I am a new hadoop developer and I have been able to install and run hadoop services in a single-node cluster. The problem comes during data visualization. What purpose does MapReduce jar file play when I need to use a data visualization tool like Tableau. I have a structured data source in which I need to add a layer of logic so that the data could make sense during visualization. Do I need to write MapReduce programs if I am going to visualize with other tools? Please shed some light on how I could go about on this issue.
This probably depends on what distribution of Hadoop you are using and which tools are present. It also depends on the actual data preparation task.
If you don't want to actually write map-reduce or spark code yourself you could try SQL-like queries using Hive (which translates to map-reduce) or the even faster Impala. Using SQL you can create tabular data (hive tables) which can easily be consumed. Tableau has connectors for both of them that automatically translate your tableau configurations/requests to Hive/Impala. I would recommend connecting with Impala because of its speed.
If you need to do work that requires more programming or where SQL just isn't enough you could try Pig. Pig is a high level scripting language that compiles to map-reduce code. You can try all of the above in their respective editor in Hue or from CLI.
If you feel like all of the above still don't fit your use case I would suggest writing map-reduce or spark code. Spark does not need to be written in Java only and has the advantage of being generally faster.
Most tools can integrate with hive tables meaning you don't need to rewrite code. If a tool does not provide this you can make CSV extracts from the hive tables or you can keep the tables stored as CSV/TSV. You can then import these files in your visualization tool.
The existing answer already touches on this but is a bit broad, so I decided to focus on the key part:
Typical steps for data visualisation
Do the complex calculations using any hadoop tool that you like
Offer the output in a (hive) table
Pull the data into the memory of the visualisation tool (e.g. Tableau), for instance using JDBC
If the data is too big to be pulled into memory, you could pull it into a normal SQL database instead and work on that directly from your visualisation tool. (If you work directly on hive, you will go crazy as the simplest queries take 30+ seconds.)
In case it is not possible/desirable to connect your visualisation tool for some reason, the workaround would be to dump output files, for instance as CSV, and then load these into the visualisation tool.
Check out some end to end solutions for data visualization.
For example like Metatron Discovery, it uses druid as their OLAP engine. So you just link your hadoop with Druid and then you can manage and visualize your hadoop data accordingly. This is an open source so that you also can see the code inside it.
Related
I am new to this Hadoop thing . What I want to accomplish is store time series data over cloud in a distributed system. Looking at few stuffs over web OpenTSDB seems to be a feasible option for me to do so .
I also have some RDBMS databases which can be stored in distributed system and used using hive.
What we plan to do is use timeseries databases and the structured RDBMS data (read and write via HIVE ) and then join time series data with this structured data . Store the output in such a way that it can be read and write like SQL something via HIVE.
Not sure if you're asking about Hadoop or TSDBs.
If you already had a Hadoop environment, sure, adding HBase and then OpenTSDB might make sense.
If you want alternatives that offer more query centric ideas, then Influxdb or TimescaleDb seem more popular in that realm.
If scalability is really the issue, then Cassandra with Kairos is another option.
As part as Hive-like processing goes, SparkSQL probably can interact with all the above
Difference between map-reduce ,hive ,pig
pig : its a data flow language, it can work on any data basically used to convert semi structure ,unstructured data to structure so that can be used in hive advance analytics using windowing function etc.
Hive : Work on structure data and provide sql type query language .
I know at back end both pig and hive uses map -reduces .
I know map-reduce can be good tool for programmer ,hive or pig for sql guy
I just want to know is there any specific use cases where we go for hive,pig and map-reduce
basically we decide that we have to use pig here hive here or we must use map -reduce .
Map-Reduce: Has better performance than pig or hive but requires more development time.
PIg: Less development time but poor performance when compared to map-reduce.
Hve: SQL type language with some good features like partitioning and bucketing to improve performance reads.Also, hive enforces schema on read.
Pig is used to format your unstructured/semi structure data format.Lets say you have a timestamp in your data which is not as per Hive timestamp format.You can convert same using pigUDF and format your data.This is just a example to explain.You can do many more things using Pig.
Hive is basically used for structured data .This maynot work well with unstructured data.This takes more time to execute as it converts into Mapreduce job.I suggest you to use impala which is much faster than hive.
Pig is a data flow language. This means that you can not use if statements or loops.
If you need to do a lot of repetition, it would be preferable to learn mapreduce.
You are able to get around this by embedding pig into a python script but this would take even longer since it would have to load all the jar files with every iteration of the loop.
Basically it boils down to how much time you spend prototyping vs. how much production work you have.
If you are a data scientist or an analyst, most of your work is new projects that require a lot of prototyping. This means that you care about getting results fast. Then you would prefer Pig or Hive.
If you are in a development team, you want to build robust code based on agreed upon methodology that does not need to be tested and then you would prefer mapreduce.
There are companies like Cloudera that provide a package of Pig, Hive, and other Hadoop tools so you wouldn't have to choose between the two.
Map Reduce is a inner component of hadoop, other Pig and hive are hadoop eco systems it means run on the top of hadoop. The purpose of both mapreduce, pig and hive purpose is process the vast amount of data in different manner.
Mapreduce: apache implemented it. highly recommendable to process entire data, it's time consume and required program skills like java (highly recommendable), pyghon, ruby and other programming languages. total data aggregate and sort by using mapper and reducer functions. Hadoop use it by default.
Hive: Facebook implemented it. most of the analysts especially bigdata analysts use this tool to analyze the data especially structure data. Backend this hive tool use mapreduce to be processed. Internally Hive use special language called HQL, It's subset of SQL language. Who is wellever in SQL, they can goes with Hive. It's highly recommended to the Datawarehouse oriented projects. Much difficult to process un structured especially schema-less data.
Pig:
Pig is a scripting language, implemented by Yahoo. The main difference between pig and Hive is pig can process any type of data, either structured or unstructured data. It means it's highly recommendable for streaming data like satellite generated data, live events, schema-less data etc. Pig first load the data later programmer write a program depends on data to make it structured. Who is expert in programming languages they will choose this Hadoop ecosystems.
I am trying to convert a application that have relational database as backend. Can I store the data relationaly in HDFS as well?
Just for the sake of storing, you can store anything in HDFS. But that won't make any sense. First of all, you should not think of Hadoop as a replacement to your RDBMS(which you are trying to do here). Both are meant for totally different purposes. Hadoop is not a good fit for your transactional, relational or real-time kind of needs. It was meant to serve your offline batch processing needs. So, it's better to analyze your use case properly and then freeze your decision.
As a suggestion I would like to point you to Hive. It provides you warehousing capabilities on top of your existing Hadoop cluster. It also provides an SQL like interface to your warehouse, which will make your life much easier if you are coming from SQL background. But again, Hive is also a batch processing system and is not a good fit if you need something real time.
You can have a look at HBase though, as suggested by abhinav. It's a DB that can run on top of your Hadoop cluster and provides you random, real time read/write access to your data. But you should keep 1 thing in mind that it's a NoSQL db. It doesn't follow the SQL terminologies and conventions. So, you might find it a bit alien initially. You might have to think about issues like how to store your data in a new storage style(columnar) unlike the row style storage of your RDBMS. Otherwise it's not a problem to setup and use it.
HTH
Any file can be stored in HDFS. But if you want an SQL type DB you should go for HBASE. If you directly store your data into HDFS you will not be able to store rationality.
I have a very very large quantity of data in CouchDB, but I have very recently found out how crippled the mapreduce functions in couch are (no chaining).
So I had this idea of running map reduce queries from the CouchDB database using Hadoop, and hopefully storing the final result in another CouchDB database?
Is this too crazy? I know I can set up Hbase to do this, but I do not want to migrate my data from CouchDB to Hbase. And I love couch as a data store.
Apparently CouchDB is supposed to be able to stream data to Hadoop via Sqoop, but I didn't see any other information than that link. Worst case, you can write your own input reader to read from CouchDB, or export your data regularly and throw it onto HDFS and run it from there.
The MapReduce functions in CouchDB are constrained to simplify caching of the results. Rather than having to search for views that are impacted by a change, views were designed to be self-contained.
This means that if you have complex MapReduce code, you can use a tool like CouchApp to embed functions within a MapReduce function. I'm having trouble finding the reference for this, but you the macro !code to embed JavaScript functions in views. Using require() or // !json, !code in CouchDB?
This could help to get some of the productivity benefit of chaining without chaining, by putting most of the code in shared functions, and merely calling the function in the different views. For the performance benefit of chaining, if that's what you're after, you may be better off just moving to HBase.
This might be an interesting question to some:
Given: 2-3 Terabyte of data stored in SQL Server(RDBMS), consider it similar to Amazons data, i.e., users -> what things they saw/clicked to see -> what they bought
Task: Make a recommendation engine (like Amazon), which displays to user, customer who bought this also bought this -> if you liked this, then you might like this -> (Also) kind of data mining to predict future buying habits as well(Data Mining). So on and so forth, basically a reco engine.
Issue: Because of the sheer volume of data (5-6 yrs worth of user habit data), I see Hadoop as the ultimate solution. Now the question is, what technological tools combinations to use?, i.e.,
HDFS: Underlying FIle system
HBASE/HIVE/PIG: ?
Mahout: For running some algorithms, which I assume uses Map-Reduce (genetic, cluster, data mining etc.)
- What am I missing? What about loading RDBMS data for all this processing? (Sqoop for Hadoop?)
- At the end of all this, I get a list of results(reco's), or there exists a way to query it directly and report it to the front-end I build in .NET??
I think the answer to this question, just might be a good discussion for many people like me in the future who want to kick start their hadoop experimentation.
For loading data from RDBMS, I'd recommend looking into BCP (to export from SQL to flat file) then Hadoop command line for loading into HDFS. Sqoop is good for ongoing data but it's going to be intolerably slow for your initial load.
To query results from Hadoop you can use HBase (assuming you want low-latency queries), which can be queried from C# via it's Thrift API.
HBase can fit your scenario.
HDFS is the underlying file system. Nevertheless you cannot load the data in HDFS (in arbitrary format) query in HBase, unless you use the HBase file format (HFile)
HBase has integration with MR.
Pig and Hive also integrate with HBase.
As Chris mentioned it, you can use Thrift to perform your queries (get, scan) since this will extract specific user info and not a massive data set it is more suitable than using MR.