Data Warehouse Testing in the Pharmaceutical Industry

This document discusses heterogeneous database systems. It defines a heterogeneous database system as an automated or semi-automated system that integrates disparate database management systems to present a unified query interface to users. It discusses issues in multi-database query processing such as query support, cost, translation and change adaptation. The architecture involves individual databases, wrapper methods, a mediator and query processing/optimization. Database integration involves schema integration through a bottom-up design approach and the conversion of local schemas to a global schema.

This document provides an overview of the process for gathering business requirements for a data management and warehousing project. It discusses why requirements are gathered, the types of requirements needed, how business processes create data in the form of dimensions and measures, and how the gathered requirements will be used to design reports to meet business needs. A straw-man proposal is presented as a starting point for further discussion.

This document proposes creating a data warehouse at Rivier College to address several challenges: data is locked in different systems requiring manual extraction; administrators struggle to pull consistent data for reporting from different sources; and data analysis is basic without standard processes. The goals of the data warehouse are to improve planning and decision making through timely delivery of standardized, repeatable reports from a centralized collection of integrated, nonvolatile data. It will evolve to incorporate more institutional data sources over time.

This document contains information about performance evaluation methods for a data engineer, including examples of performance review phrases. It discusses 12 common methods for evaluating a data engineer's performance: management by objectives, critical incident method, behaviorally anchored rating scales, behavioral observation scales, 360 degree appraisal, and checklist and weighted checklist methods. For each method, it provides details on how the method works and examples of positive and negative phrases that could be used in a performance review. The document is intended to provide useful resources for conducting a data engineer's performance appraisal.

Data pre-processing is a data mining technique that involves transforming raw data into an understandable format. Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. This ppt is about the cleaning and pre-processing.

In this talk we’ll present how at GetYourGuide we’ve built from scratch a completely new ETL pipeline using Debezium, Kafka, Spark and Airflow, which can automatically handle schema changes. Our starting point was an error prone legacy system that ran daily, and was vulnerable to breaking schema changes, which caused many sleepless on-call nights. As most companies, we also have traditional SQL databases that we need to connect to in order to extract relevant data. This is done usually through either full or partial copies of the data with tools such as sqoop. However another approach that has become quite popular lately is to use Debezium as the Change Data Capture layer which reads databases binlogs, and stream these changes directly to Kafka. As having data once a day is not enough anymore for our bussiness, and we wanted our pipelines to be resilent to upstream schema changes, we’ve decided to rebuild our ETL using Debezium. We’ll walk the audience through the steps we followed to architect and develop such solution using Databricks to reduce operation time. By building this new pipeline we are now able to refresh our data lake multiple times a day, giving our users fresh data, and protecting our nights of sleep.

This document discusses the need for observability in data pipelines. It notes that real data pipelines often fail or take a long time to rerun without providing any insight into what went wrong. This is because of frequent code, data, dependency, and infrastructure changes. The document recommends taking a production engineering approach to observability using metrics, logging, and alerting tools. It also suggests experiment management and encapsulating reporting in notebooks. Most importantly, it stresses measuring everything through metrics at all stages of data ingestion and processing to better understand where issues occur.

This document provides an overview of data warehousing, OLAP, data mining, and big data. It discusses how data warehouses integrate data from different sources to create a consistent view for analysis. OLAP enables interactive analysis of aggregated data through multidimensional views and calculations. Data mining finds hidden patterns in large datasets through techniques like predictive modeling, segmentation, link analysis and deviation detection. The document provides examples of how these technologies are used in industries like retail, banking and insurance.

This document summarizes an analysis of unstructured data and text analytics. It discusses how text analytics can extract meaning from unstructured sources like emails, surveys, forums to enhance applications like search, information extraction, and predictive analytics. Examples show how tools can extract entities, relationships, sentiments to gain insights from sources in domains like healthcare, law enforcement, and customer experience.

Data lineage tracking is one of the significant problems that financial institutions face when using modern big data tools. This presentation describes Spline – a data lineage tracking and visualization tool for Apache Spark. Spline captures and stores lineage information from internal Spark execution plans and visualizes it in a user-friendly manner.

This document discusses Informatica's data integration solutions for SAP customers. It summarizes Informatica's strategic relationship with SAP since 1998, their current SAP certifications including for SAP HANA, and details of Informatica's connectivity, integration patterns, and information lifecycle management solutions that are certified to work with SAP applications and HANA. It also provides a benchmark showing high performance for loading and extracting data from SAP HANA using Informatica PowerExchange.

This document discusses enterprise data management. It defines enterprise data management as removing organizational data issues by defining accurate, consistent, and transparent data that can be created, integrated, disseminated, and managed across enterprise applications in a timely manner. It also discusses the need for a structured data delivery strategy from producers to consumers. The document then outlines some key enterprise data categories and provides a conceptual and logical view of an enterprise master data lineage architecture with data flowing between transactional systems, a data management layer, and analytics.

Garbage in, garbage out - we have all heard about the importance of data quality. Having high quality data is essential for all types of use cases, whether it is reporting, anomaly detection, or for avoiding bias in machine learning applications. But where does high quality data come from? How can one assess data quality, improve quality if necessary, and prevent bad quality from slipping in? Obtaining good data quality involves several engineering challenges. In this presentation, we will go through tools and strategies that help us measure, monitor, and improve data quality. We will enumerate factors that can cause data collection and data processing to cause data quality issues, and we will show how to use engineering to detect and mitigate data quality problems.

This document discusses key aspects of migrating a database from SQL Server to Oracle 11g. The major steps in a migration are analysis, migration, testing, and deployment. The migration process involves migrating the schema and objects, business logic, and client applications. Tools like Oracle Migration Workbench and Database Migration Verifier help automate the migration and validation of the migrated schema and data.

What are the evolving approaches to analytics? What is Azure Data Factory? Capabilities of Azure Data Factory

Azure Data Factory can now use Mapping Data Flows to orchestrate ETL workloads. Mapping Data Flows allow users to visually design transformations on data from disparate sources and load the results into Azure SQL Data Warehouse for analytics. The key benefits of Mapping Data Flows are that they provide a visual interface for building expressions to cleanse and join data with auto-complete assistance and live previews of expression results.

This presentation gives a brief introduction to Business Intelligence (BI), its need and its applications.

The document discusses QuerySurge, an automated data testing solution that helps verify data quality and find errors. It notes that traditional data quality tools focus on profiling, cleansing and monitoring data, while QuerySurge also enables data testing through easy-to-use query wizards and comparison of source and target data without SQL coding. QuerySurge allows collaborative testing across teams and platforms, integrates with development tools, and can significantly reduce testing time and improve data quality.

Fast and easy. No Programming needed. The latest QuerySurge release introduces the new Query Wizards. The Wizards allow both novice and experienced team members to validate their organization's data quickly with no SQL programming required. The Wizards provide an immediate ROI through their ease-of-use and ensure that minimal time and effort are required for developing tests and obtaining results. Even novice testers are productive as soon as they start using the Wizards! According to a recent survey of Data Architects and other data experts on LinkedIn, approximately 80% of columns in a data warehouse have no transformations, meaning the Wizards can test all of these columns quickly & easily, (The columns with transformations can be tested using the QuerySurge Design library using custom SQL coding.) There are 3 Types of automated Data Comparisons: - Column-Level Comparison - Table-Level Comparison - Row Count Comparison There are also automated features for filtering (‘Where’ clause) and sorting (‘Order By’ clause). The Wizards provide both novices and non-technical team members with a fast & easy way to be productive immediately and speed up testing for team members skilled in SQL. Trial our software either as a download or in the cloud at www.QuerySurge.com. The trial comes with a built-in tutorial and sample data.

You've made the move to MongoDB for its flexible schema and querying capabilities in order to enhance agility and reduce costs for your business. Shouldn't your data quality process be just as organized and efficient? Using QuerySurge for testing your MongoDB data as part of your quality effort will increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your Big Data store. QuerySurge will help you keep your team organized and on track too! To learn more about QuerySurge, visit www.QuerySurge.com

This document discusses automating enterprise application and data warehouse testing using QuerySurge. It begins with an introduction to QuerySurge and its modules for automating data interface testing. These modules allow testing across different data sources with no coding required. The document then covers data maturity models and how QuerySurge can help improve testing processes. It demonstrates how QuerySurge can automate testing to gain full coverage while decreasing testing time. In conclusion, it discusses how QuerySurge provides value through increased testing efficiency and data quality.

Are you using HPE ALM or Quality Center (QC) for your requirements gathering and test management? RTTS, an alliance partner of HPE and a member of HPE’s Big Data community, can show you how to use ALM/QC and RTTS’ QuerySurge to effectively manage your data validation & testing of Vertica (or any data warehouse). In this webinar video you will see: - a custom view of ALM to store source-to-target mappings - data validation tests in QuerySurge - the execution of QuerySurge tests from ALM - the results of data validation tests stored in ALM - custom ALM reports that show data validation coverage of Vertica how we improve your data quality while reducing your costs & risks Presented by: Bill Hayduk, Founder & CEO of RTTS, the developers of QuerySurge Chris Thompson, Senior Domain Expert, Big Data testing To learn more about QuerySurge, visit www.QuerySurge.com

We explore how extract, transform and load (ETL) testing with SQL scripting is crucial to data validation and show how to test data on a large scale in a streamlined manner with an Informatica ETL testing tool.

This document discusses challenges and opportunities in automating testing for data warehouses and BI systems. It notes that while BI projects have adopted agile methodologies, testing has not. Large and diverse data volumes make testing nearly infinite test cases difficult. It proposes a testing lifecycle and V-model for BI systems. Automating complex functional tests, SQL validation, reconciliation, and test data generation can help address challenges by shortening regression cycles and enabling continuous testing. Various automation tools are discussed, including how they can validate ETL processes and reporting integrity. Automation can help complete testing and ensure data quality, compliance, and performance.