With the abundance of deep learning frameworks available today, it can be difficult to know what to choose for any particular application. Given the contrasting strengths and weaknesses of these frameworks, the ability to work with and switch between more than one is particularly important. Recent Cloudera blogs have shown how examples of applying deep learning on the Cloudera ecosystem using popular frameworks Deeplearning4j, BigDL, and Keras+TensorFlow.
Cloudera recently published a blog post on how to use Deeplearning4J (DL4J) along with Apache Hadoop and Apache Spark to get state-of-the-art results on an image recognition task. Continuing on a similar stream of work, in this post we discuss a viable alternative that is specifically designed to be used with Spark, and data available in Spark and Hadoop clusters via a Scala or Python API.
The Deep Learning landscape is still evolving.
As customers use Apache Hadoop clusters in ways other than through HUE and Hadoop Command Line Interface (CLI) and integrate it closely with the applications they develop, we often get asked how to access their secure Hadoop cluster from within the custom applications. Many customers use a service account in their application and access the cluster with a fixed service account. However, other customers would like to access as the end users who have authenticated to the application.
When it comes to self-service business intelligence and exploratory analytics, Cloudera has continued to push limits and innovate to help our customers expedite this journey and get the most value from their data. Over the past year, we have made a number of significant advancements in Hue to provide a more powerful user experience for SQL developers and make them more productive for their every day self-service BI tasks and workflows.
With the recent release of Cloudera 5.12,
An ingest pattern that we commonly see being adopted at Cloudera customers is Apache Spark Streaming applications which read data from Kafka. Streaming data continuously from Kafka has many benefits such as having the capability to gather insights faster. However, users must take into consideration management of Kafka offsets in order to recover their streaming application from failures. In this post, we will provide an overview of Offset Management and following topics.
- Storing offsets in external data stores
- Not managing offsets
Overview of Offset Management
Spark Streaming integration with Kafka allows users to read messages from a single Kafka topic or multiple Kafka topics.