Full Resource Library

What is ELT?

ELT is the process by which raw data is extracted, loaded, and transformed into a data lake or warehouse. In contrast to ETL, ELT provides faster loading.

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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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What is Hadoop?

Hadoop is an open source, Java based framework used for storing and processing big data. The data is stored on inexpensive commodity servers that run as clusters. Its distributed file system enables concurrent processing and fault tolerance.

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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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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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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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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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Sqoop

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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Hadoop Hive

Talend, the open source integration company, delivers seamless Hadoop Hive support in Talend Open Studio for Big Data. The first pure open source big data management solution, Talend Open Studio for Big Data makes it easy to work with Hadoop Hive and to integrate Hive into your enterprise data flows.

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Big Data Integration

Big data integration is a key operational challenge for today's enterprise IT departments. Talend, the leading provider of open source data management solutions, helps organizations large and small meet the big data challenge by making big data integration easy, fast, and affordable.

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Data Integration

In the past, IT departments tasked with responding to pressing technological challenges such as data integration had to choose between building custom scripts in-house or purchasing proprietary software, often at great expense – initial and ongoing. Now, forward-looking IT groups are increasingly turning to open source solutions for their companies' needs.

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MapReduce

As a software framework for distributed processing of large data sets on clusters of commodity hardware, MapReduce is essential for achieving the speed, resilience and processing power that big data can deliver.

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Architecting an Open Data Lake for the Enterprise

Learn how to reduce development time and innovate on AWS. Beachbody - sellers of fitness, weight loss, and muscle-building home-exercise videos - talks about their experience migrating to a data lake on Amazon Simple Storage Service (Amazon S3) using Talend.

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