- Spark Streaming to read data from Kafka - Storing the data on HDFS using Flume
You don't need Spark streaming to read data from Kafka and store on HDFS. It is a waste of resources. Couple Flume to use Kafka as source and HDFS as sink directly KafkaAgent.sources = kafka-sources KafkaAgent.sinks.hdfs-sinks.type = hdfs That will be for your batch layer. To analyse you can directly read from hdfs files with Spark or simply store data in a database of your choice via cron or something. Do not mix your batch layer with speed layer. Your speed layer will ingest the same data directly from Kafka into spark streaming and that will be online or near real time (defined by your window). Then you have a a serving layer to present data from both speed (the one from SS) and batch layer. HTH Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On 29 September 2016 at 15:15, Ali Akhtar <ali.rac...@gmail.com> wrote: > The web UI is actually the speed layer, it needs to be able to query the > data online, and show the results in real-time. > > It also needs a custom front-end, so a system like Tableau can't be used, > it must have a custom backend + front-end. > > Thanks for the recommendation of Flume. Do you think this will work: > > - Spark Streaming to read data from Kafka > - Storing the data on HDFS using Flume > - Using Spark to query the data in the backend of the web UI? > > > > On Thu, Sep 29, 2016 at 7:08 PM, Mich Talebzadeh < > mich.talebza...@gmail.com> wrote: > >> You need a batch layer and a speed layer. Data from Kafka can be stored >> on HDFS using flume. >> >> - Query this data to generate reports / analytics (There will be a web >> UI which will be the front-end to the data, and will show the reports) >> >> This is basically batch layer and you need something like Tableau or >> Zeppelin to query data >> >> You will also need spark streaming to query data online for speed layer. >> That data could be stored in some transient fabric like ignite or even >> druid. >> >> HTH >> >> >> >> >> >> >> >> >> Dr Mich Talebzadeh >> >> >> >> LinkedIn * >> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >> >> >> >> http://talebzadehmich.wordpress.com >> >> >> *Disclaimer:* Use it at your own risk. Any and all responsibility for >> any loss, damage or destruction of data or any other property which may >> arise from relying on this email's technical content is explicitly >> disclaimed. The author will in no case be liable for any monetary damages >> arising from such loss, damage or destruction. >> >> >> >> On 29 September 2016 at 15:01, Ali Akhtar <ali.rac...@gmail.com> wrote: >> >>> It needs to be able to scale to a very large amount of data, yes. >>> >>> On Thu, Sep 29, 2016 at 7:00 PM, Deepak Sharma <deepakmc...@gmail.com> >>> wrote: >>> >>>> What is the message inflow ? >>>> If it's really high , definitely spark will be of great use . >>>> >>>> Thanks >>>> Deepak >>>> >>>> On Sep 29, 2016 19:24, "Ali Akhtar" <ali.rac...@gmail.com> wrote: >>>> >>>>> I have a somewhat tricky use case, and I'm looking for ideas. >>>>> >>>>> I have 5-6 Kafka producers, reading various APIs, and writing their >>>>> raw data into Kafka. >>>>> >>>>> I need to: >>>>> >>>>> - Do ETL on the data, and standardize it. >>>>> >>>>> - Store the standardized data somewhere (HBase / Cassandra / Raw HDFS >>>>> / ElasticSearch / Postgres) >>>>> >>>>> - Query this data to generate reports / analytics (There will be a web >>>>> UI which will be the front-end to the data, and will show the reports) >>>>> >>>>> Java is being used as the backend language for everything (backend of >>>>> the web UI, as well as the ETL layer) >>>>> >>>>> I'm considering: >>>>> >>>>> - Using raw Kafka consumers, or Spark Streaming, as the ETL layer >>>>> (receive raw data from Kafka, standardize & store it) >>>>> >>>>> - Using Cassandra, HBase, or raw HDFS, for storing the standardized >>>>> data, and to allow queries >>>>> >>>>> - In the backend of the web UI, I could either use Spark to run >>>>> queries across the data (mostly filters), or directly run queries against >>>>> Cassandra / HBase >>>>> >>>>> I'd appreciate some thoughts / suggestions on which of these >>>>> alternatives I should go with (e.g, using raw Kafka consumers vs Spark for >>>>> ETL, which persistent data store to use, and how to query that data store >>>>> in the backend of the web UI, for displaying the reports). >>>>> >>>>> >>>>> Thanks. >>>>> >>>> >>> >> >
