5 Ways to Optimize Your Big Data

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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Preparing for GDPR

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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What is Business Intelligence (BI)?

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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What is a Data Lake?

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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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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Defining Big Data Analytics for the Cloud

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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What is Big Data? Free Guide and Definition

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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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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Building a Governed Data Lake in the Cloud

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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What is Master Data Management?

Master data management (MDM) is a method of enabling an organization to always work with—and make decisions based on—one version of current, ‘true’ data. Discover how it benefits a business, what challenges to plan for, and how to get started.

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What is Database Integration?

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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Using Machine Learning for Data Quality

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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What is Data Integration?

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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Edge Analytics – The Pros and Cons of Immediate, Local Insight

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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