Wayne Yaddow

MARCH 5, 2025

In this post, well see the fundamental procedures, tools, and techniques that data engineers, data scientists, and QA/testing teams use to ensure high-quality data as soon as its deployed. First, we look at how unit and integration tests uncover transformation errors at an early stage.

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Testing Data Transformation Statistics Metadata 52

Wayne Yaddow

MARCH 3, 2025

Photo by CDC on Unsplash Many data pipeline failures and quality issues that are detected by data observability tools in production could have been prevented earlier in the pipeline lifecycle with better pre-production testing strategies. Crucial for time-sensitive analytics and reporting processes.

Data Quality 52

Data Quality Strategy ROI Testing 52

DataKitchen

NOVEMBER 10, 2023

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

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Data Quality Testing Data Lake Data Integration 139

Wayne Yaddow

FEBRUARY 4, 2025

Its EssentialVerifying Data Transformations (Part4) Uncovering the leading problems in data transformation workflowsand practical ways to detect and preventthem In Parts 13 of this series of blogs, categories of data transformations were identified as among the top causes of data quality defects in data pipeline workflows.

Data Transformation 52

Data Transformation Testing Data Quality Strategy 52

Wayne Yaddow

MARCH 12, 2025

How GX helps data teams validate, test, and monitor complex data pipelines Introduction Data flows from diverse sources, and transformations are becoming increasingly complex. Great Expectations can enable a wide range of data transformations and conversion operations.

Data Transformation 52

Data Transformation Data Quality Testing Data Warehouse 52

DataKitchen

APRIL 26, 2021

GSK had been pursuing DataOps capabilities such as automation, containerization, automated testing and monitoring, and reusability, for several years. At Workiva, they recognized that they are only as good as their data, so they centered their initial DataOps efforts around lowering errors. Early Results are Positive.

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Measurement Metrics Data-driven Dashboards 246

Wayne Yaddow

MARCH 14, 2025

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52

Wayne Yaddow

FEBRUARY 26, 2025

AI is transforming how senior data engineers and data scientists validate data transformations and conversions. Artificial intelligence-based verification approaches aid in the detection of anomalies, the enforcement of data integrity, and the optimization of pipelines for improved efficiency.

Data Transformation 52

Data Transformation Testing Data-driven Data Quality 52

Sisense

AUGUST 28, 2020

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. Data integrity: A process and a state.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

AWS Big Data

NOVEMBER 9, 2023

A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

Data Warehouse 126

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Jen Stirrup

SEPTEMBER 30, 2021

Data Understanding is a crucial aspect of all of these areas, and the process will not proceed properly without it. From the perspective of CRISP-DM, this piece involves a number of activities: Collecting Initial Data Describing Data Exploring Data Verifying Data Quality. Evaluating the Model.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

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Metadata Data Lake Visualization Data Quality 111

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. This will enable right-sizing the Redshift data warehouse to meet workload demands cost-effectively. Additional considerations – Factor in additional tasks beyond schema conversion.

Data Warehouse 115

Data Warehouse KPI Optimization Cost-Benefit 115

AWS Big Data

FEBRUARY 8, 2023

It’s common to ingest multiple data sources into Amazon Redshift to perform analytics. Often, each data source will have its own processes of creating and maintaining data, which can lead to data quality challenges within and across sources. Answering questions as simple as “How many unique customers do we have?”

Data Quality 98

Data Quality Testing Data Warehouse Unstructured Data 98

Smart Data Collective

APRIL 29, 2020

The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. Microsoft Azure.

Management 97

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AWS Big Data

JULY 27, 2023

DataBrew is a visual data preparation tool that enables you to clean and normalize data without writing any code. The over 200 transformations it provides are now available to be used in an AWS Glue Studio visual job. Now that we identified the data quality issues to address, we need to decide how to deal with each case.

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Visualization Cost-Benefit Data Quality Publishing 98

Alation

OCTOBER 18, 2022

Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Data Transformation in the Modern Data Stack. How did the data transform exactly?

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Speed changes everything, and continuous testing across the entire CI/CD lifecycle is the key. Tricentis instills that confidence by providing software tools that enable Agile Continuous Testing (ACT) at scale.

Software 52

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erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.

Data Governance 40

Data Governance Risk Metadata Management 40

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. They can better understand data transformations, checks, and normalization. Transparency is key.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

Peter James Thomas

OCTOBER 30, 2018

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. It may be to build a new (or a first) Data Architecture. It may be to build a new (or a first) Data Architecture.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

AWS Big Data

APRIL 5, 2023

“Each of these tools were getting data from a different place, and that’s where it gets difficult,” says Jeroen Minnaert, head of data at Showpad. “If If each tool tells a different story because it has different data, we won’t have alignment within the business on what this data means.”

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Dashboards Reporting Cost-Benefit Visualization 52

DataRobot Blog

MARCH 10, 2022

The first step in building a model that can predict machine failure and even recommend the next best course of action is to aggregate, clean, and prepare data to train against. This task may require complex joins, aggregations, filtering, window functions, and many other data transformations against extremely large-scale data sets.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

Alation

OCTOBER 28, 2021

This, in turn, empowers data leaders to better identify and develop new revenue streams, customize patient offerings, and use data to optimize operations. To make good on this potential, healthcare organizations need to understand their data and how they can use it. Why Is Data Governance in Healthcare Important?

Data Governance 52

Data Governance Measurement Data Quality Metrics 52

Smarten

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Visualization 52

AWS Big Data

DECEMBER 17, 2024

Prevent the inclusion of invalid values in categorical data and process data without any data loss. Conduct data quality tests on anonymized data in compliance with data policies Conduct data quality tests to quickly identify and address data quality issues, maintaining high-quality data at all times.

Data Quality 90

Data Quality Testing Metrics Optimization 90

Wayne Yaddow

MARCH 28, 2025

Unleashing GenAIEnsuring Data Quality at Scale (Part2) Transitioning from individual repository source systems to consolidated AI LLM pipelines, the importance of automated checks, end-to-end observability, and compliance with enterprise businessrules. First: It is critical to set up a thorough data inventory and assessment procedure.

Data Quality 52

Data Quality Data Integration Data Governance Metadata 52

AWS Big Data

APRIL 29, 2025

While enabling organization-wide efficiency, the team also applied these principles to the data architecture, making sure that CLEA itself operates frugally. After evaluating various tools, we built a serverless data transformation pipeline using Amazon Athena and dbt. The Source stage maintains raw data in its original form.

Data Transformation 54

Data Transformation Cost-Benefit Testing Data Lake 54

BI-Survey

MARCH 6, 2025

For data management teams, achieving more with fewer resources has become a familiar challenge. While efficiency is a priority, data quality and security remain non-negotiable. Developing and maintaining data transformation pipelines are among the first tasks to be targeted for automation.

Data Warehouse 52

Data Warehouse Metadata Unstructured Data Data-driven 52

AWS Big Data

FEBRUARY 25, 2025

If data mapping has been enabled within the data processing job, then the structured data is prepared based on the given schema. This output is passed to next phase where data transformations and business validations can be applied. After this step, data is loaded to specified target.

Data Processing 56

Data Processing Machine Learning Data-driven Cost-Benefit 56

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing can be done through various methods, such as data profiling, Statistical Process Control, and quality checks. Are problems with data tests?

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