Apache Hadoop Reviews and Ratings

Use Cases and Deployment Scope

We are using the Apache Hadoop to handle the data which is continuously coming from different devices in real time from different geographical location across the globe and then run spark jobs and notebook to ingest the data and process it and then load it other external systems for further processing.

Likelihood to Recommend

When you have real time data which amounts to massive volumes close to terabytes daily, it’s become quite imperative that we should have a system which can handle it and ingest without losing it. Having Hadoop in place makes our product more robust, its stability comes handy. <div>

</div><div>The only challenge in running huge clusters is it require huge amount of space and memory for efficient working.</div>

Use Cases and Deployment Scope

Apache Hadoop is one of the most effective and efficient software which has been storing and processing an extremely colossal amount of data in my company for a long time now. The software Hadoop is primarily used for data collection of large amounts, storage as well as for analytics. From my experience, I have to say that Hadoop is extremely useful and has a reliable plus valid purpose.

Likelihood to Recommend

Apache Hadoop is majorly suited for companies that have large amounts of unstructured data flow like advertising and even web traffic so I feel that Hadoop is a great option when you have the extra bulk of data that is required to be stored and processed on a continuous basis. Moreover, I do recommend Hadoop but at the same time, I would also hope and suggest that the software of Hadoop gets supplemented with a faster and interactive database so that the overall querying service gets better.

Use Cases and Deployment Scope

Apache Hadoop is an open-source software library that is designed for the collection, storage, and analysis of large amounts of data sets. Apache Hadoop’s architecture comprises components that include a distributed file system. This is mostly used for massive data collection, analytics, and storage. Also, having consistent data can be integrated across other platforms and have one single source of truth.

Likelihood to Recommend

Altogether, I want to say that Apache Hadoop is well-suited to a larger and unstructured data flow like an aggregation of web traffic or even advertising. I think Apache Hadoop is great when you literally have petabytes of data that need to be stored and processed on an ongoing basis. Also, I would recommend that the software should be supplemented with a faster and interactive database for a better querying service. Lastly, it's very cost-effective so it is good to give it a shot before coming to any conclusion.

Use Cases and Deployment Scope

We use Apache Hadoop to store and process large amounts of data (petabytes per day) across thousands of data pipelines. Hadoop works reliably for this purpose. Data scientists at the company also use it for interactive querying for analytics and modeling purposes.

Likelihood to Recommend

If you have petabytes of data that you need to store and process on a regular basis and don't mind having to wait minutes for your queries to run, Apache Hadoop is great for that use case. I would supplement it with another faster interactive database for interactive querying.

Use Cases and Deployment Scope

[Apache Hadoop] is being handled as it is (mostly) intended. For large, unstructured data management from our data flows to include logging and reports extract, transform and load. We are using it at a medium scale in an on-prem server delivery with Cloudera as the management platform. While I firmly believe cloudera makes it a bit easier to manage, it obfuscates issues at times.

Likelihood to Recommend

Apache Hadoop (and its subsequent add-ons) are well-suited to larger, unstructured data flows, such as aggregation of web traffic or advertising. Geospatial algorithms and their outputs are well-suited for this kind of aggregation as structuring that data is challenging, but leaving it unstructured and performing queries as-needed is a better fit for most business models. With the advent of data science, I would expect Hadoop fits a LOT of their initial outputs quite well.

Use Cases and Deployment Scope

It's used organization-wide for older data that's not used as frequently. We use Teradata to warehouse our more recent data, but for data we don't access as often, it's migrated to Hadoop. It addresses the problem of securely storing data without paying the fortune that most warehouses charge for premium cloud storage.

Likelihood to Recommend

Need cheap enterprise-level storage for data that is necessary to keep but isn't regularly accessed? Hadoop is the option for you. If you regularly have analysts or apps accessing the data warehouse, look for something more premium such as Teradata. The good news is that general SQL knowledge transfers well to this warehouse.

Use Cases and Deployment Scope

We are using it within my department to process large sets of data that can't be processed in a timely fashion on a single computer or node. The various modules provided with Hadoop make it easy for us to implement map-reduce and perform parallel processing on large sets of data. We have approximately 40TB of data that we run various algorithms against as we try to use the data to solve business problems and prevent fraudulent transactions.

Likelihood to Recommend

Hadoop is easy to use. It is a scalable and cost-effective solution for working with large data sets. Hadoop accepts data from a variety of disparate data sources, such as social media feeds, structured or unstructured data, XML, text files, images, etc. Hadoop is also highly available and fault-tolerant, supporting multiple standby NameNodes. The performance of Hadoop is also good because it stores data in a distributed fashion allowing for distributed processing and lower run times. And Hadoop is open-source, making the source code available for modification if necessary. Hadoop also supports multiple languages like C/C++, Python, and Groovy.

Use Cases and Deployment Scope

It is being used at our Fortune 500 clients. It is great for storage, but it is not well understood by the business. The challenge is that it requires very sophisticated data scientists to use properly and in parallel, but the data scientists turn the data on its head, causing IT execution issues. This has forced IT to restructure data in a denormalized form so the business users can actually be productive. This is a big trend in organizations.

Likelihood to Recommend

Massive processing in a distributed environment with data that can be distributed. Research environments. Lab environments would also be a good use for Hadoop. Hadoop can also be used in support of Spark environments and used by Frameworks if deployed properly. The best scenario is with a Data Scientist that understands how to program appropriately.

Use Cases and Deployment Scope

Hadoop is being used to solve big data modeling problems in our firm. The corporate analytics team uses Hadoop to perform functions like data manipulation, information retrieval, data mapping, and statistical modeling. The business problem which it solves is the limitation of CSV/Excel files to handle more than a million rows. Hadoop allows you to process big data and also has connectivity with platforms like R Studio where you can deploy mathematical models.

Likelihood to Recommend

Hadoop is very well suited for big data modeling problems in various industries like finance, insurance, healthcare, automobiles, CRM, etc. In every industry where you need data analysis in real time, Hadoop is a perfect fit in terms of storage, analysis, retrieval, and processing. It won't be a very good tool to perform ETL (Extract Transform Load) techniques though.

Use Cases and Deployment Scope

It is massively being used in our organization for data storage, data backup, and machine learning analytics. Managing vast amounts of data has become quite easy since the arrival of the Hadoop environment. Our department is on verge of moving towards Spark instead of MapReduce, but for now, Hadoop is being used extensively for MapReduce purposes.

Likelihood to Recommend

<div>Hadoop helps us tackle our problem of maintaining and processing a huge amount of data efficiently. High availability, scalability and cost efficiency are the main considerations for implementing Hadoop as one of the core solutions in our big-data infrastructure. Where relational databases fall short with regard to tuning and performance, Hadoop rises to the occasion and allows for massive customization leveraging the different tools and modules. We use Hadoop to input raw data and add layers of consolidation or analysis to make business decisions about disparate data points.</div><div>

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