Cloudera Engineering Blog · Use Case Posts
Our thanks to Melanie Imhof, Jonas Looser, Thierry Musy, and Kurt Stockinger of the Zurich University of Applied Science in Switzerland for the post below about their research into the query performance of Impala for mixed workloads.
Recently, we were approached by an industry partner to research and create a blueprint for a new Big Data, near real-time, query processing architecture that would replace its current architecture based on a popular open source database system.
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:
Learn how Spark facilitates the calculation of computationally-intensive statistics such as VaR via the Monte Carlo method.
Under reasonable circumstances, how much money can you expect to lose? The financial statistic value at risk (VaR) seeks to answer this question. Since its development on Wall Street soon after the stock market crash of 1987, VaR has been widely adopted across the financial services industry. Some organizations report the statistic to satisfy regulations, some use it to better understand the risk characteristics of large portfolios, and others compute it before executing trades to help make informed and immediate decisions.
Using an appropriate network representation and the right tool set are the key factors in successfully merging structured and time-series data for analysis.
In Part 1 of this series, you took your first steps for using Apache Giraph, the highly scalable graph-processing system, alongside Apache Hadoop. In this installment, you’ll explore a general use case for analyzing time-dependent, Big Data graphs using data from multiple sources. You’ll learn how to generate random large graphs and small-world networks using Giraph – as well as play with several parameters to probe the limits of your cluster.
Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working as a Principal Big Data Consultant at Allstate Insurance, for the guest post below about his experiences with Impala.
It started with a simple request from one of the managers in my group at Allstate to put together a demo of Tableau connecting to Cloudera Impala. I had previously worked on Impala with a large dataset about a year ago while it was still in beta, and was curious to see how Impala had improved since then in features and stability.
Did you know that using the Crunch API is a powerful option for doing time-series analysis?
Apache Crunch is a Java library for building data pipelines on top of Apache Hadoop. (The Crunch project was originally founded by Cloudera data scientist Josh Wills.) Developers can spend more time focused on their use case by using the Crunch API to handle common tasks such as joining data sets and chaining jobs together in a pipeline. At Cloudera, we are so enthusiastic about Crunch that we have included it in CDH 5! (You can get started with Apache Crunch here and here.)
Our thanks to Amar Parkash, a Software Developer at Goibibo, a leading travel portal in India, for the enthusiastic support of Hue you’ll read below.
At Goibibo, we use Hue in our production environment. I came across Hue while looking for a near real-time log search tool and got to know about Cloudera Search and the interface provided by Hue. I tried it on my machine and was really impressed by the UI it provides for Apache Hive, Apache Pig, HDFS, job browser, and basically everything in the Big Data domain. We immediately deployed Hue in production, and that has been one of the best decisions we have ever made for our data platform at Goibibo.
Our thanks to Janos Matyas, CTO and Founder of SequenceIQ, for the guest post below about his company’s use case for Morphlines (part of the Kite SDK).
SequenceIQ has an Apache Hadoop-based platform and API that consume and ingest various types of data from different sources to offer predictive analytics and actionable insights. Our datasets are structured, unstructured, log files, and communication records, and they require constant refining, cleaning, and transformation.
The following post, by Sarah Cannon of Digital Reasoning, was originally published in that company’s blog. Digital Reasoning has graciously permitted us to re-publish here for your convenience.
At the beginning of each release cycle, engineers at Digital Reasoning are given time to explore the latest in Big Data technologies, examining how the frequently changing landscape might be best adapted to serve our mission. As we sat down in the early stages of planning for Synthesys 3.8 one of the biggest issues we faced involved reconciling the tradeoff between flexibility and performance. How can users quickly and easily retrieve knowledge from Synthesys without being tied to one strict data model?
Spark is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.
Data science is a broad church. I am a data scientist — or so I’ve been told — but what I do is actually quite different from what other “data scientists” do. For example, there are those practicing “investigative analytics” and those implementing “operational analytics.” (I’m in the second camp.)