Cloudera Engineering Blog
Big Data best practices, how-to's, and internals from Cloudera Engineering and the community
Our thanks to Micah Whitacre, a senior software architect on Cerner Corp.’s Big Data Platforms team, for the post below about Cerner’s use case for CDH + Apache Kafka. (Kafka integration with CDH is currently incubating in Cloudera Labs.)
Over the years, Cerner Corp., a leading Healthcare IT provider, has utilized several of the core technologies available in CDH, Cloudera’s software platform containing Apache Hadoop and related projects—including HDFS, Apache HBase, Apache Crunch, Apache Hive, and Apache Oozie. Building upon those technologies, we have been able to architect solutions to handle our diverse ingestion and processing requirements.
Find Cloudera tech talks in Seattle, Las Vegas, London, Madrid, Budapest, Barcelona, Washington DC, Toronto, and other cities through the end of 2014.
Below please find our regularly scheduled quarterly update about where to find tech talks by Cloudera employees—this time, for the remaining dates of 2014. Note that this list will be continually curated during the period; complete logistical information may not be available yet. And remember, many of these talks are in “free” venues (no cost of entry).
The new integration between Flume and Kafka offers sub-second-latency event processing without the need for dedicated infrastructure.
In this previous post you learned some Apache Kafka basics and explored a scenario for using Kafka in an online application. This post takes you a step further and highlights the integration of Kafka with Apache Hadoop, demonstrating both a basic ingestion capability as well as how different open-source components can be easily combined to create a near-real time stream processing workflow using Kafka, Apache Flume, and Hadoop. (Kafka integration with CDH is currently incubating in Cloudera Labs.)
The Case for Flafka
The number of powerful data query tools in the Apache Hadoop ecosystem can be confusing, but understanding a few simple things about your needs usually makes the choice easy.
Ah, the good old days. I recall vividly that in 2007, I was faced to store 1 billion XML documents and make them accessible as well as searchable. I had few choices on a given shoestring budget: build something one my own (it was the rage back then—and still is), use an existing open source database like PostgreSQL or MySQL, or try this thing that Google built successfully and that was now implemented in open source under the Apache umbrella: Hadoop.
This Spark Streaming use case is a great example of how near-real-time processing can be brought to Hadoop.
Spark Streaming is one of the most interesting components within the Apache Spark stack. With Spark Streaming, you can create data pipelines that process streamed data using the same API that you use for processing batch-loaded data. Furthermore, Spark Steaming’s “micro-batching” approach provides decent resiliency should a job fail for some reason.
With Cloudera Director, cloud deployments of Apache Hadoop are now as enterprise-ready as on-premise ones. Here’s the technology behind it.
As part of the recent Cloudera Enterprise 5.2 release, we unveiled Cloudera Director, a new product that delivers enterprise-class, self-service interaction with Hadoop clusters in cloud environments. (Cloudera Director is free to download and use, but commercial support requires a Cloudera Enterprise subscription.) It provides a centralized administrative view for cloud deployments and lets end users provision and scale clusters themselves using automated, repeatable, managed processes. To summarize, the same enterprise-grade capabilities that are available with on-premise deployments are now also available for cloud deployments. (For an overview of and motivation for Cloudera Director, please check out this blog post.)
The combination of OpenShift and Kite SDK turns out to be an effective one for developing and testing Apache Hadoop applications.
At Cloudera, our engineers develop a variety of applications on top of Hadoop to solve our own data needs (here and here). More recently, we’ve started to look at streamlining our development process by using a PaaS (Platform-as-a-Service) for some of these applications. Having single-click deployment and updates to consistent development environments lets us onboard new developers more quickly, and helps ensure that code is written and tested along patterns that will ensure high quality.
Thanks to new improvements in Hue, CDH 5.2 offers the best GUI yet for using Hadoop.
CDH 5.2 includes important new usability functionality via Hue, the open source GUI that makes Apache Hadoop easy to use. In addition to shipping a brand-new app for managing security permissions, this release is particularly feature-packed, and is becoming a great complement to BI tools from Cloudera partners like Tableau, MicroStrategy, and Zoomdata because a more usable Hadoop translates into better BI overall across your organization!
Impala authentication can now be handled by a combination of LDAP and Kerberos. Here’s why, and how.
Impala, the open source analytic database for Apache Hadoop, supports authentication—the act of proving you are who you say you are—using both Kerberos and LDAP. Kerberos has been supported since release 1.0, LDAP support was added more recently, and with CDH 5.2, you can use both at the same time.