How to Modernize Data Lake Technologies with Cloud-Based Solutions

Are you interested in extracting and analyzing financial data? Do you want to be familiar with the concept of big data? Kozminski University has just launched an extraordinary program in big data analysis for you. The Director of the program has a great pleasure to invite you to participate in a webinar aimed at introducing the program and answering the questions regarding the content of the program.

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.

Decisions made at the national and state level are affecting local communities all around the country. But these communities vary greatly. How do local leaders take control to ensure the right decisions are made for their communities? In this Webinar Series, Qlik and IPC Global will demonstrate how near real-time data and analytics can empower local leaders at K-12 and higher education institutions, and state and local government agencies to support decision making and improve service delivery.

The Internet of Things, social media and enterprise content all have one thing in common: they generate vast quantities of data — ever-faster. So much so that picking out relevant information and assembling it into meaningful patterns is a tall order. These new data sources also present another challenge. They generate unstructured data - such as text and emojis - that don’t fit neatly into traditional database structures. This data can however hold valuable insights on important and hard-to-quantify concepts such as social sentiment.