Full Resource Library

Creating Cluster Connection Metadata from Configuration Files

In this tutorial, create Hadoop Cluster metadata by importing the configuration from the Hadoop configuration files.
This tutorial uses Talend Data Fabric Studio version 6 and a Hadoop cluster: Cloudera CDH version 5.4.
1. Create a new Hadoop cluster metadata definition
Ensure that the Integration perspective is selected.
In the Project Repository, expand Metadata, right-click Hadoop Cluster, and click Create Hadoop Cluster to open the wizard.
In the Name field of the Hadoop Cluster Connection wizard, type MyHadoopCluster_files. In the Purpose field, type Cluster connection metadata, in the Description field, type Metadata to connect to a Cloudera CDH 5.4 cluster, and click Next.

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Running a Job on YARN

In this tutorial, create a Big Data batch Job running on YARN, read data from HDFS, sort them and display them in the Console.

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Running a Job on Spark

Learn how to create a Big Data batch Job using the Spark framework, read data from HDFS, sort them and display them in the Console.

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Best Practices Report: Multiplatform Data Architectures

This Multiplatform Data Architectures report explains in detail what MDAs are and do, with a focus on helping data professionals and their business counterparts worldwide architect, govern, and grow their MDAs for better business outcomes via well-integrated and unified distributed data from many sources.

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ETL in the Cloud

Since the dawn of big data, the ETL (extract, transform, and load) process has been the heart that pumps information through modern business networks. Today, cloud-based ETL is a critical tool for managing massive data sets, and one that companies will increasingly rely on in the future.

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What is MapReduce?

MapReduce is a programming model or pattern within the Hadoop framework that is used to access big data stored in the Hadoop File System (HDFS). The map function takes input, pairs, processes, and produces another set of intermediate pairs as output.

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ETL Testing: An Overview

ETL testing refers to tests applied throughout the ETL process to validate, verify, and ensure the accuracy of data while preventing duplicate records and data loss. Learn the 8 stages of ETL testing, 9 types of tests, common challenges, how to find the best tool, and more.

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Big Data Quality

With the advent of big data, data quality management is both more important and more challenging than ever. Fortunately the combination of Hadoop open source distributed processing technologies and Talend open source data management solutions bring big data quality operations within the reach of any organization.

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ETL vs ELT: Defining the Difference

The difference between ETL and ELT lies in where data is transformed into business intelligence and how much data is retained in working data warehouses. Discover what those differences mean for business intelligence, which approach is best for your organization, and why the cloud is changing everything.

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YARN in Hadoop

The recent introduction of YARN in Hadoop provides organizations that are managing big data with even greater processing speed and scalability. An acronym for Yet Another Resource Negotiator, YARN in Hadoop solves a bottleneck in the first version of Hadoop MapReduce and reduces the strict dependency of Hadoop environments on MapReduce.

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YARN MapReduce

YARN in Hadoop provides a new processing platform for big data that is not constrained to MapReduce. Also known as MapReduce 2.0, YARN decouples the resource management and scheduling capabilities from the data processing component in Hadoop, limiting the dependency of Hadoop environments on the MapReduce program.

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Data Warehouse Tools

The market for data warehouse tools and other integration technologies is shifting in favor of open source solutions. Talend is at the forefront of this movement, providing progressive businesses with open source data warehouse tools that deliver as much or more quality and functionality as proprietary solutions, while having substantially lower total cost of ownership.

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Talend, the leading provider of open source data integration solutions, expands the utility of Sqoop by incorporating Sqoop functionality into a unified big data integration solution that's versatile and easy to use.

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