Sales Data Analysis using Dataiku DSS

Dataiku Data Science Studio (DSS), a complete data science software platform, is used to explore, prototype, build, and deliver data products. It significantly reduces the time taken by data scientists, data analysts, and data engineers to perform data loading, data cleaning, data preparation, data integration, and data transformation when building powerful predictive applications.

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Sales Data Analysis using Dataiku DSS

Importing and Analyzing Data in Datameer

Datameer, an end-to-end big data analytics platform, is built on Apache Hadoop to perform integration, analysis, and visualization of massive volumes of both structured and unstructured data. It can be rapidly integrated with any data sources such as new and existing data sources to deliver an easy-to-use, cost-effective, and sophisticated solution for big data analytics.

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Importing and Analyzing Data in Datameer

Kylo Setup for Data Lake Management

Kylo is a feature-rich data lake platform built on Apache Hadoop and Apache Spark. It provides data lake solution enabling self-service data ingest, data preparation, and data discovery. It integrates best practices around metadata capture, security, and data quality. It contains many special purposed routines for data lake operations leveraging Apache Spark and Apache Hive.

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Kylo Setup for Data Lake Management

Call Detail Record Analysis –  K-means Clustering with R

Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics. Most of the telecom companies use CDR information for fraud detection by clustering the user profiles, reducing customer churn by usage activity, and targeting the profitable customers by using RFM analysis.

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Call Detail Record Analysis – K-means Clustering with R

Importing and Analyzing Data in Datameer

Datameer, an end-to-end big data analytics platform, is built on Apache Hadoop to perform integration, analysis, and visualization of massive volumes of both structured and unstructured data. It can be rapidly integrated with any data sources such as new and existing data sources to deliver an easy-to-use, cost-effective, and sophisticated solution for big data analytics.

read more

Importing and Analyzing Data in Datameer

Kylo Setup for Data Lake Management

Kylo is a feature-rich data lake platform built on Apache Hadoop and Apache Spark. It provides data lake solution enabling self-service data ingest, data preparation, and data discovery. It integrates best practices around metadata capture, security, and data quality. It contains many special purposed routines for data lake operations leveraging Apache Spark and Apache Hive.

read more

Kylo Setup for Data Lake Management

Call Detail Record Analysis –  K-means Clustering with R

Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics. Most of the telecom companies use CDR information for fraud detection by clustering the user profiles, reducing customer churn by usage activity, and targeting the profitable customers by using RFM analysis.

read more

Call Detail Record Analysis – K-means Clustering with R

Call Detail Record Analysis –  K-means Clustering with R

Call Detail Record Analysis – K-means Clustering with R

Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics. Most of the telecom companies use CDR information for fraud detection by clustering the user profiles, reducing customer churn by usage activity, and targeting the profitable customers by using RFM analysis.