Apache Sqoop is a tool for use with Hadoop, used to transfer data between Apache Hadoop and other, structured data stores.
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RabbitMQ
Score 9.0 out of 10
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RabbitMQ, an open source message broker, is part of Pivotal Software, a VMware company acquired in 2019, and supports message queue, multiple messaging protocols, and more.
RabbitMQ is available open source, however VMware also offers a range of commercial services for RabbitMQ; these are available as part of the Pivotal App Suite.
Sqoop is great for sending data between a JDBC compliant database and a Hadoop environment. Sqoop is built for those who need a few simple CLI options to import a selection of database tables into Hadoop, do large dataset analysis that could not commonly be done with that database system due to resource constraints, then export the results back into that database (or another). Sqoop falls short when there needs to be some extra, customized processing between database extract, and Hadoop loading, in which case Apache Spark's JDBC utilities might be preferred
It is highly recommended that if you have microservices architecture and if you want to solve 2 phase commit issue, you should use RabbitMQ for communication between microservices. It is a quick and reliable mode of communication between microservices. It is also helpful if you want to implement a job and worker mechanism. You can push the jobs into RabbitMQ and that will be sent to the consumer. It is highly reliable so you won't miss any jobs and you can also implement a retry of jobs with the dead letter queue feature. It will be also helpful in time-consuming API. You can put time-consuming items into a queue so they will be processed later and your API will be quick.
What RabbitMQ does well is what it's advertised to do. It is good at providing lots of high volume, high availability queue. We've seen it handle upwards of 10 million messages in its queues, spread out over 200 queues before its publish/consume rates dipped. So yeah, it can definitely handle a lot of messages and a lot of queues. Depending on the size of the machine RabbitMQ is running on, I'm sure it can handle more.
Decent number of plugins! Want a plugin that gives you an interface to view all the queues and see their publish/consume rates? Yes, there's one for that. Want a plugin to "shovel" messages from one queue to another in an emergency? Check. Want a plugin that does extra logging for all the messages received? Got you covered!
Lots of configuration possibilities. We've tuned over 100 settings over the past year to get the performance and reliability just right. This could be a downside though--it's pretty confusing and some settings were hard to understand.
Sqoop2 development seems to have stalled. I have set it up outside of a Cloudera CDH installation, and I actually prefer it's "Sqoop Server" model better than just the CLI client version that is Sqoop1. This works especially well in a microservices environment, where there would be only one place to maintain the JDBC drivers to use for Sqoop.
It breaks communication if we don't acknowledge early. In some cases our work items are time consuming that will take a time and in that scenario we are getting errors that RabbitMQ broke the channel. It will be good if RabbitMQ provides two acknowledgements, one is for that it has been received at client side and second ack is client is completed the processing part.
RabbitMQ is very easy to configure for all supported languages (Python, Java, etc.). I have personally used it on Raspberry Pi devices via a Flask Python API as well as in Java applications. I was able to learn it quickly and now have full mastery of it. I highly recommend it for any IoT project.
I gave it a 10 but we do not have a support contract with any company for RabbitMQ so there is no official support in that regard. However, there is a community and questions asked on StackOverflow or any other major question and answer site will usually get a response.
Sqoop comes preinstalled on the major Hadoop vendor distributions as the recommended product to import data from relational databases. The ability to extend it with additional JDBC drivers makes it very flexible for the environment it is installed within.
Spark also has a useful JDBC reader, and can manipulate data in more ways than Sqoop, and also upload to many other systems than just Hadoop.
Kafka Connect JDBC is more for streaming database updates using tools such as Oracle GoldenGate or Debezium.
Streamsets and Apache NiFi both provide a more "flow based programming" approach to graphically laying out connectors between various systems, including JDBC and Hadoop.
RabbitMQ has a few advantages over Azure Service Bus 1) RMQ handles substantially larger files - ASB tops out at 100MB, we use RabbitMQfor files over 200MB 2) RabbitMQ can be easily setup on prem - Azure Service Bus is cloud only 3) RabbitMQ exchanges are easier to configure over ASB subscriptions ASB has a few advantages too 1) Cloud based - just a few mouse clicks and you're up and running
When combined with Cloudera's HUE, it can enable non-technical users to easily import relational data into Hadoop.
Being able to manipulate large datasets in Hadoop, and them load them into a type of "materialized view" in an external database system has yielded great insights into the Hadoop datalake without continuously running large batch jobs.
Sqoop isn't very user-friendly for those uncomfortable with a CLI.
Positive: we don't need to keep way too many backend machines around to deal with bursts because RabbitMQ can absorb and buffer bursts long enough to let an understaffed set of backend services to catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines around.
Negative: we've got many angry customers due to queues suddenly disappearing and dropping our messages when we try to publish to them afterward. Ideally, RabbitMQ should warn the user when queues expire due to inactivity but it doesn't, and due to our own bugs we've lost a lot of customer data as a result.
Positive: makes decoupling the web and API services from the deeper backend services easier by providing queues as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
