Cloudera Engineering Blog · Hadoop Posts
Learn how to read FIX message files directly with Hive, create a view to simplify user queries, and use a flattened Apache Parquet table to enable fast user queries with Impala.
The Financial Information eXchange (FIX) protocol is used widely by the financial services industry to communicate various trading-related activities. Each FIX message is a record that represents an action by a financial party, such as a new order or an execution report. As the raw point of truth for much of the trading activity of a financial firm, it makes sense that FIX messages are an obvious data source for analytics and reporting in Apache Hadoop.
This new feature gives Hadoop admins the commonplace ability to replace failed DataNode drives without unscheduled downtime.
Hot swapping—the process of replacing system components without shutting down the system—is a common and important operation in modern, production-ready systems. Because disk failures are common in data centers, the ability to hot-swap hard drives is a supported feature in hardware and server operating systems such as Linux and Windows Server, and sysadmins routinely upgrade servers or replace a faulty components without interrupting business-critical services.
Apache Hadoop ecosystem, time to celebrate! The much-anticipated, significantly updated 4th edition of Tom White’s classic O’Reilly Media book, Hadoop: The Definitive Guide, is now available.
The Hadoop ecosystem has changed a lot since the 3rd edition. How are those changes reflected in the new edition?
Thanks to Big Data Solutions Architect Matthieu Lieber for allowing us to republish the post below.
A customer of mine wants to take advantage of both worlds: work with his existing Apache Avro data, with all of the advantages that it confers, but take advantage of the predicate push-down features that Parquet provides. How to reconcile the two?
Having a good grasp of HDFS recovery processes is important when running or moving toward production-ready Apache Hadoop. In the conclusion to this two-part post, pipeline recovery is explained.
An important design requirement of HDFS is to ensure continuous and correct operations that support production deployments. For that reason, it’s important for operators to understand how HDFS recovery processes work. In Part 1 of this post, we looked at lease recovery and block recovery. Now, in Part 2, we explore pipeline recovery.
Having a good grasp of HDFS recovery processes is important when running or moving toward production-ready Apache Hadoop.
An important design requirement of HDFS is to ensure continuous and correct operations to support production deployments. One particularly complex area is ensuring correctness of writes to HDFS in the presence of network and node failures, where the lease recovery, block recovery, and pipeline recovery processes come into play. Understanding when and why these recovery processes are called, along with what they do, can help users as well as developers understand the machinations of their HDFS cluster.
Thanks to Călin-Andrei Burloiu, Big Data Engineer at antivirus company Avira, and Radu Pastia, Senior Software Developer in the Big Data Team at Orange, for the guest post below about the Couchdoop connector for bringing Couchbase data into Hadoop.
Couchdoop is a Couchbase connector for Apache Hadoop, developed by Avira on CDH, that allows for easy, parallel data transfer between Couchbase and Hadoop storage engines. It includes a command-line tool, for simple tasks and prototyping, as well as a MapReduce library, for those who want to use Couchdoop directly in MapReduce jobs. Couchdoop works natively with CDH 5.x.
Couchdoop can help you:
You may have noticed that this report went on hiatus for December 2014 due to a lack of critical news mass (plus, we realize that most of you are out of the loop until mid-January). It’s back with a vengeance, though:
Strata + Hadoop World San Jose 2015 (Feb. 17-20) is a focal point for learning about production-izing Hadoop.
Strata + Hadoop World sessions have always been indispensable for learning about Hadoop internals, use cases, and admin best practices. When deep learning is needed, however—and deep dives are a necessity if you’re running Hadoop in production, or aspire to—tutorials are your ticket.
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.