insightsoftware to Strengthen xP&A and Write-Back Capabilities in Qlik With Fiplana Acquisition
Deal will extend insightsoftware’s operational planning, reporting, and analytics capabilities in the Qlik ecosystem.
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Analytics
Defining Asynchronous Microservice APIs for Fraud Detection | Designing Event-Driven Microservices
In this video, Wade explores the process of decomposing a monolith into a series of microservices. You'll see how Tributary bank extracts a variety of API methods from an existing monolith. Tributary Bank wants to decompose its monolith into a series of microservices. They are going to start with their Fraud Detection service. However, before they can start, they first have to untangle the existing code. They will need to define a clean API that will allow them to move the functionality to an asynchronous, event-driven microservice.
Data Science vs. Data Analytics: Key Differences
Organizations increasingly use data to gain a competitive edge. Two key disciplines have emerged at the forefront of this approach: data science and data analytics. While both fields help you extract insights from data, data analytics focuses more on analyzing historical data to guide decisions in the present. In contrast, data science enables you to create data-driven algorithms to forecast future outcomes. These disciplines differ significantly in their methodologies, tools, and outcomes.
5 Key Data Governance Principles for Effective Data Management
Digitalization has led to more data collection, integral to many industries from healthcare diagnoses to financial transactions. For instance, hospitals use data governance practices to break siloed data and decrease the risk of misdiagnosis or treatment delays. Therefore, managing data to protect its integrity and security leads to high-quality, reliable data that empowers organizations to make informed decisions.
Log and Event Analytics on Databricks: Overview and Use Cases
Built on the foundation of Apache Spark, Databricks is a unified, open data lakehouse platform that empowers customers to efficiently and cost-effectively process, store, manage, and analyze large volumes of enterprise data.
Unify your data: AI and Analytics in an Open Lakehouse
Cloudera customers run some of the biggest data lakes on earth. These lakes power mission-critical, large-scale data analytics and AI use cases—including enterprise data warehouses. Nearly two years ago, Cloudera announced the general availability of Apache Iceberg in the Cloudera platform, which helps users avoid vendor lock-in and implement an open lakehouse. With an open data lakehouse powered by Apache Iceberg, businesses can better tap into the power of analytics and AI.
Bringing Financial Services Business Use Cases to Life: Leveraging Data Analytics, ML/AI, and Gen AI
The financial services industry is undergoing a significant transformation, driven by the need for data-driven insights, digital transformation, and compliance with evolving regulations. In this context, Cloudera and TAI Solutions have partnered to help financial services customers accelerate their data-driven transformation, improve customer centricity, ensure compliance with regulations, enhance risk management, and drive innovation.
Navigating the Jet Hub
Discover the power of the Jet Hub. This video unveils how you can manage your reports and data sources.