Traditional messaging models fall into two categories: Shared Message Queues and Publish-Subscribe models. Both models have their own pros and cons. Neither could successfully handle big data ingestion at scale due to limitations in their design. Apache Kafka implements a publish-subscribe messaging model which provides fault tolerance, scalability to handle large volumes of streaming data for real-time analytics. It was developed at LinkedIn in 2010 to meet its growing data pipeline needs. Apache Kafka bridges the gaps that traditional messaging models failed to achieve.
One of the worst things that can happen in mission-critical production environments is loss of data and another is downtime. For a search service that provides end users with easy access to data using natural language, downtime would mean complete halt for those parts of your organization. Even worse if the search service is fueling your online business, it interrupts your customer access and end user experience.
That is why we designed multiple options of backup and disaster recovery for your data served via Cloudera Search,
This article was originally posted by Tom Smith Research Analyst and Business Stratgist, DZone, Inc on their website and is being shared here with permission.
Doug Cutting, Chief Architect at Cloudera, shares how the company uses open-source software to help companies use data to improve their business.
What does your company use open-source software to accomplish?
Everything we do.
This is a guest blog post from Jasper Pult, Technology Consultant at Lufthansa Industry Solutions, an international IT consultancy covering all aspects of Big Data, IoT and Cloud. The below work was implemented using Director’s API v9 and certain API details might change in future versions.
Cloud computing is quickly replacing traditional on premises solutions in all kinds of industries. With Apache Hadoop workloads often varying in resource requirements over time,
We are excited to announce the general availability of Cloudera Altus SDK for Java to programmatically leverage the Altus platform-as-a service for ETL, batch machine learning, and cloud bursting. Altus empowers customers and partners alike, to run data engineering workloads in the cloud, leveraging cloud infrastructures such as AWS. Cloudera Altus also provides the ability to create data engineering pipelines using both a web console and CLI.
Cloudera Altus SDK for Java was developed to provide easier programmatic access with the popular Java programming language so that users can automate their data engineering workloads.