Writing and Reading Data in HDFS
In this tutorial, generate random data and write them to HDFS. Then, read the data from HDFS, sort them and display the result 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.
Creating Cluster Connection Metadata
Learn how to create Hadoop Cluster metadata automatically by connecting to the Cloudera Manager.
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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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Get Value Out of Your Data With a 360° Data Hub
This white paper shows you what is a data hub and why are the most forward-looking companies creating them.
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Apache Sqoop: A Complete Guide
Apache Sqoop is an instrument expressly designed to import and export structured data into and out of Hadoop and repositories like relational databases, data warehouses, and NoSQL stores.
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Data Extraction Tools: Improving Data Warehouse Performance
Data extraction tools significantly expedite the collection of relevant data. Learn about the tool and the benefits achieved by leveraging the right data extraction software.
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Why ELT Tools Are Disrupting the ETL Market
With 50% of business data residing in the cloud, more organizations are turning to ELT tools to address their processing needs. Learn how ELT tools are impacting the future of data integration.
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How to Move Data from Salesforce to AWS Redshift
Learn how to extract CRM data from Salesforce and load it into the Amazon Web Services (AWS) Redshift cloud data warehouse.
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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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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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O’Reilly Report: Moving Hadoop to the Cloud
There’s a lot more to deploying Hadoop to the public cloud than simply renting machines. The right selection and set up helps you harness the features and flexibility of your cloud service to optimize your big data projects.
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Data Lakes: Purposes, Practices, Patterns, and Platforms
TDWI surveyed top data management professionals to discover 12 priorities for a successful data lake implementation. With the right tools, a data lake enables self-service data access and extends programs for data warehousing, analytics, data integration, and more data-driven solutions.
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Self-Service Analytics
Organizations today are supercharged by data, but most only analyze a fraction of what they collect. Find out how self-service analytics is building a data-driven culture across a range of companies.
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