Apache Hadoop Reviews

The integration with Apache Hadoop with lots of different techniques within your business can be a challenge.

I have been using Apache Hadoop for approximately nine years.

 Apache Hadoop is stable.

The scalability of Apache Hadoop is very good.

The price of Apache Hadoop could be less expensive.

My advice to others is if you have a strong engineering team then this solution is excellent.

I rate Apache Hadoop an eight out of ten.

We use the solution as a data link for our customer payment and SaaS information. We get data from various sources and then utilize and leverage that data.

One valuable feature is that we can download data. Another is that it is a low-cost solution. Hadoop has also made it feasible to have all the data available in one area.

We have plans to increase usage and this is where we've realized that when we have all these clusters and we're running queries and analyzing, we are facing some latency issues. I think more of the solution needs to be focused around the panel processing and retrieval of data. 

I have been using this solution for about seven or eight years. 

This is a stable product. 

The scalability of the solution is good. Approximately 100 people are currently using this solution within our company. 

I would rate the tech support as a four out of five. 

Positive

I would recommend this product to others. I would rate it as an eight out of ten. 

There are several use cases for Hadoop. Sometimes it's used for data warehousing. Other times, it's analytics. And In some cases, it's used to do transformation. For example, I have one client using it to decompress, compress, or encrypt data on ingestion. So, he used it like an ETL engine.

Hadoop is extensible — it's elastic.

Hadoop's security could be better.

I've been using Hadoop for about eight years. I'm not sure exactly.

Performance is one of the reasons people choose Hadoop.

Scalability is one of Hadoop's strong suits.

I've never had to use Hadoop support. 

The complexity of Hadoop's setup depends on the customer and their needs. However, most of my customers wind up using Hadoop as a service, which makes it very easy. It doesn't need much maintenance. My staff maintains multiple systems, so it's not like there would ever be somebody dedicated to one, and Hadoop is not a high-touch platform.

I rate Hadoop seven out of 10. It's very good, but it could always be better. To anyone considering Hadoop, I recommend that you be mindful of what you're trying to achieve.

We primarily use the solution for the enterprise data hub and big data warehouse extension.

The ability to add multiple nodes without any restriction is the solution's most valuable aspect.

What needs improvement depends on the customer and the use case. The classical Hadoop, for example, we consider an old variant. Most now work with flash data.

There is a very wide application for this solution, but in enterprise companies, if you work with classical BI systems, it would be good to include an additional presentation layer for BI solutions.

There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution. 

We've been working with the solution for three to four years.

The solution is stable. It has very good disaster stability and multi-rack configuration.

It is possible to scale the solution. We work with companies that have hundreds of users.

The initial setup might not be straightforward for our customers, but it's easy enough for us to handle. However, if we don't build a proof of concept for the company first it may take some time and be quite complex. Pilot projects take about three months to deploy and full spec projects take up to a year because we have to work in all requirements in data governance, security, etc.

We originally built on Hortonworks tech which didn't require any licensing, but that is getting discontinued in 2022, so it's been proposed we move to Cloudera which will have licensing costs associated with it.

We use the on-premises deployment model. It's a requirement for the company we work with, which is a bank. Often customers demand we work with on-premises deployment models.

I'd rate the solution seven out of ten. In terms of the ability to build middleware and offer scalability, it would be 10 out of 10 from me. However,  if you take into account only the visualization, I'd only rate it at three or four out of ten.

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.

The distributed processing is excellent. 

On the solution, Spark is very good. 

The performance is pretty good.

For the visualization tools, we use Apache Hadoop and it is very slow.

It lacks some query language. We have to use Apache Linux. Even so, the query language still has limitations with just a bit of documentation and many of the visualization tools do not have direct connectivity. They need something like BigQuery which is very fast. We need those to be available in the cloud and scalable.

The solution needs to be powerful and offer better availability for gathering queries.

The solution is very expensive.

I've been using the solution for about five years now.

The solution is stable and offers good performance. It doesn't crash or freeze. It's not buggy at all.

You can scale the solution if you need to. We find that it's pretty easy to expand it out.

There were about 13-20 people using it at any given time.

The technical support was pretty good. It's my understanding that the company was pretty satisfied with the level of support they received. They were knowledgeable and responsive.

I've also worked with MySQL and Postgres. Hadoop is more for analytical processing. While the others claim to have a distributor, Hadoop is far better in that regard. It's excellent compared to other options.

The initial setup was pretty straightforward. It was not overly complex for our team.

The solution isn't cheap. It's quite costly.

The solution is perfect for those dealing with a huge amount of data. Still, you need to check to make sure it meets your company's requirements. You need to understand them before actually choosing the technology you'll ultimately use.

Overall, I would rate the solution at a seven out of ten.

• Content management solution
• Unified Data solution
• Apache Hadoop running on Linux

• Data ingestion: It has rapid speed, if Apache Accumulo is used.
• Data security
• Inexpensive

It needs better user interface (UI) functionalities.

There are no licensing costs involved, hence money is saved on the software infrastructure.

We are primarily dumping all the prior payment transaction data into a loop system and then we use some of the plug and play analytics tools to translate it.

The most valuable feature is the database.

We're finding vulnerabilities in running it 24/7. We're experiencing some downtime that affects the data.

It would be good to have more advanced analytics tools.

I've been using the solution for five years.

The solution is scalable. From a payments perspective, we're using the solution on a large scale.

We've never contacted technical support.

We didn't previously use a different solution.

The initial setup was complex. There was a lot of data that we had to bring over from various sources and it was quite a long process.

We did have some assistance with the implementation.

We use the on-premises deployment model.

We're more inclined towards an operational data source to fill our customer's needs. Hadoop is good for analytics and some reporting requirements. 

It's a good solution for those needing something for the purposes of management reporting.

I'd rate the solution eight out of ten.