/Recent content on CDAPHugo -- gohugo.io/accelerators/Mon, 01 Jan 0001 00:00:00 +0000/accelerators//accelerators/analytics/Mon, 01 Jan 0001 00:00:00 +0000/accelerators/analytics//case-studies/bi-migration/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/bi-migration/Scenario The customer, a Health Insurance company, was using Netezza to aggregate and report on multiple dimensions on different health care and services. They were looking for alternatives to offload the data from Netezza to reduce the workload for reducing the cost. Data Administrators, Data Analysts, Data Scientist and Engineers were operating and supporting the loading, cleansing and reporting efforts. They were facing a lot of difficulties in the following areas that was hindering their transition into Hadoop:/case-studies/data-lake/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/data-lake/Scenario The customer, a Health Insurance company, was using Netezza to aggregate and report on multiple dimensions on different health care and services. They were looking for alternatives to offload the data from Netezza to reduce the workload for reducing the cost. Data Administrators, Data Analysts, Data Scientist and Engineers were operating and supporting the loading, cleansing and reporting efforts. They were facing a lot of difficulties in the following areas that was hindering their transition into Hadoop:/case-studies/finance/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/finance/Scenario The customer, a Fortune 50 financial institution, created a pipeline that aggregates batched data into a secured on-premise cluster to create daily aggregates and reports. The current system performed multiple transformations, which created new datasets. The customer faced multiple issues: The data pipeline was inefficient, took 6 hours to run, and required manual intervention almost on a daily basis Reports were not aligning correctly with day boundaries. Any points of failure require reconfiguring and restarting the pipeline, a time-consuming and frustrating task./case-studies/finance2/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/finance2/Scenario The customer, a Fortune 500 company in the Financial sector, had custom-built a data pipeline to perform data validation and correction transforms. The pipeline was constructed using multiple complex technologies. Examples performed on the 3 billion records included: Standardization, verification, and cleansing of USPS codes Domain set validation, Null Checks, Length Checks Regular expression validation (email, SSN, dates, etc.) The legacy pipeline ran overnight, required multiple teams to keep it operating, and costly experts to maintain it./case-studies/fraud-detection/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/fraud-detection/Scenario The customer, a Health Insurance company, was using Netezza to aggregate and report on multiple dimensions on different health care and services. They were looking for alternatives to offload the data from Netezza to reduce the workload for reducing the cost. Data Administrators, Data Analysts, Data Scientist and Engineers were operating and supporting the loading, cleansing and reporting efforts. They were facing a lot of difficulties in the following areas that was hindering their transition into Hadoop:/get-started/Mon, 01 Jan 0001 00:00:00 +0000/get-started//case-studies/healthcare/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/healthcare/Scenario The customer, a Health Insurance company, was using Netezza to aggregate and report on multiple dimensions on different health care and services. They were looking for alternatives to offload the data from Netezza to reduce the workload for reducing the cost. Data Administrators, Data Analysts, Data Scientist and Engineers were operating and supporting the loading, cleansing and reporting efforts. They were facing a lot of difficulties in the following areas that was hindering their transition into Hadoop:/case-studies/sports-and-entertainment/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/sports-and-entertainment/Scenario The customer, a Sports and Entertainment company has arriving in XML files and the data scientists couldn’t query for the information they needed. They weren’t sure what the final format would be for the tables and needed to be able to adjust quickly. They required access to the final data from their existing BI tools such as Tableau and R. CDAP value proposition(s) CDAP enables customers to ingest and transform the XML data into multiple tables without writing any code./accelerators/pipelines/Mon, 01 Jan 0001 00:00:00 +0000/accelerators/pipelines//resources/plugins/Mon, 01 Jan 0001 00:00:00 +0000/resources/plugins//case-studies/retail/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/retail/Scenario The customer, a Health Insurance company, was using Netezza to aggregate and report on multiple dimensions on different health care and services. They were looking for alternatives to offload the data from Netezza to reduce the workload for reducing the cost. Data Administrators, Data Analysts, Data Scientist and Engineers were operating and supporting the loading, cleansing and reporting efforts. They were facing a lot of difficulties in the following areas that was hindering their transition into Hadoop:/case-studies/telco/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/telco/Scenario The customer, a Fortune 50 company in the Telecom sector, developed a legacy custom data pipeline that performed format-preserving encryption and data masking. The pipeline extracted data from Teradata to HDFS, performed transformations, and loaded the results back into Teradata on a daily basis. This pipeline, built by a third-party service, was operationally unstable and required constant, costly intervention to keep it running. CDAP value proposition(s) The self-service, code-free interface allowed the in-house team to reproduce and replace the existing pipeline./case-studies/risk-analytics/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/risk-analytics/Scenario The customer, a Health Insurance company, was using Netezza to aggregate and report on multiple dimensions on different health care and services. They were looking for alternatives to offload the data from Netezza to reduce the workload for reducing the cost. Data Administrators, Data Analysts, Data Scientist and Engineers were operating and supporting the loading, cleansing and reporting efforts. They were facing a lot of difficulties in the following areas that was hindering their transition into Hadoop:/accelerators/rules-engine/Mon, 01 Jan 0001 00:00:00 +0000/accelerators/rules-engine//case-studies/e-commerce/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/e-commerce/Scenario A Fortune 500 e-commerce company built a data pipeline that ingested their Twitter stream in real-time. The data was cleansed and transformed prior to conducting multi-dimensional aggregation and sentiment analysis on marketing campaigns based on tweets. The results were updated twice daily to HBase. However, the legacy pipeline suffered on two fronts: first, latency in the existing pipeline delayed the decision making process. Second, the existing data movement process proved to be costly in time and money./case-studies/telecommunications/Mon, 01 Jan 0001 00:00:00 +0000/case-studies/telecommunications/Scenario The customer, a Health Insurance company, was using Netezza to aggregate and report on multiple dimensions on different health care and services. They were looking for alternatives to offload the data from Netezza to reduce the workload for reducing the cost. Data Administrators, Data Analysts, Data Scientist and Engineers were operating and supporting the loading, cleansing and reporting efforts. They were facing a lot of difficulties in the following areas that was hindering their transition into Hadoop:/accelerators/videos/Mon, 01 Jan 0001 00:00:00 +0000/accelerators/videos//resources/videos/Mon, 01 Jan 0001 00:00:00 +0000/resources/videos//accelerators/wrangler/Mon, 01 Jan 0001 00:00:00 +0000/accelerators/wrangler/