Data is the fuel that drives government, enables transparency, and powers citizen services. But while state and local governments seek to improve policies, decision making, and the services constituents rely upon, data silos create accessibility and sharing challenges that hinder public sector agencies from transforming their data into a strategic asset and leveraging it for the common good.

Firms are burdened with tech debt and endless regulatory compliance, often leaving innovation last to receive the necessary budgets. Data-fuelled innovation requires a pragmatic strategy. This blog lays out some steps to help you incrementally advance efforts to be a more data-driven, customer-centric organization.

Cloudera Machine Learning (CML) is a cloud-native and hybrid-friendly machine learning platform. It unifies self-service data science and data engineering in a single, portable service as part of an enterprise data cloud for multi-function analytics on data anywhere. CML empowers organizations to build and deploy machine learning and AI capabilities for business at scale, efficiently and securely, anywhere they want.

We’ve come a long way since 1778 when George Washington’s spies gathered and shared military intelligence on the British Army’s tactical operations in occupied New York. But information broadly, and the management of data specifically, is still “the” critical factor for situational awareness, streamlined operations, and a host of other use cases across today’s tech-driven battlefields.

Cloudera has a strong track record of providing a comprehensive solution for stream processing. Cloudera Stream Processing (CSP), powered by Apache Flink and Apache Kafka, provides a complete stream management and stateful processing solution. In CSP, Kafka serves as the storage streaming substrate, and Flink as the core in-stream processing engine that supports SQL and REST interfaces.

With all of the buzz around cloud computing, many companies have overlooked the importance of hybrid data. Many large enterprises went all-in on cloud without considering the costs and potential risks associated with a cloud-only approach. The truth is, the future of data architecture is all about hybrid.

The previous decade has seen explosive growth in the integration of data and data-driven insight into a company’s ability to operate effectively, yielding an ever-growing competitive advantage to those that do it well. Our customers have become accustomed to the speed of decision making that comes from that insight. Data is integral for both long-term strategy and day-to-day, or even minute-to-minute operation.

In June 2022, Cloudera announced the general availability of Apache Iceberg in the Cloudera Data Platform (CDP). Iceberg is a 100% open-table format, developed through the Apache Software Foundation, which helps users avoid vendor lock-in and implement an open lakehouse. The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse (CDW), Cloudera Data Engineering (CDE), and Cloudera Machine Learning (CML).

Z-order is an ordering for multi-dimensional data, e.g. rows in a database table. Once data is in Z-order it is possible to efficiently search against more columns. This article reveals how Z-ordering works and how one can use it with Apache Impala.

Apache Spark with its rich data APIs has been the processing engine of choice in a wide range of applications from data engineering to machine learning, but its security integration has been a pain point.t Many enterprise customers needi finer granularity of control, in particular at the column and row level (commonly known as Fine Grained Access Control or FGAC).

Cloudera Data Platform (CDP) unifies the technologies from Cloudera Enterprise Data Hub (CDH) and Hortonworks Data Platform (HDP). As part of that unification process, Cloudera merged the YARN Scheduler functionality from the legacy platforms, creating a Capacity Scheduler that better services all customers. In merging this scheduler functionality, Cloudera significantly reduced the time and effort to migrate from CDH and HDP.