Everything Data Scientists Should Know About Organizing Data Lakes

The recent GDPR regulations have changed the way companies handle the data they collect and store, making it imperative for data scientists to explore innovative ways to crunch and catalog data while ensuring that the company adheres to these new rulings that seek to establish complete data security.Join Big Data Expert Ronald Van Loon, and Simplilearns Chief Product Officer Anand Narayanan in a live video chat to understand.
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OTHER ON-DEMAND WEBINARS

How to Use Data Prep to Accelerate Cloud Data Lake Adoption

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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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How to Stay Ahead of a Crisis, and What to Do if You Can't

Imagine you’re a luxury brand releasing a product to a rapt and loyal audience. But instead of receiving rave reviews, your product incites controversy—and social chatter swells around that negative impression. Suddenly, the brand is making headlines that threaten its reputation and could alienate customers. How can you implemen
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Global Data Management in the Cloud

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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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What’s Ahead in Data Management in 2019?

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This webinar is a must attend for technical users and business managers who are facing these changes. The expert panel on this webinar will help attendees understand what’s ahead in 2019 and beyond for data management. Attendees can then apply that information to prioritize the data management changes they must address and how they will prepare via hiring, training, budgeting, making a business case, and adopting the right data platforms and tools.
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