In a previous blog of this series, Turning Streams Into Data Products, we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. We discussed how Cloudera Stream Processing (CSP) with Apache Kafka and Apache Flink could be used to process this data in real time and at scale. In this blog we will show a real example of how that is done, looking at how we can use CSP to perform real-time fraud detection.

Earlier in the quarter we had announced that BigQuery BI Engine support for all BI and custom applications was generally available. Today we are excited to announce the preview launch of Preferred Tables support in BigQuery BI Engine! BI Engine is an in-memory analysis service that helps customers get low latency performance for their queries across all BI tools that connect to BigQuery.

The digitalization of tax and operational transfer pricing processes can have a huge impact on a multinational company’s ability to efficiently forecast and report its tax liability.

BigQuery BI Engine is a fast, in-memory analysis service that lets users analyze data stored in BigQuery with rapid response times and with high concurrency to accelerate certain BigQuery SQL queries. BI Engine caches data instead of query results, allowing different queries over the same data to be accelerated as you look at different aspects of the data.

Fast and clean. These two words define the ideal financial close process. This standard is held up as a measure of a finance or accounting department’s effectiveness. Companies are expected to get the financial close process done within a standard business week. This demonstrates competence, resource efficiency, and good management. An efficient financial consolidation and close process does two vital things.

Today, data drives everything from strategic business decisions to workout plans. As a result, analytics, visualization, and dashboards are becoming necessary in business software, online websites, and consumer apps. With people needing quick access to data for better insights and decisions, embedded analytics has become inevitable in our work.

Much of the hype around big data and analytics focuses on business value and bottom-line impacts. Those are enormously important in the private and public sectors alike. But for government agencies, there is a greater mission: improving people’s lives. Data makes the most ambitious and even idealistic goals—like making the world a better place—possible.

Since we launched Talend Data Fabric in 2015, we’ve believed strongly that merely focusing on the mechanics of data — capturing, moving, and storing data — is not enough to become data-driven. Everyone in the organization must be able to easily find, trust, and use data. That’s what data health is all about, and that’s what Talend makes possible. Forrester looked at the 15 software providers that matter the most when it comes to Enterprise Data Fabric.

Across the globe, cloud concentration risk is coming under greater scrutiny. The UK HM Treasury department recently issued a policy paper “Critical Third Parties to the Finance Sector.” The paper is a proposal to enable oversight of third parties providing critical services to the UK financial system.

In order to better serve their customers and users, digital applications and platforms continue to store and use sensitive data such as Personally Identifiable Information (PII), genetic and biometric information, and credit card information. Many organizations that provide data for analytics use cases face evolving regulatory and privacy mandates, ongoing risks from data breaches and data leakage, and a growing need to control data access.