Data classification and lineage is the first of Talend’s 5 Pillars for GDPR compliance. Data lineage tracks the origin of data, and data classification is the sorting process of data into categories, based on user-defined characteristics.
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Talend Open Studio for Data Integration is the leading open source solution for data integration.
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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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Cloud architecture for IoT refers to the different modules that make up each organization’s system for cloud computing and data processing. A strong cloud architecture helps ease the transition of data through new technologies.
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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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Data redundancy occurs when the same piece of data is held within a database or data storage technology.
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The General Data Protection Regulation (GDPR) is a European Union regulation that will take effect on May 28, 2018, and failure to comply with GDPR can expose your organization to a penalty of up to 4% of global revenue. Get all the details and resources you need to make sure your organization is prepared for GDPR.
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Business intelligence (BI) is the processes, technologies, skills, and applications used to make informed, data-driven business decisions. It includes data collection, data aggregation, analysis, and meaningful presentation that facilitates decision-making.
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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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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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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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Big data describes the process of 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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Learn to diagnose when you need an ETL tool, and get a list of key features to look for.
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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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Database integration is the process used to aggregate information from multiple sources and share a current, clean version of it across an organization. It is the operational core of big data. Here’s a look at the process, partners, and tools used in integration.
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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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Data integration is the process of combining data from several different sources into a unified view, making it more actionable and valuable to those accessing it. Organizations across all professional fields establish data integration initiatives to analyze their data more effectively, which helps improve strategic decision making and increase the competitiveness of a business.
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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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Gartner mentioned that 90% of deployed data will be useless and Experian mentioned about 32% of data in US firms to be inaccurate. The key takeaway is that data is the most valuable asset for any company. So it would be a shame to completely discard or let it lie dormant in an abandoned data lake somewhere. It’s imperative that all data scientists tap into their swelling pools of IoT data to make sense of the various endpoints of information and help develop conclusions that will ultimately deliver business outcomes.
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Without metadata, there is no way to create a holistic and actionable view of the information supply chain.
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