Cloudera Engineering Blog · How-to Posts
Thanks to Michael Williams, BIRT Product Evangelist & Forums Manager at analytics software specialist Actuate Corp. (now OpenText), for the guest post below. Actuate is the primary builder and supporter of BIRT, a top-level project of the Eclipse Foundation.
The Actuate (now OpenText) products BIRT Designer Professional and BIRT iHub allow you to connect to multiple data sources to create and deliver meaningful visualizations securely, with scalability reaching millions of users and devices. And now, with Impala emerging as a standard Big Data query engine for many of Actuate’s customers, solid BIRT integration with Impala has become critical.
Learn how to set up a Hadoop cluster in a way that maximizes successful production-ization of Hadoop and minimizes ongoing, long-term adjustments.
Previously, we published some recommendations on selecting new hardware for Apache Hadoop deployments. That post covered some important ideas regarding cluster planning and deployment such as workload profiling and general recommendations for CPU, disk, and memory allocations. In this post, we’ll provide some best practices and guidelines for the next part of the implementation process: configuring the machines once they arrive. Between the two posts, you’ll have a great head start toward production-izing Hadoop.
A new Spark tutorial and Trifacta deployment option make Cloudera Live even more useful for getting started with Apache Hadoop.
When it comes to learning Hadoop and CDH (Cloudera’s open source platform including Hadoop), there is no better place to start than Cloudera Live (cloudera.com/live). With a quick, one-button deployment option, Cloudera Live launches a four-node Cloudera cluster that you can learn and experiment in free for two-weeks. To help plan and extend the capabilities of your cluster, we also offer various partner deployments. Building on the addition of interactive tutorials and Tableau and Zoomdata integration, we have added a new tutorial on Apache Spark and a new Trifacta partner deployment.
Thanks to Ben Harden of CapTech for allowing us to re-publish the post below.
Getting delimited flat file data ingested into Apache Hadoop and ready for use is a tedious task, especially when you want to take advantage of file compression, partitioning and performance gains you get from using the Avro and Parquet file formats.
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.
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.
Using this new tutorial alongside Cloudera Live is now the fastest, easiest, and most hands-on way to get started with Hadoop.
At Cloudera, developer enablement is one of our most important objectives. One only has to look at examples from history (Java or SQL, for example) to know that knowledge fuels the ecosystem. That objective is what drives initiatives such as our community forums, the Cloudera QuickStart VM, and this blog itself.
The key to getting the most out of Spark is to understand the differences between its RDD API and the original Mapper and Reducer API.
Venerable MapReduce has been Apache Hadoop‘s work-horse computation paradigm since its inception. It is ideal for the kinds of work for which Hadoop was originally designed: large-scale log processing, and batch-oriented ETL (extract-transform-load) operations.
The ability to quickly and accurately count complex events is a legitimate business advantage.
In our work as data scientists, we spend most of our time counting things. It is the foundational skill that is used in data cleansing, reporting, feature engineering, and simple-but-effective machine learning models like Naive Bayes classifiers. Hilary Mason has a quote about the benefits of counting that I love:
IPython Notebook and Spark’s Python API are a powerful combination for data science.
The developers of Apache Spark have given thoughtful consideration to Python as a language of choice for data analysis. They have developed the PySpark API for working with RDDs in Python, and further support using the powerful IPythonshell instead of the builtin Python REPL.