Category: Data Science
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Bringing machine learning models into production
Developing and bringing machine learning models into production is a task with a lot of challenges. These include model and attribute selection, dealing with missing values, normalization and others. Finding a workflow that puts all the gears, from data preprocessing and analysis over building models and selecting the best performing one to serving the model […]
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Plumber: Getting R ready for production environments?
R Project and Production Running R Project in production is a controversially discussed topic, as is everything concerning R vs Python. Lately there have been some additions to the R Project, that made me look into this again. Researching R and its usage in production environments I came across several packages / project, that can […]
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Apache HAWQ: Full SQL and MPP support on HDFS
Pivotal ported their massively parallel processing (MPP) database Greenplum to Hadoop and made it open source as an incubating project at Apache, called Apache HAWQ. This bring together full ANSI SQL with MPP capabilities and Hadoop integration. The integration in an existing Hadoop installation is easy, as you can integrate all existing data via external […]
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Apache Zeppelin: Visualization and Spark data processing
Apache Zeppelin is a web-based notebook for interactive data analytics. It comes will features for all the steps of data analysis: Data Ingestion Data Discovery Data Analytics Data Visualization & Collaboration Besides that feature set it also supports multiple languages in the backend. Currently it supports languages like: Apache Spark (SQL, PySpark, Java, Scala) R […]
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Apache Spark 2.0
Apache Spark has release version 2.0, which is a major step forward in usability for Spark users and mostly for people, who refrained from using it, due to the costs of learning a new programming language or tool. This is in the past now, as Spark 2.0 supports improved SQL functionalities with SQL2003 support. It […]
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Python vs. R for Data Science
In Data Science there are two languages that compete for users. On one side there is R, on the other Python. Both have a huge userbase, but there is some discussion, which is better to use in a Data Science context. Lets explore both a bit: R R is a language and programming environment especially […]
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Apache Spark: The Next Big (Data) Thing?
Since Apache Spark became a Top Level Project at Apache almost a year ago, it has seen some wide coverage and adoption in the industry. Due to its promise of being faster than Hadoop MapReduce, about 100x in memory and 10x on disk, it seems like a real alternative to doing pure MapReduce. Written in […]
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Comparing Stinger to Impala
With Hadoop 2.0 and the new additions of Stinger and Impala I did a (not representive) test of the performance on a Virtual Box running on my desktop computer. It was using the following setup: 4 GB RAM Intel Core i5 2500 3.3 GHz The datasets were the following: Dataset 1: 71.386.291 rows and 5 […]
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SQL on Hadoop: Facebook’s Presto
Earlier this month Facebook open sourced its own product for using SQL on Hadoop. It is called Presto and is something like Facebook’s answer to Cloudera’s Impala or Hortonwork’s Stinger already presented in an earlier post called SQL and Hadoop on this site. Presto is unlike Hive and more like Impala, since it doesn’t rely […]
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SQL and Hadoop
Bringing SQL to Hadoop has been one of the major trends in Big Data these last twelve months. Reason enough for me to take a closer look at that scene right now. One reason to build an interface based on SQL for Hadoop is to make the technology available for more people. Companies that have […]