Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.
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PostgreSQL
Score 8.3 out of 10
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PostgreSQL (alternately Postgres) is a free and open source object-relational database system boasting over 30 years of active development, reliability, feature robustness, and performance. It supports SQL and is designed to support various workloads flexibly.
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.
PostgreSQL is ideal for handling databases that contain large volumes of information due to its efficiency, speed and above all because of the good management it makes of our resources, it also behaves very well in distributed environments of high demand, if you want a database of stable data and excellent performance PostgreSQL is one of the best.
Hadoop is a batch oriented processing framework, it lacks real time or stream processing.
Hadoop's HDFS file system is not a POSIX compliant file system and does not work well with small files, especially smaller than the default block size.
Hadoop cannot be used for running interactive jobs or analytics.
The performance of PostgreSQL has been enhanced through the years, but always is better to have as much performance as we can.
The replication services could be done directly within the database, and more easily.
The Object Orientation of the Database could be extended, and albeit it manages inheritance of tables, and accepts XML and JSON as primary types, it would be wonderful if one could attach methods more easily to tables (to make them more like classes), and instances (rows for example).
Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free
Great! Hadoop has an easy to use interface that mimics most other data warehouses. You can access your data via SQL and have it display in a terminal before exporting it to your business intelligence platform of choice. Of course, for smaller data sets, you can also export it to Microsoft Excel.
Postgresql is the best tool out there for relational data so I have to give it a high rating when it comes to analytics, data availability and consistency, so on and so forth. SQL is also a relatively consistent language so when it comes to building new tables and loading data in from the OLTP database, there are enough tools where we can perform ETL on a scalable basis.
The data queries are relatively quick for a small to medium sized table. With complex joins, and a wide and deep table however, the performance of the query has room for improvement.
We went with a third party for support, i.e., consultant. Had we gone with Azure or Cloudera, we would have obtained support directly from the vendor. my rating is more on the third party we selected and doesn't reflect the overall support available for Hadoop. I think we could have done better in our selection process, however, we were trying to use an already approved vendor within our organization. There is plenty of self-help available for Hadoop online.
AWS, Heroku, and Digital Ocean all provide Postgres-as-a-service, where you pretty much never need to administrate it yourself but they do it for you. The Postgres community also has developed awesome and reasonably priced extensions, such as Citus DB and CockroachDB in case you need additional support for running it. If you need documentation, Postgres's docs are super thorough and their official forms are active.
The online training is request based. Had there been recorded videos available online for potential users to benefit from, I could have rated it higher. The online documentation however is very helpful. The online documentation PDF is downloadable and allows users to pace their own learning. With examples and code snippets, the documentation is great starting point.
I feel that this is a highly reliable and scalable solution computing technology that is highly capable of processing large data sets across multiple servers and thousands of machines in a well-defined and distributed manner. Apache Hadoop can automatically scale up the number of servers and machines that are needed to process, store, and analyze data sets. It also handles explosions in data with big data technology. Apache Hadoop is good at handling all node failures as well.
In this case, Postgres is preferred because it handles large data sets and requires fewer hardware resources than its competitor, MySQL. Compared to PostgreSQL, Microsoft products are excellent, but the installation process for MS SQL is lengthy. PostgreSQL has an advantage over its competitors in that it can adapt or configure third-party programs, applications, or settings.
As it was open source makes it popular choice for handling large chuck of datasets
It was free earlier but now it’s licensed but still enterprise is a fine tuned version which makes it easier for new users and administrators to use it
Our investment is worth every single penny.
Initial cost is more as you might need to hire administrators to setup the cluster and make them in scalable. But once done it’s pretty easy
Easy to administer so our DevOps team has only ever used minimal time to setup, tune, and maintain.
Easy to interface with so our Engineering team has only ever used minimal time to query or modify the database. Getting the data is straightforward, what we do with it is the bigger concern.
