https://dataprd.com/tags/data-governance/ Recent content in Data Governance on Dataprd.Com Hugo en-us Fri, 25 Aug 2017 16:11:15 +0000 https://dataprd.com/posts/data-governance-in-the-big-data-and-hadoop-world-whitepaper/ Fri, 25 Aug 2017 16:11:15 +0000 https://dataprd.com/posts/data-governance-in-the-big-data-and-hadoop-world-whitepaper/ <p><strong><em>My full whitepaper — <a href="https://dataprd.com/files/Data_Governance_2015.pdf" title="Data Governance in the Big Data and Hadoop World">Download PDF</a></em></strong></p> <p>Amidst vast data lakes and a high velocity of incoming data, enterprises are finding that despite the procedures, policies, and systems that are already in place, their data governance frameworks still aren&rsquo;t delivering the insights needed to drive smart business decisions. Pain points such as privacy and security, appropriate permissions, and labor-intensive data mining processes are challenging general data governance frameworks that do not allow for flexibility or agility. Hadoop can be an excellent framework to support a large data storage repository with immense processing power. The key to success is understanding how to unleash Hadoop&rsquo;s powerful storage capabilities. This white paper reveals:</p> https://dataprd.com/posts/top-7-challenges-of-building-a-data-lake/ Sat, 07 Jan 2017 18:26:42 +0000 https://dataprd.com/posts/top-7-challenges-of-building-a-data-lake/ <p>While from the technical perspective, deployment, management and provisioning tools are available to quickly set up a Hadoop cluster, introducing it to the organization is a tough task. Here are the biggest challenges of building a data lake <strong>1. Understand the purpose and limitations of the technology</strong></p> <ul> <li>Hadoop environment provides a vendor independent tool-chain for data storage, management and analytics in a scalable fashion - it can manage infinite amount of data and with add-ons makes real-time and data science use-cases available for the enterprise</li> <li>Many organizations do not have Big Data. They just load their RDBMS content and get surprised that a 20 node enterprise Hadoop setup&rsquo;s performance is subpar compared with a single PostgreSQL instance. Sure, because the purpose and capabilities are different - we should compare apples with apples after all</li> <li>In 95% of the cases stakeholders think that this new technology will substitute their painful proprietary data environment of RDBMSs. It is a highly distributed environment so the case is that it is never capable to substitute proprietary data systems. But it can live well beneath those providing extreme value. The purpose of the data lake is different. Ingest data, all of your data, and deal with portions of it without costly ETLs to move it from here to there. Refine your data system, be it processing, reporting or prediction models by slowly adding more datasets.</li> </ul> <p><strong>2. Security</strong></p>