Apache Hadoop Reviews

• Storage
• Processing (cost efficient)

With the increase in data size for the business, this horizontal scalable appliance has answered every business question in terms of storage and processing. Hadoop ecosystem has not only provided a reliable distributed aggregation system but has also allowed room for analytics which has resulted in great data insights.

The Apache team is doing great job and releasing Hadoop versions much ahead of what we can think about. Every room for improvement is fixed as soon as a version is released by ASF. Currently, Apache Oozie 4.0.1 has some compatibility issues with Hadoop 2.5.2.

2.5 years

Not at all.

We did when we started initially with Hadoop 1.x, which did’t have HA, but now we don’t have any stability issue.

Hadoop is known for its scalability. Yahoo stores approx. 455 PB in their Hadoop cluster.

It depends on the Hadoop distributor. I would rate Hortonworks 9/10.

I would rate Hortonworks 9/10.

We previously used Netezza. We switched because our business required a highly scalable appliance like Hadoop.

It's a bit complex in terms of build around for commodities, but soon it will ease up as the product matures.

We used a vendor team who were 9/10.

Valuable storage and processing with a lower cost than previously.

Best in pricing and licensing depends on the flavors, but remember it is only good if you have very large data set which cannot be handled by traditional RDBMS.

Cloud options.

First, understand your business requirement; second, evaluate the traditional RDBMS scalability and capability, and finally, if you have reached to the tip of an iceberg (RDBMS) then yes, you definitely need an island (Hadoop) for your business. Feasibility checks are important and efficient for any business before you can take any crucial step. I would also say “Don’t always flow with stream of a river because some time it will lead you to a waterfall, so always research and analyze before you take a ride.”

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. 

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.

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.

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 mainly use Apache Hadoop for real-time streaming. Real-time streaming and integration using Spark streaming and the ecosystem of Spark technologies inside Hadoop.

I actually like most of the capabilities, but I think Spark has added reposit capabilities on top of the Hadoop ecosystem. The Spark area includes the capabilities that I like the most with Hadoop. 

I don't have any concerns because each part of Hadoop has its use cases. To date, I haven't implemented a huge product or project using Hadoop, but on the level of POCs, it's fine. 

The community of Hadoop is now a cluster, I think there is room for improvement in the ecosystem.

From the Apache perspective or the open-source community, they need to add more capabilities to make life easier from a configuration and deployment perspective.

I have been using this solution for roughly five years.

I've never experienced any bugs or glitches.

Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability.

It's a well-known fact that Hadoop's configuration is pretty hard. 

Usually, people need to study and prepare for a few use cases and compare multiple ecosystems before choosing one. When people think of using a big data solution, Hadoop comes to mind. For certain use cases, Hadoop is comparable with other technologies. For example, when building a sort of real-time data warehouse — an enterprise data hub —, people don't think about using Hadoop directly. People often use solutions like DROID for building.

At the end of the day, you need to compare technologies — existing technologies against their use cases. You need to study your use case and select the technology inside of Hadoop that will fit your use case. You may find another ecosystem that solves your problem, just keep in mind, Hadoop is not the only solution, there are a lot of solutions. It depends on the use case. 

Overall, on a scale from one to ten, I would give Hadoop a rating of eight.