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The Chief Data Officer | Digital Transformation

Today, data isn't a cost center. It's a business driver. And Chief Data Officers are responsible for using data to create real results and transform their business. Meet Ray, the CDO at a high tech global electronics manufacturer. Ray relies on the Cloudera Data Platform to bring multiple data sources together, Ray's company can connect supply chain, go-to-market and product research data in one place, while lowering the cost on their network.

Why Replicating HBase Data Using Replication Manager is the Best Choice

In this article we discuss the various methods to replicate HBase data and explore why Replication Manager is the best choice for the job with the help of a use case. Cloudera Replication Manager is a key Cloudera Data Platform (CDP) service, designed to copy and migrate data between environments and infrastructures across hybrid clouds.

Beyond Data Fabrics: Cloudera Modern Data Architectures

As Cloudera CMO David Moxey outlined in his blog, we live in a hybrid data world. Data is growing and continues to accelerate its growth. It is changing in makeup and appearing in ever more places. Driving insight and value from it all is as much of an opportunity as it is a challenge. As a result, it’s getting ​​progressively more complex for businesses to access, use, and create value from it.

Supercharge Your Data Lakehouse with Apache Iceberg in Cloudera Data Platform

We are excited to announce the general availability of Apache Iceberg in Cloudera Data Platform (CDP). Iceberg is a 100% open table format, developed through the Apache Software Foundation, and helps users avoid vendor lock-in. Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP)—including Cloudera Data Warehousing (CDW), Cloudera Data Engineering (CDE), and Cloudera Machine Learning (CML).

Technology Spotlight: Apache Iceberg

At Cloudera, we are committed to staying true to our open source roots and working well within the communities is critical to that. Since 2021, we have supported the growing Iceberg community with hundreds of contributions across Impala, Hive, Spark and Iceberg. We look forward to continuing the momentum as companies embrace the open lakehouse. General release now available in the Cloudera Data Platform.

Fraud Detection with Cloudera Stream Processing Part 1

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.

Introduction to Cloudera Edge Flow Manager

This video is a 101 introduction about Edge Flow Manager (EFM), the Cloudera Edge Management (CEM) solution for managing and monitoring Apache MiNiFi agents at scale. The video goes through all the different views of the user interface to demonstrate and explain all of the features for designing flows, publishing flows to the agents, execute remote commands, monitoring the agents, etc.

Making the World a Better Place with Data

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.

Build Hybrid Data Pipelines and Enable Universal Connectivity With CDF-PC Inbound Connections

In the second blog of the Universal Data Distribution blog series, we explored how Cloudera DataFlow for the Public Cloud (CDF-PC) can help you implement use cases like data lakehouse and data warehouse ingest, cybersecurity, and log optimization, as well as IoT and streaming data collection. A key requirement for these use cases is the ability to not only actively pull data from source systems but to receive data that is being pushed from various sources to the central distribution service.