Forrester has done the research on big data fabric platforms and the results are in. Talend is a Leader in The Forrester Wave: Big Data Fabric, Q2 2018.
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Kafka and other streaming data frameworks now allow for a scalable, flexible way to move and process your streaming data.
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Apache Spark is one of the most powerful tools available for high speed big data operations and management. Spark’s in-memory processing power and Talend’s single-source, GUI management tools are bringing unparalleled data agility to business intelligence.
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Big data is only getting bigger, which means now is the time to optimize. Optimizing big data means (1) removing latency in processing, (2) exploiting data in real time, (3) analyzing data prior to acting, and more. Learn how to get started today.
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A data lake is a central storage repository that holds big data from many sources in a raw format. The benefits of the data lake format are enticing many organizations to ditch their data warehouses. Discover what sets data lakes apart, why they are becoming more popular, and how to start building one.
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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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Data lakes and data warehouses are both widely used for storing big data, but they are not interchangeable terms. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
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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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Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is a time consuming process, but the business intelligence benefits demand it. And today, savvy self-service data preparation tools are making it easier and more efficient than ever.
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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 analytics is the process of translating massive amounts of digital information into useful business intelligence. Utilizing this data, companies can provide actionable information that can be used in real-time to improve business operations, optimize applications for the cloud, and more.
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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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Talend Spring ’18 helps you to build cloud data warehouses and cloud data lakes more quickly and enable faster big data integration.
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Watch the Webinar on Demand and get an overview of supervised Learning, unsupervised Learning and reinforcement Learning methods.
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Big data is the catch-all term used to describe gathering, analyzing, and using massive amounts of digital information to improve operations. It is rapidly changing the way we live, shop, and approach daily life. Understand what big data is and how you can put it to work for you.
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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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The main purpose of a Data Lake is to provide full and direct access to raw (unfiltered) organizational data as an alternative to storing varying and sometimes limited datasets in scattered, disparate data silos.
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Learn how Big Data is changing the DQ methodology. Big Data has made Machine Learning (ML) mainstream and just as DQ has impacted ML, ML is also changing the DQ implementation methodology.
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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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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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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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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.
Learn how to create Hadoop Cluster metadata automatically by connecting to the Cloudera Manager.
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Extract, Transform, Load (ETL) is is the process of gathering data from an unlimited number of sources, organizing it together, and centralizing it into a single repository.
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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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