How to Use Data Prep to Accelerate Cloud Data Lake Adoption

What began as a trickle is now the mainstream: Organizations are moving to the cloud for data management. No longer is the cloud just a cheaper place to park data; it is key to supporting business-critical innovation in advanced analytics, data science, and AI as well as for end-user business intelligence, data exploration, and data visualization. Data lakes and data warehouses running on market-leading platforms such as AWS are growing fast, just as they are on the platforms of competing providers. However, as cloud-based workloads grow in number and size, organizations are facing difficult data preparation challenges. The flow of data raw, diverse, and frequently unstructured can quickly turn cloud data lakes into impenetrable swamps. Without good data preparation technologies and practices, users of all types are frustrated; their productivity and satisfaction suffer because it’s too hard to get accurate data that’s appropriately transformed and structured for their purposes.
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OTHER ON-DEMAND WEBINARS

The Many Faces of Metadata Management

tdwi.org

Few things bring down the value of data faster than confusion and uncertainty about what it is, where it came from, and whether it is good quality data. Yet as more users seek to access and interact with data and reports for business intelligence and analytics and as data sources become larger and more varied, confusion and uncertainty spread fast. Executives, managers, regulatory administrators, and other key personnel cannot rely on their reports, KPIs, and dashboards. Users cannot even find reports that the organization is producing. Instead, users spend more of their time trying to locate data and reports and correcting mistakes than they do applying data insights to solve business problems.
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Global Data Management in the Cloud

tdwi.org

Organizations that operate worldwide typically need to manage data both locally and globally. Local business units and subsidiaries must address region-specific data and accounting standards, regulations, customer requirements, and market drivers. At the same time, corporate headquarters must share data broadly and maintain a complete view of performance for the entire enterprise. For many global firms, data is the business. They need state-of-the-art data management just to remain innovative and competitive. Hence, multinational businesses face a long list of new business and technical requirements for modern data management.
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Big Data as a Gateway to Knowledge Management

dataversity.net

Knowledge management may be making a comeback the term we heard about in the early noughts, a formal system that helps manage what an organization knows. Developments in artificial intelligence and database technology have brought the promises of knowledge management back into the forefront.
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Faster AI Deployment on Hadoop and Spark with RapidMiner and Microsoft Azure HDInsight

rapidminer.com

Rapid Miner and Azure HDInsight work together to deliver a complete data science and machine learning platform for massive amounts of data using popular open source frameworks such as Hadoop, Hive, MapReduce, and Spark.Hear from RapidMiner Product Manager, Jesus Puente and Cloud Software Engineer Beth Zeranski, Cloud at Microsoft for this 60-minute webinar where they will discuss.
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