Wayne Yaddow

MARCH 28, 2025

Different schemas, naming standards, and data definitions are frequently used by disparate repository source systems, which can lead to datasets that are incompatible or conflicting. To guarantee uniformity among datasets and enable precise integration, consistent data models and terminology must be established.

Data Integration 52

Data Integration Data Governance Government Datasets 52

Precisely

JANUARY 15, 2024

When you delve into the intricacies of data quality, however, these two important pieces of the puzzle are distinctly different. Knowing the distinction can help you to better understand the bigger picture of data quality. What Is Data Validation? Read What Is Data Verification, and How Does It Differ from Validation?

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Data Validation Database Business Intelligence Data Integration 114

Data Engineering Weekly

FEBRUARY 2, 2025

DeepSeek development involves a unique training recipe that generates a large dataset of long chain-of-thought reasoning examples, utilizes an interim high-quality reasoning model, and employs large-scale reinforcement learning (RL). Many articles explain how DeepSeek works, and I found the illustrated example much simpler to understand.

Data Engineer 59

Data Engineer Data Engineering Engineering Data Lake 59

Monte Carlo

JULY 30, 2024

In this article, we’ll dive into the six commonly accepted data quality dimensions with examples, how they’re measured, and how they can better equip data teams to manage data quality effectively. Table of Contents What are Data Quality Dimensions? What are the 7 Data Quality Dimensions?

Data Validation 96

Data Validation Datasets Medical Raw Data 96

Striim

APRIL 18, 2025

Many organizations struggle with: Inconsistent data formats : Different systems store data in varied structures, requiring extensive preprocessing before analysis. Siloed storage : Critical business data is often locked away in disconnected databases, preventing a unified view.

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High Quality Data Business Intelligence Unstructured Data Data Pipeline 52

Towards Data Science

JANUARY 7, 2024

If the data changes over time, you might end up with results you didn’t expect, which is not good. To avoid this, we often use data profiling and data validation techniques. Data profiling gives us statistics about different columns in our dataset. It lets you log all sorts of data. So let’s dive in!

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Precisely

NOVEMBER 18, 2024

And of course, getting your data up to the task is the other critical piece of the AI readiness puzzle. Yoğurtçu identifies three critical steps that you should take to prepare your data for AI initiatives: Identify all critical and relevant datasets , ensuring that those used for AI training and inference are accounted for.

Data Analytics 72

Data Analytics Data Governance Data Integration Government 72

Databand.ai

MAY 30, 2023

Here are several reasons data quality is critical for organizations: Informed decision making: Low-quality data can result in incomplete or incorrect information, which negatively affects an organization’s decision-making process. Learn more in our detailed guide to data reliability 6 Pillars of Data Quality 1.

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Data Cleanse Datasets Data Governance Data Validation 85

Ascend.io

NOVEMBER 14, 2024

Distributed Data Processing Frameworks Another key consideration is the use of distributed data processing frameworks and data planes like Databricks , Snowflake , Azure Synapse , and BigQuery. These platforms enable scalable and distributed data processing, allowing data teams to efficiently handle massive datasets.

Data Pipeline 52

Data Pipeline Amazon Web Services Data Integration Data 52

Striim

JANUARY 17, 2025

To achieve accurate and reliable results, businesses need to ensure their data is clean, consistent, and relevant. This proves especially difficult when dealing with large volumes of high-velocity data from various sources.

Healthcare 52

Healthcare Google Cloud Government Data Validation 52

Data Engineering Podcast

MAY 26, 2024

Data center migration: Physical relocation or consolidation of data centers Virtualization migration: Moving from physical servers to virtual machines (or vice versa) Section 3: Technical Decisions Driving Data Migrations End-of-life support: Forced migration when older software or hardware is sunsetted Security and compliance: Adopting new platforms (..)

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Precisely

DECEMBER 13, 2023

Read our eBook Validation and Enrichment: Harnessing Insights from Raw Data In this ebook, we delve into the crucial data validation and enrichment process, uncovering the challenges organizations face and presenting solutions to simplify and enhance these processes.

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Raw Data Entertainment Data Validation Education 96

Precisely

NOVEMBER 18, 2024

And of course, getting your data up to the task is the other critical piece of the AI readiness puzzle. Yoğurtçu identifies three critical steps that you should take to prepare your data for AI initiatives: Identify all critical and relevant datasets , ensuring that those used for AI training and inference are accounted for.

Data Analytics 59

Data Analytics Data Governance Government Data Integration 59

Precisely

NOVEMBER 20, 2023

We work with organizations around the globe that have diverse needs but can only achieve their objectives with expertly curated data sets containing thousands of different attributes. Enrichment: The Secret to Supercharged AI You’re not just improving accuracy by augmenting your datasets with additional information.

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Raw Data Insurance Data Portfolio 121

Pinterest Engineering

SEPTEMBER 26, 2023

At the end of this pipeline, the data with training features are ingested in the database. Figure 1: hybrid logging for features On a daily basis, the features are joined with the labels to produce the final training dataset. Performance Validation We validate the auto-retraining quality at two places throughout the pipeline.

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Software Engineer Software Engineering Machine Learning Datasets 105

Towards Data Science

JULY 8, 2024

Williams on Unsplash Data pre-processing is one of the major steps in any Machine Learning pipeline. Tensorflow Transform helps us achieve it in a distributed environment over a huge dataset. This dataset is free to use for commercial and non-commercial purposes. A description of the dataset is shown in the below figure.

Data Preparation 70

Data Preparation Datasets Metadata Data Ingestion 70

U-Next

MARCH 7, 2023

Define Data Wrangling The process of data wrangling involves cleaning, structuring, and enriching raw data to make it more useful for decision-making. Data is discovered, structured, cleaned, enriched, validated, and analyzed. Values significantly out of a dataset’s mean are considered outliers.

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Data Mining Scala Government Data Governance 96

Precisely

OCTOBER 5, 2023

Data integrity is all about building a foundation of trusted data that empowers fast, confident decisions that help you add, grow, and retain customers, move quickly and reduce costs, and manage risk and compliance – and you need data enrichment to optimize those results. Read Why is Data Enrichment Important?

Raw Data 69

Raw Data Insurance Datasets Telecommunication 69

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming, and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools.

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Data Cleanse Data Validation Data Pipeline Data Governance 52

Rockset

MARCH 1, 2023

The concurrent queries will not see the effect of the data loads until the data load is complete, creating 10s of minutes of data lags. OLTP databases aren’t built to ingest massive volumes of data streams and perform stream processing on incoming datasets. So they are not suitable for real-time analytics.

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Data Ingestion Database Architecture SQL 144

DataKitchen

APRIL 13, 2021

Databand — Data pipeline performance monitoring and observability for data engineering teams. . Soda Data Monitoring — Soda tells you which data is worth fixing. Soda doesn’t just monitor datasets and send meaningful alerts to the relevant teams. Observe, optimize, and scale enterprise data pipelines. .

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Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

LinkedIn Engineering

JUNE 2, 2023

For example, if a media outlet uses incorrect data from an Economic Graph report in their reporting, it could result in a loss of trust among their readership. We currently address over 50 requests for our data and insights per month. This is particularly useful for the Asimov team to see dataset health over time at a glance quickly.

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Building Banking Datasets Media 75

Monte Carlo

JANUARY 10, 2024

Data Profiling 2. Data Cleansing 3. Data Validation 4. Data Auditing 5. Data Governance 6. Use of Data Quality Tools Refresh your intrinsic data quality with data observability 1. Data Profiling Data profiling is getting to know your data, warts and quirks and secrets and all.

Data Cleanse 52

Data Cleanse Data Engineer Data Engineering Engineering 52

Scott Logic

SEPTEMBER 13, 2024

At their core, ML models learn from data. They are trained on large datasets to recognise patterns and make predictions or decisions based on new information. During the model evaluation phase (validation mode), we will use a labelled dataset of emails to calculate metrics like accuracy, precision and recall.

Medical 52

Medical Hospitality Datasets Machine Learning 52

The Modern Data Company

JULY 18, 2023

High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. Data products should incorporate mechanisms for data validation, cleansing, and ongoing monitoring to maintain data integrity.

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Data Governance High Quality Data Government Data 69

Databand.ai

AUGUST 30, 2023

Accurate data ensures that these decisions and strategies are based on a solid foundation, minimizing the risk of negative consequences resulting from poor data quality. There are various ways to ensure data accuracy. Data cleansing involves identifying and correcting errors, inconsistencies, and inaccuracies in data sets.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Wayne Yaddow

MARCH 3, 2025

Building a Resilient Pre-Production Data Validation Framework Proactively validating data pipelines before production is the key to reducing data downtime, improving reliability, and ensuring accurate business insights. Enhances the robustness of data transformations by ensuring they handle edge cases effectively.

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Data Pipeline Data Validation Data Datasets 52

dbt Developer Hub

SEPTEMBER 28, 2022

So let’s say that you have a business question, you have the raw data in your data warehouse , and you’ve got dbt up and running. You’re in the perfect position to get this curated dataset completed quickly! You’ve got three steps that stand between you and your finished curated dataset. Or are you?

Engineering 52

Engineering Raw Data Datasets BI 52

Databand.ai

AUGUST 30, 2023

Validity: Adherence to predefined formats, rules, or standards for each attribute within a dataset. Uniqueness: Ensuring that no duplicate records exist within a dataset. Integrity: Maintaining referential relationships between datasets without any broken links.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Monte Carlo

OCTOBER 10, 2024

Table of Contents What Does an AI Data Quality Analyst Do? An AI Data Quality Analyst should be comfortable with: Data Management : Proficiency in handling large datasets. Data Cleaning and Preprocessing : Techniques to identify and remove errors. Attention to Detail : Critical for identifying data anomalies.

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Unstructured Data Google Cloud Machine Learning ETL Tools 52

Knowledge Hut

JULY 26, 2023

Consider exploring relevant Big Data Certification to deepen your knowledge and skills. What is Big Data? Big Data is the term used to describe extraordinarily massive and complicated datasets that are difficult to manage, handle, or analyze using conventional data processing methods.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Databand.ai

JULY 6, 2023

By routinely conducting data integrity tests, organizations can detect and resolve potential issues before they escalate, ensuring that their data remains reliable and trustworthy. Data integrity monitoring can include periodic data audits, automated data integrity checks, and real-time data validation.

Data Integration 52

Data Integration Process Data Validation Data Governance 52

Monte Carlo

OCTOBER 2, 2024

Tracking data lineage is especially important when working with Python, as the language is so easy to use that you can end up digging your own grave if you start making large unintended changes to your most important datasets. Automated Tools for Python Data Lineage So how can we easily add data lineage to our Python workflows?

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Data Engineering Weekly

MARCH 10, 2024

Pradheep Arjunan - Shared insights on AZ's journey from on-prem to the cloud data warehouses. Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects.

Data Engineer 52

Data Engineer Data Engineering Engineering Datasets 52

Precisely

AUGUST 29, 2024

To maximize your investments in AI, you need to prioritize data governance, quality, and observability. Solving the Challenge of Untrustworthy AI Results AI has the potential to revolutionize industries by analyzing vast datasets and streamlining complex processes – but only when the tools are trained on high-quality data.

Data Governance 52

Data Governance Government High Quality Data Datasets 52

Knowledge Hut

JANUARY 25, 2024

What is Data Cleaning? Data cleaning, also known as data cleansing, is the essential process of identifying and rectifying errors, inaccuracies, inconsistencies, and imperfections in a dataset. It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data.

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