DataOps Implementation Guide DataOps For Big Data, ETL, Data Migration, Business Intelligence Reporting Data-centric projects are becoming both bigger in size and complexity, which makes execution that much more difficult. This not only creates delays in project execution but also results in poor data quality. More and more projects are facing: Longer time to Market…
Migrating Database to Redshift, Snowflake, Azure DW and Test with iCEDQHow to Migrate to Redshift, Snowflake, Azure Data Warehouse and Test with iCEDQ Many companies are migrating database actively for multiple reasons End of life (Ex. Netezza) Database cannot handle big data Companies moving the database to the cloud (ex. Redshift, Vertica, Snowflake, Azure, etc.) In-house database maintenance…
What Is BI Testing and the Importance of BI Report Testing Reports and dashboards are extensively used to make business decisions. These decisions later form the basis for the company’s growth and success. If BI reports are wrong, then decisions taken based on these reports will also be wrong. Inaccurate reports affect the organization’s credibility…
A Practical Guide for Data Centric Testing: Automated ETL Testing “Information is the oil of the 21st century, and analytics is the combustion engine”- Peter Sondergaard, Senior Vice President, Gartner Big Data and Business Intelligence are becoming an increasingly important source of statistical information which is used as a vital part of the critical decision-making…
Overcome Data Testing Challenges Today all decisions in an organization are being made on the data available to them. Hence it has become critical to ensure that the does is free of any issues or defects. The way to ensure that there are no data issues and it is fit for the business, is to…
AML Software Implementation & Production Monitoring with iCEDQ DataOps Platform iCEDQ accelerates AML software implementation and prevents false positive signals in AML operations. AML software is a downstream system that consumes data from multiple sources. AML software analyzes data based on compliance models. This results in suspicious activity reports. Further, they also monitor data for regulations such…
iCEDQ your Gatekeeper for Data Issues The Challenges: Today’s organizations have thousands of data integration (ETL) processes constantly moving silos of data from various operational and/or external data sources to downstream applications. Since the downstream system doesn’t have control over incoming data or the process, it can cause serious data issues due to: The quality of…
What Are The Challenges Of A Data Factory DataOps Platform for Integrated Data Testing & Production Monitoring DataOps is a set of practices and tools used by Big Data teams to increase velocity, reliability, and quality of data analytics. It emphasizes communication, collaboration, integration, automation, measurement and cooperation between data scientists, analysts, data/ETL (extract, transform,…
Auditing -The Missing Element in Data Management and Data Governance Perhaps the most astonishing fact, however, is that IT has been blind for so long to the need for monitoring and metering (Auditing) for data health, and yet this fundamental engineering concept. For instance, Figure 1 illustrates a centrifugal steam engine governor. Figure 1: The…
QA Challenges in Data Integration Projects Download Whitepaper from here QA Challenges Data Integration Projects Quality Assurance (QA) is a very important component of any data-centric application project. Projects such as data warehouse, data migration, ETL, Data Lakes and MDM are no exception. The Majority of these projects are the multi-year and multi-million dollar in nature…
