Apache Hadoop was reliable. However, you can't stop managing it. If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content. It scaled well. Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources. View full review »

The main features of the tool are the distribution, how it makes data clusters, and what the data is, which are all very organized in Apache Hadoop.

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My customers like the HDFS and the data warehouse capabilities within Hadoop.

They have integrated other tools as well, like Power BI and Oracle BI, both on Azure, for reporting. Oracle BI is difficult to integrate.

It is difficult to integrate them with Hadoop.

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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.

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It's primarily open source. You can handle huge data volumes and create your own views, workflows, and tables. I can also use it for real-time data streaming.

Its ability to handle open data access is significant, and the support is substantial, though not as responsive as one might hope. It's very effective for data processing and storage.

Overall, it's very good for data processing and storage.

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Hadoop File System is a perfect choice if we want to use any database systems or file systems because it is open-source. It has no cost. Or else, we’ll have to use Amazon S3 or Azure database, for which we will have to pay a lot. A lot of big data processing needs a proper partition and structure. Hadoop File System is compatible with almost all the query engines. That’s another reason why people would be very comfortable working with the Hadoop ecosystem.

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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.

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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.

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It's open-source, so it's very cost-effective. Apache Hadoop has its strengths. For example, in my previous organization, which was a small startup, we used it because it was cost-effective. 

We only had to pay for the servers, and we could optimize applications and performance using our employees, which was especially cost-effective in India. So, human resources were the main investment, not software. 

That was five years ago, though. In the last five years, I've mainly seen Redshift, Azure, and Oracle in the market.

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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.

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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.

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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. 

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

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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.

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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.

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Hadoop is extensible — it's elastic.

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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.

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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.

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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. 

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The distributed processing is excellent. 

On the solution, Spark is very good. 

The performance is pretty good.

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The most valuable features are powerful tools for ingestion, as data is in multiple systems.

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The most valuable feature is the database.

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The solution is perfect for when you have big data. It's good for managing and replication.

It's good for storing historical data and handling analytics on a huge amount of data.

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

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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.

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The most valuable thing about this program for us is that it is very powerful and very cheap. We're using a lot of the program's modules and features because we're using software and hardware that can be difficult to integrate. For example, we're using supersets and a lot of old products from difficult systems. We love having the various options and features that allow us to work with flexibility.

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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.

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• Scalability
• Parallel processing

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

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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.

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High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization.

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

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Impala. As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R (for further data analysis) or QlikView (for data visualisation).

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HDFS allows you to store large data sets optimally.

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The Distributed File System, which is the base of Hadoop, has been the most valuable feature with its ability to store video, pictures, JSON, XML, and plain text all in the same file system.

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• Storage
• Processing (cost efficient)

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I liked that Apache Hadoop was powerful, had a lot of tools, and the fact that it was free and community-developed. 

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