I know, there's no cloud solution internally in Bangladesh, so if you want to use a cloud solution here, you'll have to move your data outside Bangladesh, and this is why Apache Hadoop is still deployed on-premises.

More than fifty people use Apache Hadoop directly, particularly the IT and analytics expert teams. The solution is being used by developers, people in operations, and people who maintain security.

In my company, Apache Hadoop is not fully implemented yet. It's still in the implementation phase and at least for the next two to three years, there isn't any plan of discarding it.

I'm giving Apache Hadoop a rating of seven out of ten.

I don't have any recommendations currently for people who want to implement Apache Hadoop because I'm still in the learning phase and I don't have much knowledge yet. The IT team in my company is also struggling every time in terms of preparing everything and still needs help from external vendors because the team isn't an expert on Apache Hadoop yet. My company's expertise is in Oracle Exadata because usage of that product started in 2002 or 2003.

My company is a customer of Apache Hadoop.

The solution helps to store and retrieve information.

Apache Hadoop is crucial in projects that save and retrieve data daily. Its valuable features are scalability and stability. It is easy to integrate with the existing infrastructure. 

I have been using the tool for a few years. 

I rate the tool's stability a nine out of ten. 

I take support from the DevOps team. 

I recommend the tool to others since it is good. 

We use Apache Hadoop for analytics purposes.

The ability to take a lot of data and attempt to basically deliver the appropriate splices and summary chart is the most crucial function that I have discovered. 

This stands in contrast to some of the other tools that are available, such as SQL and SAS, which are likely incapable of handling such a large volume of data. Even R, for instance, is unable to handle such data volumes. 

Apache Hadoop can manage large amounts and volumes of data with relative ease, which is a feature that is beneficial.

In terms of processing speed, I believe that some of this software as well as the Hadoop-linked software can be better. While analyzing massive amounts of data, you also want it to happen quickly. Faster processing speed is definitely an area for improvement.

I am not sure about the cloud's technical aspects, whether there are things that happen in the cloud architecture that essentially make it a little slow, but speed could be one. And, second, the Hadoop-linked programs and Hadoop-linked software that are available could do much more and much better in terms of UI and UX.

I mentioned it definitely, and this is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen. 

If the UI and UX can be improved slightly, I believe it will go a long way toward increasing adoption and effectiveness.

I have been using Apache Hadoop for six months.

It is far more stable than some of the other software that I have tried. It's also the current version of Hadoop software and is becoming increasingly more stable.

When a new version is released, the subsequent ones are always more stable and easier to use.

According to what I have seen in my current enterprise, once I joined the organization, it was fairly simple to have it for an employee, and this is true for everyone who's been onboarded in my own designation. I would imagine that it is fairly scalable across an enterprise.

I am fairly certain that we have between 10 and 15,000 employees who use it.

I have not had any direct experience with technical support.

We have an in-house technical support team that handles it.

I have since changed careers, I no longer use any automation tools, nor does my job need me to compare the capabilities of other tools.

I am working with Risk Analytic tools. I work with data these days, therefore I use technologies like Hive, Shiny R, and other data-intensive programs.

Shiny is a plugin that you can have on R. As a result of changing my profiles, I am now working in a position that is more data-centric and less focused on process automation.

We currently have proprietary tools, a proprietary cloud software, therefore I don't really need to employ any external cloud vendors. Aside from that, I only use the third-party technologies I've already indicated, primarily Hadoop and R.

This is one of the prime, one of the cornerstone software that we use. I have never been in a position to compare the like-for-like comparison with another software.

As it is proprietary software for the enterprise that I am currently working on, I had no trouble setting it up.

I am not sure about the price, but in terms of usability and utility of the software as a whole, I would rate it a three and a half to four out of five.

When I was a digital transformation consultant for my prior employer, I downloaded and read the reviews.

It involved learning about workflow automation tools as well as process automation. I looked at a number of these platforms as part of that, but I have never actually used them.

I would recommend this solution for data professionals who have to work hands-on with big data.

For instance, if you work with smaller or more finite data sets, that is, data sets that do not keep updating themselves, I would most likely recommend R or even Excel, where you can do a lot of analysis. However, for data professionals who work with large amounts of data, I would strongly recommend Hadoop. It's a little more technical, but it does the job.

I would rate Apache Hadoop an eight out of ten. I would like to see some improvements, but I appreciate the utility it provides.

Its main use case is to create a data warehouse or data lake, which is a collection of data from multiple product processors used by a banking organization. They have core banking, which has savings accounts or deposits as one system, and they have a CRM or customer information system. They also have a credit card system. All of them are separate systems in most cases, but there is a linkage between the data. So, the main motivation is to consolidate all that data in one place and link it wherever required so that it acts as a single version of the truth, which is used for management reporting, regulatory reporting, and various forms of analyses.

We have done two or three projects with Hadoop, and we have taken the latest version available at that time. So far, it was deployed on-premises.

The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable.

Another feature that I like is online analysis. In some cases, data requires online analysis. We like using Hadoop for that.

It requires a great deal of learning curve to understand. The overall Hadoop ecosystem has a large number of sub-products. There is ZooKeeper, and there are a whole lot of other things that are connected. In many cases, their functionalities are overlapping, and for a newcomer or our clients, it is very difficult to decide which of them to buy and which of them they don't really need. They require a consulting organization for it, which is good for organizations such as ours because that's what we do, but it is not easy for the end customers to gain so much knowledge and optimally use it. However, when it comes to power, I have nothing to say. It is really good.

We have been working with this solution for two and a half to three years.

The core file system and the offline data ingestion are extremely stable. In my experience, there is a bit less stability during online data ingestion. When you have incremental online data, sometimes it stops or aborts before finishing. It is rare, but it does, but the offline data injection and the basic processing are very stable.

Its scalability is very good. Most of our clients have used it on-prem. So, to a large extent, it is up to them to provide hardware for large data, which they have. Its scalability is linear. As long as the hardware is given to it, there are no complaints.

About 70% of its users are from a client's IT in terms of setting it up and providing support to make sure that the pipeline is there. Business users are about 30%. They are the people who use the analytics derived from the warehouse or data lake. Collectively, there are about 120 users. The size of the data is mostly in terms of the number of records it handles, which could be 30 or 40 million.

We have not dealt with them too many times. I would rate them a four out of five. There are no complaints.

Positive

Some of our clients are using Teradata, and some of them are using Hadoop.

After the hardware is available, getting the environment and software up and running has taken us a minimum of a week or 10 days. Sometimes, it has taken us longer, but usually, this is what it takes at the minimum to get everything up. It includes the downloads and also setting it up and making things work together to start using it.

For the original deployment, because there are so many components and not everyone knows everything pretty well, we have seen that we had to deploy four or five people in various areas at the initial deployment stage. However, once it is running, one or two people are required for maintenance.

Different clients derive different levels of return based on the sophistication of the analytics that they derive out of it and how they use it. I don't know how much ROI they have got, but I can say that some clients have not got a decent ROI, but some of our clients are happy with it. It is very much client-dependent.

We don't directly pay for it. Our clients pay for it, and they usually don't complain about the price. So, it is probably acceptable.

I would rate it a nine out of ten because of the complexity, but technically, it is okay.

Apache Hadoop's real-time data processing is weak and is not enough to satisfy our customers, so we may have to pick other products. We are continuously researching other solutions and other vendors.

Another weak point of this solution, technically speaking, is that it's very difficult to run and difficult to smoothly implement. Preparation and integration are important.

The integration of this solution with other data-related products and solutions, and having other functions, e.g. API connectivity, are what I want to see in the next release.

We've started using Apache Hadoop since 2011.

We selected Apache Hadoop because it is not dependent on third-party vendors. Previously, our main business unit was related to big vendors like IBM, Oracle, and EMC, etc. We wanted to have a competitive advantage in technology, so we selected the Apache project and used Apache open source.

The solution was implemented through a local vendor team here in Korea.

We evaluated IBM, Oracle, and EMC solutions.

My position in the company falls under the research and development of new technologies and solutions. I investigate, research, download, and read information and reports as part of my job.

Our company has a big data business division, and we propose, develop, and implement things which are related to big data projects. We are using Cloud Hadoop open source versions, distributed versions, and commercial Hadoop distributed versions. We propose all these versions to our customers from any industry.

Our focus is on the public sector. Big data is our strong point in Korea. Our company is the leader in big data technology, including infrastructure and visualization. This is a solution we provide to our customers. We are also in partnership with IBM. Our main focus is on Apache Hadoop.

We provide Apache Hadoop to our customers. I work for a systems integrator and technical consulting company.

Overall, our satisfaction with this solution is so-so. We continuously investigate new technologies and other solutions.

The Hadoop open source version was implemented in 95% of our company's customer base. Our remaining customers had the local vendor's Hadoop platform package implemented for them.

Our company is in the big data business. Before the big data business back in 1976, we implemented BI (business intelligence), DW (data warehouse), EIS, and DSS (decision support system), so we are in partnership with IBM.

I don't have advice for people looking into implementing this solution because I'm not in the business unit. I'm in the research field. My role is to plan new technology and provide consultation to our customers for big data projects in the early stages.

My rating for Apache Hadoop from a technical standpoint is eight out of ten.