Apache Hadoop Reviews

We use this solution for our Enterprise Data Lake.

Using this solution has reduced the overall TCO. It has also improved data processing time for the machine and provides greater insight into our unstructured data.

The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics.

We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.

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 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.