I have been trying to understand Nifi and Talend for sometime now and there are some questions that are bothering me.
Where does Nifi excel in data flow management apart from being a light weight than talend?
How good is talend when it comes to realtime data processing from different sources such as MySQL, Casandra etc.
How time consuming is it to do ETL in talend pitched against Nifi with Spark in processing data?
What are the other areas that one might be better than other (Incase someone has a better point to pitch)?
It would be really helpful if someone has also some data points to say anything about this?
Related
Is Apache NiFi slower than StreamSets?
I have created a pipeline which receives data from a Kafka topic and dumps the data in another Kafka topic in both Apache NiFi and StreamSets but StreamSets is way faster than NiFi.
I am using consumekafkaRecord processor in NiFi and KafkaConsumer in StreamSets.
I am very familiar with NiFi. I do not believe NiFi has any advantage over Streamsets for that specific scenario when looked at in terms of per node speed only. NiFi is designed to handle arbitrary sources and sinks which means it generally doesnt and shouldnt assume any transactional behavior of a source. Kafka though does offer a great design pattern around grabbing data, doing things, sending data to kafka or another place and then acking the response. This being an increasingly common and scaleable pattern the NiFi community is launching a NiFi-FN approach which makes both the general data distribution case and a case like this optimal in NiFi. NiFi brings a ton of really important advantages when you look at durability, reliability, diversity of data and sources/sinks, and built-in provenance. If all you need is perf and for this specific case Streamsets is better or for that matter I'd recommend Spark/Spark Streaming. If your needs will expand beyond what is described here and is data distribution/data flow management focused then NiFi will be absolutely the best choice.
I've spent a lot of time reading and watching videos of people talking about how they use tools designed for handling huge datasets and real-time processing in their architectures. And while I understand what it is that tools like Hadoop/Cassandra/Kafka etc do, no one seems to explain how the data gets from these large processing tools to rendering something on a client/webpage.
From what I understand of big data tools, is that you can't build your application the same way you would a standard web-app querying MySQL, which I can understand given the size of the data that flows through these tools, however, for all this talk of "realtime data analytics" I cannot find any explanation of how the actual analytics gets put in front of someone in terms of some chart/table/etc?
explain how the data gets from these large processing tools to rendering something on a client/webpage.
With respect to this, one way would be to process the big data using Spark or Hadoop and store the results onto a RDBMS. Then have your webapp pull data from RDBMS to render charts, table etc. I can provide you the examples that I have done myself if you need more information.
Impala supports ODBC/JDBC interfaces. So, you actually could hook up a web app to it the same way you do with MySQL.
Other stuff you might want to check out is HBase, Kudu or Solr. In some realtime architectures data ends up in one of those. And all of them have some sort of an API that you can use in your web app to access their data.
If you want a simple solution for realtime data processing and analytics, check out the new Stride API, which enables developers to collect, process, and analyze streaming data and then either visualize summary data in Stride or push processed data out to applications in realtime. This is a very easy way to build the kind of realtime reporting dashboards and monitoring / alerting systems you described above.
Take a look at the Stride API technical docs for examples and more info on how to implement this.
How effective is to use Apache NIFI for the ETL process having source as HDFS & destination as Oracle DB. What are the limitations of Apache NIFI compared other ETL tools such as Pentaho,Datastage,etc..
Main advantage of NiFi
The main advantages of NiFi:
Intuitive gui, which allows for easy inspection of the data
Strong delivery guarantees
Low latency, you can support both batch and streaming usecases
It can handle any format, not only limited to SQL tables, but can also move log files etc.
Schema aware, and can share schema with solutions like Kafka, Flink, Spark
Main limitation of NiFi
NiFi is really a tool for moving data around, you can do enrichments of individual records but it is typically mentioned to do 'EtL' with a small t. A typical thing that you would not want to do in NiFi is joining two dynamic data sources.
For joining tables, tools like Spark, Hive, or classical ETL alternatives are often used.
For joining streams, tools like Flink and Spark Streaming are often used.
Conclusion
NiFi is a great tool, you just need to make sure you use it for the right usecase. Where needed you can use other tools to complement it.
Extra strong full disclosure: I am an employee of Cloudera, the company that supports NiFi and other projects such as Spark and Flink. I have used other ETL tools before, but not to the same extent as NiFi.
Not sure about sqoop, I can explain the benifits of using Apache Nifi. In your case the data in HDFS could be of any format(Unstructured), Nifi has a capability to process and bring it to format of your choice so that you can directly save it to any RDBMS.
Nifi handles back-pressure in vary effective way to have lossless transmission.
One of the critical features that NiFi provides that our competitors generally don't is the ability to stop jobs and examine the flow and downstream systems while it's running. For you, this means you can test the flow against a test HDFS folder and a test Oracle DB, let some data go through, pause the flow and poke around Oracle to make sure it's to your liking after a matter of seconds or minutes instead of waiting for a "job to complete." It makes the process extremely agile.
Actually Nifi is very good tool. You can easily manipulate processors. In short time you can migrate huge data.
But for destinations such as RDBMS, there are always problems. I used to have a lot of problems about "non-killing" threads, you have to be very careful about stopping processes and the configuration of processors. Some processors like QueryDatabasetable consumes huge memory and the server goes down.
I am planning to do a class project and was going through few technologies where I can automate or set the flow of data between systems and found that there are couple of them i.e. Apache NiFi and StreamSets ( to my knowledge ). What I couldn't understand is the difference between them and use-cases where they can be used? I am new to this and if anyone can explain me a bit would be highly appreciated. Thanks
Suraj,
Great question.
My response is as a member of the open source Apache NiFi project management committee and as someone who is passionate about the dataflow management domain.
I've been involved in the NiFi project since it was started in 2006. My knowledge of Streamsets is relatively limited so I'll let them speak for it as they have.
The key thing to understand is that NiFi was built to do one really important thing really well and that is 'Dataflow Management'. It's design is based on a concept called Flow Based Programming which you may want to read about and reference for your project 'https://en.wikipedia.org/wiki/Flow-based_programming'
There are already many systems which produce data such as sensors and others. There are many systems which focus on data processing like Apache Storm, Spark, Flink, and others. And finally there are many systems which store data like HDFS, relational databases, and so on. NiFi purely focuses on the task of connecting those systems and providing the user experience and core functions necessary to do that well.
What are some of those key functions and design choices made to make that effective:
1) Interactive command and control
The job of someone trying to connect systems is to be able to rapidly and efficiently interact with the constant streams of data they see. NiFi's UI allows you do just that as the data is flowing you can add features to operate on it, fork off copies of data to try new approaches, adjust current settings, see recent and historical stats, helpful in-line documentation and more. Almost all other systems by comparison have a model that is design and deploy oriented meaning you make a series of changes and then deploy them. That model is fine and can be intuitive but for the dataflow management job it means you don't get the interactive change by change feedback that is so vital to quickly build new flows or to safely and efficiently correct or improve handling of existing data streams.
2) Data Provenance
A very unique capability of NiFi is its ability to generate fine grained and powerful traceability details for where your data comes from, what is done to it, where its sent and when it is done in the flow. This is essential to effective dataflow management for a number of reasons but for someone in the early exploration phases and working a project the most important thing this gives you is awesome debugging flexibility. You can setup your flows and let things run and then use provenance to actually prove that it did exactly what you wanted. If something didn't happen as you expected you can fix the flow and replay the object then repeat. Really helpful.
3) Purpose built data repositories
NiFi's out of the box experience offers very powerful performance even on really modest hardware or virtual environments. This is because of the flowfile and content repository design which gives us the high performance but transactional semantics we want as data works its way through the flow. The flowfile repository is a simple write ahead log implementation and the content repository provides an immutable versioned content store. That in turn means we can 'copy' data by only ever adding a new pointer (not actually copying bytes) or we can transform data by simply reading from the original and writing out a new version. Again very efficient. Couple that with the provenance stuff I mentioned a moment ago and it just provides a really powerful platform. Another really key thing to understand here is that in the business of connecting systems you don't always get to dictate things like size of data involved. The NiFi API was built to honor that fact and so our API lets processors do things like receive, transform, and send data without ever having to load the full objects in memory. These repositories also mean that in most flows the majority of processors do not even touch the content at all. However, you can easily see from the NiFi UI precisely how many bytes are actually being read or written so again you get really helpful information in establishing and observing your flows. This design also means NiFi can support back-pressure and pressure-release naturally and these are really critical features for a dataflow management system.
It was mentioned previously by the folks from the Streamsets company that NiFi is file oriented. I'm not really sure what the difference is between a file or a record or a tuple or an object or a message in generic terms but the reality is when data is in the flow then it is 'a thing that needs to be managed and delivered'. That is what NiFi does. Whether you have lots of really high speed tiny things or you have large things and whether they came from a live audio stream off the Internet or they come from a file sitting on your harddrive it doesn't matter. Once it is in the flow it is time to manage and deliver it. That is what NiFi does.
It was also mentioned by the Streamsets company that NiFi is schemaless. It is accurate that NiFi does not force conversion of data from whatever it is originally to some special NiFi format nor do we have to reconvert it back to some format for follow-on delivery. It would be pretty unfortunate if we did that because what this means is that even the most trivial of cases would have problematic performance implications and luckily NiFi does not have that problem. Further had we gone that route then it would mean handling diverse datasets like media (images, video, audio, and more) would be difficult but we're on the right track and NiFi is used for things like that all the time.
Finally, as you continue with your project and if you find there are things you'd like to see improved or that you'd like to contribute code we'd love to have your help. From https://nifi.apache.org you can quickly find information on how to file tickets, submit patches, email the mailing list, and more.
Here are a couple of fun recent NiFi projects to checkout:
https://www.linkedin.com/pulse/nifi-ocr-using-apache-read-childrens-books-jeremy-dyer
https://twitter.com/KayLerch/status/721455415456882689
Good luck on the class project! If you have any questions the users#nifi.apache.org mailing list would love to help.
Thanks
Joe
Both Apache NiFi and StreamSets Data Collector are Apache-licensed open source tools.
Hortonworks does have a commercially supported variant called Hortonworks DataFlow (HDF).
While both have a lot of similarities such as a web-based ui, both are used for ingesting data there are a few key differences. They also both consist of a processors linked together to perform transformations, serialization, etc.
NiFi processors are file-oriented and schemaless. This means that a piece of data is represented by a FlowFile (this could be an actual file on disk, or some blob of data acquired elsewhere). Each processor is responsible for understanding the content of the data in order to operate on it. Thus if one processor understands format A and another only understands format B, you may need to perform a data format conversion in between those two processors.
NiFi can be run standalone, or as a cluster using its own built-in clustering system.
StreamSets Data Collector (SDC) however, takes a record based approach. What this means is that as data enters your pipeline it (whether its JSON, CSV, etc) it is parsed into a common format so that the responsibility of understanding the data format is no longer placed on each individual processor and any processor can be connected to any other processor.
SDC also runs standalone, and also a clustered mode, but it runs atop Spark on YARN/Mesos instead, leveraging existing cluster resources you may have.
NiFi has been around for about the last 10 years (but less than 2 years in the open source community).
StreamSets was released to the open source community a little bit later in 2015. It is vendor agnostic, and as far as Hadoop goes Hortonworks, Cloudera, and MapR are all supported.
Full Disclosure: I am an engineer who works on StreamSets.
They are very similar for data ingest scenarios.
Apache NIFI(HDP) is more mature and StreamSets is more lightweight.
Both are easy to use, both have strong capability. And StreamSets could easily
They have companies behind, Hortonworks and Cloudera.
Obviously there are more contributors working on NIFI than StreamSets, of course, NIFI have more enterprise deployments in production.
Two of the key differentiators between the two IMHO are.
Apache NiFi is a Top Level Apache project, meaning it has gone through the incubation process described here, http://incubator.apache.org/policy/process.html, and can accept contributions from developers around the world who follow the standard Apache process which ensures software quality. StreamSets, is Apache LICENSED, meaning anyone can reuse the code, etc. But the project is not managed as an Apache project. In fact, in order to even contribute to Streamsets, you are REQUIRED to sign a contract. https://streamsets.com/contributing/ . Contrast this with the Apache NiFi contributor guide, which wasn't written by a lawyer. https://cwiki.apache.org/confluence/display/NIFI/Contributor+Guide#ContributorGuide-HowtocontributetoApacheNiFi
StreamSets "runs atop Spark on YARN/Mesos instead, leveraging existing cluster resources you may have." which imposes a bit of restriction if you want to deploy your dataflows further toward the Edge where the Devices that are generating the data live. Apache MiniFi, a sub-project of NiFi can run on a single Raspberry Pi, while I am fairly confident that StreamSets cannot, as YARN or Mesos require more resources than a Raspberry Pi provides.
Disclosure: I am a Hortonworks employee
I use my API logs to extract information like:
In this period of time how many are the users of my API ?
Or in this period of time, what type of services are called the most ?
Almost all the information I extract depend on the timestamp. Actually I use MongoDB and I added the time-stamp as an index(for 80GB, indexes size is 12GB).
A migration to cassandra or Hbase was recommended for me. And I want to know which is better for my use case:
Analysis for timeseries data.
Both good write and read performance are required.
Possibility of using hadoop to do my data analysis.
Thanks for sharing your point of view or your experience.
Advantages of Cassandra:
Cassandra generally shows better performance (though both are excellent).
Cassandra is substantially easier to setup and manage from an operational stand point (though there are tools that will help either way).
Advantages of HBase:
Native to the hadoop ecosystem
HBase will require you installing hadoop anyway, and you get a nice two-for-one. To use Cassandra you will probably need to go to use DataStax Enterprise, a commercial, non-open source product, OR investigate using Spark for your analytics work which has an open-source connector with Cassandra.
Chocolate or Vanilla ice cream - which is better?
I would suggest that you would be the best decision maker. Set up development environments for each option, and this will tell you much more about operational and tuning issues than, I think, anyone else might be able to give you. :)