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. What is data integrity?

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

DataKitchen

DECEMBER 29, 2022

The Terms and Conditions of a Data Contract are Automated Production Data Tests. A data contract is a formal agreement between two parties that defines the structure and format of data that will be exchanged between them. The best data contract is an automated production data test.

Testing 130

Testing Statistics Data Quality Data Integration 130

Sisense

MARCH 9, 2020

Learn about the changes they’re making to not just remain competitive, but win in the future to stand the test of time. One of the main goals of a digital transformation is to empower everyone within an organization to make smarter, data-driven decisions. More data, more problems. Actionable analytics increase adoption.

Data Integration 93

Data Integration Enterprise Slice and Dice Digital Transformation 93

Sisense

SEPTEMBER 23, 2019

Mike Cohn’s famous Test Pyramid places API tests at the service level (integration), which suggests that around 20% or more of all of our tests should focus on APIs (the exact percentage is less important and varies based on our needs). So the importance of API testing is obvious. API test actions.

Testing 89

Testing Strategy Modeling Publishing 89

DataKitchen

JANUARY 25, 2022

DataOps improves the robustness, transparency and efficiency of data workflows through automation. For example, DataOps can be used to automate data integration. Previously, the consulting team had been using a patchwork of ETL to consolidate data from disparate sources into a data lake.

Consulting 244

Consulting Testing Data Lake Data Quality 244

Octopai

AUGUST 3, 2022

It’s also a critical trait for the data assets of your dreams. What is data with integrity? Data integrity is the extent to which you can rely on a given set of data for use in decision-making. Where can data integrity fall short? Too much or too little access to data systems.

Data Integration 98

Data Integration Manufacturing Data Quality Data Governance 98

AWS Big Data

SEPTEMBER 4, 2024

Effective data analytics relies on seamlessly integrating data from disparate systems through identifying, gathering, cleansing, and combining relevant data into a unified format. Reverse ETL use cases are also supported, allowing you to write data back to Salesforce. Kamen Sharlandjiev is a Sr. His secret weapon?

Data Integration 89

Data Integration Data Lake Data-driven Cost-Benefit 89

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

To improve data reliability, enterprises were largely dependent on data-quality tools that required manual effort by data engineers, data architects, data scientists and data analysts.

Data Quality 130

Data Quality Dashboards Data-driven Software 130

O'Reilly on Data

OCTOBER 11, 2022

Selenium , the first tool for automated browser testing (2004), could be programmed to find fields on a web page, click on them or insert text, click “submit,” scrape the resulting web page, and collect results. But the core of the process is simple, and hasn’t changed much since the early days of web testing. What’s required?

Data Integration 351

Data Integration Insurance Dashboards Data-driven 351

AWS Big Data

JULY 26, 2023

Many AWS customers have integrated their data across multiple data sources using AWS Glue , a serverless data integration service, in order to make data-driven business decisions. Are there recommended approaches to provisioning components for data integration?

Data Integration 79

Data Integration Snapshot Testing Visualization 79

AWS Big Data

JANUARY 9, 2025

Simplified data corrections and updates Iceberg enhances data management for quants in capital markets through its robust insert, delete, and update capabilities. These features allow efficient data corrections, gap-filling in time series, and historical data updates without disrupting ongoing analyses or compromising data integrity.

Metadata 106

Metadata Snapshot Cost-Benefit Optimization 106

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics. Tie tests to alerts.

Testing 214

Testing Manufacturing Data Quality Statistics 214

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. It’s a very simple and powerful idea: simulate data that you find interesting and see what a model predicts for that data. 6] See: Testing and Debugging Machine Learning Models. [7]

Machine Learning 276

Machine Learning Modeling Testing Risk Management 276

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Wayne Yaddow

FEBRUARY 18, 2025

Introduction Data transformations and data conversions are crucial to ensure that raw data is organized, processed, and ready for useful analysis. However, these two processes are essentially distinct, and their testing needs differ in manyways.

Testing 52

Testing Data Transformation Data-driven Data Quality 52

Wayne Yaddow

FEBRUARY 21, 2025

Complex Data TransformationsTest Planning Best Practices Ensuring data accuracy with structured testing and best practices Photo by Taylor Vick on Unsplash Introduction Data transformations and conversions are crucial for data pipelines, enabling organizations to process, integrate, and refine raw data into meaningful insights.

Testing 52

Testing Data Transformation Data Quality Data Integration 52

O'Reilly on Data

DECEMBER 19, 2018

have a large body of tools to choose from: IDEs, CI/CD tools, automated testing tools, and so on. are only starting to exist; one big task over the next two years is developing the IDEs for machine learning, plus other tools for data management, pipeline management, data cleaning, data provenance, and data lineage.

Machine Learning 279

Machine Learning Software Metadata Testing 279

Business Over Broadway

SEPTEMBER 27, 2018

genetic counseling, genetic testing). Data Integration as your Customer Genome Project. Data Integration is an exercise in creating your customer genome. Using the 2×2 graphical approach to understanding data size (i.e., pharmacogenomics) and risk assessment of genetic disorders (e.g.,

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

CIO Business Intelligence

OCTOBER 24, 2024

The rest of their time is spent creating designs, writing tests, fixing bugs, and meeting with stakeholders. “So Forrester said gen AI will affect process design, development, and data integration, thereby reducing design and development time and the need for desktop and mobile interfaces.

ROI 127

ROI Data-driven Enterprise Experimentation 127

DataKitchen

JANUARY 19, 2022

The Matillion data integration and transformation platform enables enterprises to perform advanced analytics and business intelligence using cross-cloud platform-as-a-service offerings such as Snowflake. DataKitchen acts as a process hub that unifies tools and pipelines across teams, tools and data centers.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

DataKitchen

NOVEMBER 18, 2021

Write tests that catch data errors. Build observability and transparency into your end-to-end data pipelines. We talk about systemic change, and it certainly helps to have the support of management, but data engineers should not underestimate the power of the keyboard. Automate manual processes. Implement DataOps methods.

Testing 246

Testing Data Governance Measurement Software 246

CIO Business Intelligence

FEBRUARY 26, 2025

The problem is that, before AI agents can be integrated into a companys infrastructure, that infrastructure must be brought up to modern standards. In addition, because they require access to multiple data sources, there are data integration hurdles and added complexities of ensuring security and compliance.

IT 141

IT Enterprise Interactive Data Quality 141

O'Reilly on Data

MAY 21, 2019

Not surprisingly, data integration and ETL were among the top responses, with 60% currently building or evaluating solutions in this area. In an age of data-hungry algorithms, everything really begins with collecting and aggregating data. and managed services in the cloud. Metadata and artifacts needed for audits.

Machine Learning 279

Machine Learning Technology Deep Learning Data Science 279

DataKitchen

NOVEMBER 18, 2022

Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. A financial analogy: Data Quality is your Balance Sheet, Data Observability is your Cash Flow Statement.

Data Quality 130

Data Quality Testing Measurement Data Integration 130

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.

Data Quality 136

Data Quality Testing Data Lake Data Integration 136

CIO Business Intelligence

APRIL 8, 2025

Recognizing and rewarding data-centric achievements reinforces the value placed on analytical ability. Establishing clear accountability ensures data integrity. Implementing Service Level Agreements (SLAs) for data quality and availability sets measurable standards, promoting responsibility and trust in data assets.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

Wayne Yaddow

FEBRUARY 6, 2025

Photo by Mika Baumeister on Unsplash Introduction Data transformations are important processes in data engineering, enabling organizations to structure, enrich, and integrate data for analytics , reporting, and operational decision-making. Assess which factors apply most to your pipeline (e.g.,

Testing 52

Testing Data Transformation Data-driven Manufacturing 52

Wayne Yaddow

FEBRUARY 11, 2025

Managing tests of complex data transformations when automated data testing tools lack important features? Photo by Marvin Meyer on Unsplash Introduction Data transformations are at the core of modern business intelligence, blending and converting disparate datasets into coherent, reliable outputs.

Testing 52

Testing Data Transformation Data Quality Statistics 52

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. PyTest, JUnit,NUnit).

Testing 52

Testing Data Transformation Statistics Metadata 52

DataKitchen

AUGUST 16, 2024

Data Pipeline Observability: Optimizes pipelines by monitoring data quality, detecting issues, tracing data lineage, and identifying anomalies using live and historical metadata. This capability includes monitoring, logging, and business-rule detection.

Marketing 124

Marketing Data Quality Testing Metadata 124

AWS Big Data

DECEMBER 16, 2024

For each service, you need to learn the supported authorization and authentication methods, data access APIs, and framework to onboard and test data sources. This approach simplifies your data journey and helps you meet your security requirements.

Visualization 71

Visualization Data Processing Testing Publishing 71

DataKitchen

MAY 10, 2024

Production: During the production cycle, oversee multi-tool and multi-data set processes, such as dashboard production and warehouse building, ensuring that all components function correctly and the correct data is delivered to your customers. Quickly locate and address data or process errors before they affect downstream results.

Metrics 124

Metrics Testing Data Quality Dashboards 124

AWS Big Data

NOVEMBER 22, 2024

Testing these upgrades involves running the application and addressing issues as they arise. Each test run may reveal new problems, resulting in multiple iterations of changes. They then need to modify their Spark scripts and configurations, updating features, connectors, and library dependencies as needed. Python 3.7) to Spark 3.3.0

Cost-Benefit 95

Cost-Benefit Data-driven Software Testing 95

AWS Big Data

OCTOBER 11, 2024

It covers the essential steps for taking snapshots of your data, implementing safe transfer across different AWS Regions and accounts, and restoring them in a new domain. This guide is designed to help you maintain data integrity and continuity while navigating complex multi-Region and multi-account environments in OpenSearch Service.

Snapshot 96

Snapshot Dashboards Management Testing 96

DataKitchen

MAY 10, 2024

Production : During the production cycle, oversee multi-tool and multi-data set processes, such as dashboard production and warehouse building, ensuring that all components function correctly and the correct data is delivered to your customers. Verifying data completeness and conformity to predefined standards.

Data Quality 130

Data Quality Testing Software Dashboards 130

Domino Data Lab

JULY 20, 2022

Validations and tests are key elements to building machine learning pipelines you can trust. We've also talked about incorporating tests in your pipeline, which many data scientists find problematic. Enter Deepchecks - an open source Python package for testing and validating machine learning models and data.

Machine Learning 98

Machine Learning Testing Modeling Data Science 98

datapine

FEBRUARY 10, 2021

Your Chance: Want to test a social media dashboard software for free? A social media dashboard is an invaluable management tool that is used by professionals, managers, and companies to gather, optimize, and visualize important metrics and data from social channels such as Facebook, Twitter, LinkedIn, Instagram, YouTube, etc.

Dashboards 193

Dashboards Scorecard KPI Metrics 193