Sisense

AUGUST 28, 2020

All these pitfalls are avoidable with the right data integrity policies in place. Means of ensuring data integrity. Data integrity can be divided into two areas: physical and logical. Physical data integrity refers to how data is stored and accessed. Data integrity: A process and a state.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

AWS Big Data

FEBRUARY 6, 2023

For more details on how to configure and schedule the log collector, refer to the yarn-log-collector GitHub repo. Transform the YARN job history logs from JSON to CSV After obtaining YARN logs, you run a YARN log organizer, yarn-log-organizer.py, which is a parser to transform JSON-based logs to CSV files.

Dashboards 78

Dashboards Optimization Data Lake Cost-Benefit 78

Peter James Thomas

OCTOBER 30, 2018

All the references I can find to it are modern pieces comparing it to the CDO role, so perhaps it is apochryphal. It may well be that one thing that a CDO needs to get going is a data transformation programme. This may purely be focused on cultural aspects of how an organisation records, shares and otherwise uses data.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

AWS Big Data

NOVEMBER 15, 2023

For more information on this foundation, refer to A Detailed Overview of the Cost Intelligence Dashboard. Additionally, it manages table definitions in the AWS Glue Data Catalog , containing references to data sources and targets of extract, transform, and load (ETL) jobs in AWS Glue.

Dashboards 97

Dashboards Analytics Metadata Data Warehouse 97

AWS Big Data

NOVEMBER 13, 2023

Creating an external schema from the data share database on the consumer, mirroring that of the producer cluster with identical names. Testing: Conducting an internal week-long regression testing and auditing process to meticulously validate all data points by running the same workload and twice the workload.

Data Warehouse 94

Data Warehouse Data Lake Analytics Data Science 94

AWS Big Data

NOVEMBER 16, 2023

Building a starter version of anything can often be straightforward, but building something with enterprise-grade scale, security, resiliency, and performance typically requires knowledge of and adherence to battle-tested best practices, and using the right tools and features in the right scenario. Data Vault 2.0

Enterprise 86

Enterprise Data Warehouse Data Lake Optimization 86

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130