The Example-Rich, Hands-On Guide to Data Munging with Apache HadoopTM
Data scientists spend much of their time “munging” data: handling day-to-day tasks such as data cleansing, normalization, aggregation, sampling, and transformation. These tasks are both critical and surprisingly interesting. Most important, they deepen your understanding of your data’s structure and limitations: crucial insight for improving accuracy and mitigating risk in any analytical project.
Now, two leading Hortonworks data scientists, Ofer Mendelevitch and Casey Stella, bring together powerful, practical insights for effective Hadoop-based data munging of large datasets. Drawing on extensive experience with advanced analytics, the authors offer realistic examples that address the common issues you’re most likely to face. They describe each task in detail, presenting example code based on widely used tools such as Pig, Hive, and Spark.
This concise, hands-on eBook is valuable for every data scientist, data engineer, and architect who wants to master data munging: not just in theory, but in practice with the field’s #1 platform–Hadoop.
A framework for understanding the various types of data quality checks, including cell-based rules, distribution validation, and outlier analysis
Assessing tradeoffs in common approaches to imputing missing values
Implementing quality checks with Pig or Hive UDFs
Transforming raw data into “feature matrix” format for machine learning algorithms
Choosing features and instances
Implementing text features via “bag-of-words” and NLP techniques
Handling time-series data via frequency- or time-domain methods
Manipulating feature values to prepare for modeling
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