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Xzazu + uProc Integrations

Appy Pie Connect allows you to automate multiple workflows between Xzazu and uProc

About Xzazu

Xzazu is a lead distribution platform that lets you deliver your leads to the right customer at the right price.

About uProc

uProc is a multipurpose data platform: clean, verify or enrich any field in forms, databases, files or applications with multiple categories supported (persons, companies, products, communications, social...).

uProc Integrations
Connect Xzazu + uProc in easier way

It's easy to connect Xzazu + uProc without coding knowledge. Start creating your own business flow.

    Triggers
  • New Outbound Lead

    Triggers when a new outbound lead is available for your contract.

  • New Outbound Ping

    Triggers when a new outbound ping is available for your contract.

  • New Pong Result

    Triggers when a new pong result is available for your ping.

    Actions
  • Create a New Inbound Lead

    Create an Inbond lead.

  • Select Tool

    Select a tool to perform verification or enrichment

How Xzazu & uProc Integrations Work

  1. Step 1: Choose Xzazu as a trigger app and Select "Trigger" from the Triggers List.

    (30 seconds)

  2. Step 2: Authenticate Xzazu with Appy Pie Connect.

    (10 seconds)

  3. Step 3: Select uProc as an action app.

    (30 seconds)

  4. Step 4: Pick desired action for the selected trigger.

    (10 seconds)

  5. Step 5: Authenticate uProc with Appy Pie Connect.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Xzazu and uProc

Xzazu?

Xzazu is an open-source distributed computation platform which leverages the power of the cloud to process tasks in parallel. It provides a programming model that makes it easy to write MapReduce programs over massive datasets.

uProc?

uProc is a lightweight library that simplifies writing Hadoop MapReduce jobs. It eliminates the need to write Java code for setting up the Hadoop job and allows users to focus on expressing the logic for processing the data. uProc is based on the idea of using pre-written functions (known as uFuncs. to write MapReduce programs, similar to SQL views. uProc allows users to create their own custom uFuncs, and we are providing several pre-written ones (such as aggregate, join, etc.. in uProc.

Integration of Xzazu and uProc

The fplowing figure illustrates how Xzazu works with uProc:

Figure 1. Integration of Xzazu and uProc

Based on Figure 1, if you are using uProc, you can directly use Xzazu through a function call. The fplowing Java code shows an example:

import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.libmapper.XzazuMapReducer; import org.apache.hadoop.mapreduce.libmapper.XzazuMapper; import org.apache.hadoop.mapreduce.libmapper.XzazuReducer; public class Main { public static void main(String[] args. { Configuration conf = new Configuration(); conf .set("fs.defaultFS", "/xzazu". .set("mapreduce.job.reduceservice.class", "org.apache.xzazu.utils.mapreduce"); // create the configuration Job job = new Job(conf, "MyApp"); // create the mappers and reducers Mapper mapper = new MyMapper(); Reducer reducer = new MyReducer(); // create the input/output paths String inputPath = "/user/MyPath"; Path outputPath = new Path(conf, "MyOutputPath"); // register the mappers/reducers job .setInputFormatClass(TextInputFormat .class. .setOutputFormatClass(TextOutputFormat .class); // set up the job job .setMapperClass(XzazuMapper .class. .setReducerClass(XzazuReducer .class); // start the job System .out .println(job .waitForCompletion(true). ; } private static class MyMapper extends XzazuMapReducer<LongWritable, Text , Text> { @Override public void map(LongWritable key, Text value, Context context. throws IOException, InterruptedException { } } private static class MyReducer extends XzazuReducer<Text, Text , LongWritable> { @Override public void reduce(Text key, Iterable<Text> values, Context context. throws IOException, InterruptedException { } } }

Benefits of Integration of Xzazu and uProc

You can easily write your complex data processing tasks by learning Xzazu and uProc separately or together because they integrate well in different areas (as shown in Figure 2. Moreover, you can use any existing Java program as a mapper or reducer with the Xzazu framework by using an implementation of two interfaces (XzazuMapper and XzazuReducer.

Figure 2. Benefits of Integration of Xzazu and uProc

The process to integrate Xzazu and uProc may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.