Installing MiNiFi agents has never been so easy!

This video walks you through one of the new features coming with Edge Flow Manager 1.6.0: the one-line installer command. Did you ever think that installing a MiNiFi (C++ or Java) agent was complicated? Did you ever struggle with generating and configuring the certificates for mTLS communication between the agents and Edge Flow Manager?

Creating a data-driven culture with self service and data literacy

In this segment, Geraldine Wong, CDO of GXS Bank, explains how her bank's data strategy aims to promote inclusion through superior data insights and AI, but achieving this requires building a data-driven culture by providing employees the right tools, access, and knowledge about the data.

Real-time Fraud Detection - Use Case Implementation

When it comes to fraud detection in financial services, streaming data with Confluent enables you to build the right intelligence-as early as possible-for precise and predictive responses. Learn how Confluent's event-driven architecture and streaming pipelines deliver a continuous flow of data, aggregated from wherever it resides in your enterprise, to whichever application or team needs to see it. Enrich each interaction, each transaction, and each anomaly with real-time context so your fraud detection systems have the intelligence to get ahead.

Designing Event-Driven Systems

Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change. Proven approaches such as service-oriented (SOA) and event-driven architectures (EDA) are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but as this practical ebook demonstrates, they provide a more holistic and compelling approach when applied together.

Data Lake vs Data Warehouse

Data warehouses and data lakes represent two of the leading solutions for enterprise data management in 2023. While data warehouses and data lakes may share some overlapping features and use cases, there are fundamental differences in the data management philosophies, design characteristics, and ideal use conditions for each of these technologies.

Active Data Warehouses vs. Traditional Data Warehouses

In the digital age, data is the lifeblood of any organization. The way you store and analyze your data can significantly impact your success. This is where data warehouses come into the picture. Data warehouses are essential for businesses of all sizes, as they provide a central repository for data from a variety of sources, which can then be used for analysis and reporting. This data can be used to make better business decisions, improve operational efficiency, and identify new opportunities.