There are many ways that Apache Kafka has been deployed in the field. In our Kafka Summit 2021 presentation, we took a brief overview of many different configurations that have been observed to date. In this blog series, we will discuss each of these deployments and the deployment choices made along with how they impact reliability.
Leveraging the Internet of Things (IoT) allows you to improve processes and take your business in new directions. But it requires you to live on the edge. That’s where you find the ability to empower IoT devices to respond to events in real time by capturing and analyzing the relevant data.
There are two big gaps in the Apache Kafka project when we think of operating a cluster. The first is monitoring the cluster efficiently and the second is managing failures and changes in the cluster. There are no solutions for these inside the Kafka project but there are many good 3rd party tools for both problems. Cruise Control is one of the earliest open source tools to provide a solution for the failure management problem but lately for the monitoring problem as well.
Shared Data Experience (SDX) on Cloudera Data Platform (CDP) enables centralized data access control and audit for workloads in the Enterprise Data Cloud. The public cloud (CDP-PC) editions default to using cloud storage (S3 for AWS, ADLS-gen2 for Azure). This introduces new challenges around managing data access across teams and individual users. To solve these challenges for S3 and ADLS-gen2, Cloudera has introduced a new service — the Ranger Authorization Service (RAZ).
Across the federal government, agencies are struggling to identify, organize, analyze, and act on troves of data. It’s a problem that leaders are working actively to tackle, but they’re in a race against immeasurable volumes of data that is continuously being generated in perpetuity in stores known and unknown. At the Internal Revenue Service, decades’ worth of data exceeds even the most cutting-edge processing capabilities.
The CDP Operational Database (COD) builds on the foundation of existing operational database capabilities that were available with Apache HBase and/or Apache Phoenix in legacy CDH and HDP deployments.
In recent years there has been increased interest in how to safely and efficiently extend enterprise data platforms and workloads into the cloud. CDOs are under increasing pressure to reduce costs by moving data and workloads to the cloud, similar to what has happened with business applications during the last decade. Our upcoming webinar is centered on how an integrated data platform supports the data strategy and goals of becoming a data-driven company.
The shift to cloud has been accelerating, and with it, a push to modernize data pipelines that fuel key applications. That is why cloud native solutions which take advantage of the capabilities such as disaggregated storage & compute, elasticity, and containerization are more paramount than ever. At Cloudera, we introduced Cloudera Data Engineering (CDE) as part of our Enterprise Data Cloud product — Cloudera Data Platform (CDP) — to meet these challenges.
The more an enterprise wants to know about itself and its business prospects, the more data it needs to collect and analyze. Additionally, the more data it collects and stores, the better its ability to know customers, to find new ones, and to provide more of what they want to buy. Sounds simple, but a surprising majority of U.S.
Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform.
