HOW TO GET STARTED WITH HORTONWORKS DATA CLOUD FOR AWS

As organizations move into the cloud, the choices for handling high-scale data for analytical use are flourishing and evolving. How do we address BI/analytics, data science, security/application monitoring, and log data management workloads? Do we really need potentially overlapping warehouse, data lake, and security and observability capabilities on top of object storage, or can an evolved data lake or emerging “lakehouse” platform do it all? Join Doug Henschen, VP and principal analyst at Constellation Research and Thomas Hazel, Chief Technology and Science Officer at ChaosSearch for a broad-ranging discussion on the challenges and strategy considerations that go into choosing the right platform.

Your team is serious about ensuring database performance at scale. But legacy NoSQL technology could be eroding the impact of your achievements. Following best practices for efficient data modeling, query optimization and observability is fundamental. But their power can be limited – or enhanced – by specific database capabilities. Often-overlooked database innovations can serve as a force multiplier, paving a much smoother path to speed at scale (e.g., millions of read/write operations and millisecond P99 response).

The need to monetize data has become a strategic imperative for businesses undergoing digital transformation, whether it’s using data to improve customer engagement, identify compelling opportunities or deliver actionable insight. However, as businesses look to expand revenue-generating, customer-facing applications beyond operational processing to include analytics, they find themselves outgrowing the their transactional database. They need full operational and full analytic capabilities.

If your organization is in a highly-regulated industry or relies on data for competitive advantage data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.