By Bahaaldine Azarmi

ISBN-10: 1782169490

ISBN-13: 9781782169499

Talend, a winning Open resource info Integration answer, speeds up the adoption of recent huge facts applied sciences and successfully integrates them into your present IT infrastructure. it can do that as a result of its intuitive graphical language, its a number of connectors to the Hadoop surroundings, and its array of instruments for information integration, caliber, administration, and governance.

This is a concise, pragmatic e-book that would consultant you thru layout and enforce huge info move simply and practice enormous information analytics jobs utilizing Hadoop applied sciences like HDFS, HBase, Hive, Pig, and Sqoop. you'll discover and how you can write complicated processing task codes and the way to leverage the ability of Hadoop tasks during the layout of graphical Talend jobs utilizing company modeler, meta-data repository, and a palette of configurable components.

Starting with figuring out easy methods to approach a large number of information utilizing Talend immense facts elements, you'll then tips on how to write activity approaches in HDFS. you'll then examine how one can use Hadoop tasks to method facts and the way to export the information for your favorite relational database system.

You will find out how to enforce Hive ELT jobs, Pig aggregation and filtering jobs, and easy Sqoop jobs utilizing the Talend titanic information part palette. additionally, you will examine the fundamentals of Twitter sentiment research the directions to structure information with Apache Hive.

Talend for giant info will aid you begin engaged on titanic facts initiatives instantly, from easy processing initiatives to complicated initiatives utilizing universal significant information styles

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The second component drops the existing tweets table by using the following command: DROP TABLE IF EXISTS tweets 3. username+"/packt/chp02 4. We set the delimiter to the semicolon, and the data location to the directory where we have written the tweets file. 5. Connect the component by right-clicking on it and choosing onComponent Ok for each of them. At the end, your Job should look as follows: Hive creates tweets table [ 34 ] Chapter 3 Checking in Hive if the table has been created is pretty easy; just open a terminal in your Cloudera VM and issue the following commands: • $ hive: This command connects to the Hive server and opens the Hive command-line tool • $ use default: This command selects the database • $ desc tweets: This command shows tweets table description and prints the following information: The Hive tweet table's description Formatting tweets with Apache Hive In this last part of the Hive integration process, we now need to create a formatted tweet table and separate the content into the following two parts: • The effective content of the tweet • The username of the tweet author We are doing this because so far we only have the merged content and we couldn't determine who is the most active user on a specific topic.

2. Double-click on the tHiveConnection component. 3. hdfsPort [ 33 ] Formatting Data You may have noticed that we need to install a JAR file to be able to use the tHiveConnection component; just click on the Install Jar button and follow the instructions. Now that our Hive connection is properly configured, we can use this connection for each tHiveRow component by performing the following steps: 1. Double-click on the component. 2. In the property view, check the Use an existing connection checkbox.

Modify the third tHiveRow component and change the dropped table name using the following command: DROP TABLE IF EXISTS formatted_tweets [ 35 ] Formatting Data 3. username+"/ packt/chp02/formatedTweets" So far, we only have the part that creates a formatted_tweets table over HDFS, but we need to use the Talend Hive ELT features to feed the formatted tweets HDFS folder. This is done by performing the following steps: 1. From the palette, drag-and-drop a tHiveELTInput component, a tHiveELTMap component, and a tHiveELTOutput component.

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Talend for Big Data by Bahaaldine Azarmi

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