Data Engineering Weekly

MAY 27, 2023

The top 75% percentile jobs in Amsterdam, London, and Dublin pay nearly 50% more than those in Berlin [link] Trivago: Implementing Data Validation with Great Expectations in Hybrid Environments The article by Trivago discusses the integration of data validation with Great Expectations.

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RudderStack

MAY 18, 2021

In this post, you will know about common challenges to data validation and how RudderStack can break them down & make it a smooth step in your workflow

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KDnuggets

AUGUST 29, 2023

New features and concepts.

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Acceldata

DECEMBER 5, 2022

ValidationLearn how a data observability solution can automatically clean and validate incoming data pipelines in real-time.

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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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Snowflake

NOVEMBER 19, 2024

Additionally, Snowflake provides robust key management solutions once the data is under its management, further enhancing security and control over sensitive information. In addition, Private Link and limiting HTTP request acceptance from certain IP addresses (also known as “IP whitelisting”) help to limit data access.

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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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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?

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Towards Data Science

FEBRUARY 6, 2023

Pydantic models expect to receive JSON-like data, so any data we pass to our model for validation must be a dictionary. This really allows a lot of granularity with data validation without writing a ton of code. HOME: str GUILD: str PAY: int = pydantic.Field(.,

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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. Introducing checks like format validation (e.g.,

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Christophe Blefari

MARCH 15, 2024

Understand how BigQuery inserts, deletes and updates — Once again Vu took time to deep dive into BigQuery internal, this time to explain how data management is done. Pandera, a data validation library for dataframes, now supports Polars.

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Monte Carlo

JANUARY 10, 2024

In this article, we present six intrinsic data quality techniques that serve as both compass and map in the quest to refine the inner beauty of your data. Data Profiling 2. Data Cleansing 3. Data Validation 4. Data Auditing 5. Data Governance 6. Table of Contents 1.

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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.

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Cloudyard

NOVEMBER 17, 2024

Technical: Step 2: Creating Custom Data Metric Functions We will define custom DMFs to validate specific data quality aspects: a) High Transaction Amount Check: CREATE OR REPLACE DATA METRIC FUNCTION CHECK_HIGH_TRANSACTION( TABLE_NAME TABLE(TRANSACTION_AMOUNT NUMBER) ) RETURNS NUMBER AS $$ SELECT COUNT(*) FROM TABLE_NAME WHERE TRANSACTION_AMOUNT > (..)

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Databand.ai

JUNE 20, 2023

To achieve data integrity, organizations must implement various controls, processes, and technologies that help maintain the quality of data throughout its lifecycle. These measures include data validation, data cleansing, data integration, and data security, among others.

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Precisely

FEBRUARY 23, 2024

Only 26% regard this tactic to be highly effective, whereas more than 40% indicate a strong preference for automated systems and scalable data validation tools. Scalable Data Quality Systems Drive Profitability These findings should not come as a surprise.

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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.

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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.

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Ascend.io

OCTOBER 28, 2024

It is important to note that normalization often overlaps with the data cleaning process, as it helps to ensure consistency in data formats, particularly when dealing with different sources or inconsistent units. Data Validation Data validation ensures that the data meets specific criteria before processing.

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Christophe Blefari

FEBRUARY 18, 2023

Benn thinks about the role of a data team in the business decisional journey. Balancing quality and coverage with our data validation framework — Dropbox tech team developed a data validation framework in SQL. The validation runs as an Airflow operator every time a new data has been ingested.

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Databand.ai

JUNE 21, 2023

By doing so, data integrity tools enable organizations to make better decisions based on accurate, trustworthy information. The three core functions of a data integrity tool are: Data validation: This process involves checking the data against predefined rules or criteria to ensure it meets specific standards.

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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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Data Engineering Weekly

MARCH 31, 2024

The Netflix blog emphasizes the importance of finding the zombie data and the system design around deleting unused data. Data is only as good as the business value it provides, and the business value can only be seen from the consumer's perspective.

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Databand.ai

JULY 3, 2023

The value of that trust is why more and more companies are introducing Chief Data Officers – with the number doubling among the top publicly traded companies between 2019 and 2021, according to PwC. In this article: Why is data reliability important? Note that data validity is sometimes considered a part of data reliability.

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Databand.ai

AUGUST 30, 2023

It plays a critical role in ensuring that users of the data can trust the information they are accessing. There are several ways to ensure data consistency, including implementing data validation rules, using data standardization techniques, and employing data synchronization processes.

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Databand.ai

AUGUST 30, 2023

Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. DataOps tools help ensure data quality by providing features like data profiling, data validation, and data cleansing.

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Monte Carlo

OCTOBER 10, 2024

Attention to Detail : Critical for identifying data anomalies. Tools : Familiarity with data validation tools, data wrangling tools like Pandas , and platforms such as AWS , Google Cloud , or Azure. Data observability tools: Monte Carlo ETL Tools : Extract, Transform, Load (e.g., Informatica , Talend ).

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DataKitchen

MAY 14, 2024

Chris will overview data at rest and in use, with Eric returning to demonstrate the practical steps in data testing for both states. Session 3: Mastering Data Testing in Development and Migration During our third session, the focus will shift towards regression and impact assessment in development cycles.

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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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Data Engineering Weekly

APRIL 30, 2023

Watch a panel of data leaders as they discuss how to build strategies for measuring data team ROI. Watch On-demand Trivago: Implementing Data Validation with Great Expectations in Hybrid Environments The article by Trivago discusses the integration of data validation with Great Expectations.

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DataKitchen

JUNE 19, 2023

DataOps TestGen delivers simple, fast data quality test generation and execution. DataOps TestGen profiles your data and algorithmically generates dozens of data validations. Your Data Journey starts with verifying that you can trust your data and re-verifying it every time you refresh it in production.

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Scott Logic

SEPTEMBER 13, 2024

Validate the Data: Ensure that the training, validation and testing datasets are represent real-world situations the model will face. Proper data validation is very important to prevent garbage-in, garbage-out scenarios. Check for issues like missing values, duplicates and biases.

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The Pragmatic Engineer

OCTOBER 17, 2024

Web frontend:   Angular 17 with server-side rendering support (SSR).

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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. The post Data testing tools: Key capabilities you should know appeared first on Databand.

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Databand.ai

AUGUST 30, 2023

It’s also important during data migration and integration projects, where data is moved or transformed and must maintain its integrity. Data Validation Testing Data validation testing ensures that the data entered into the system meets the predefined rules and requirements.

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RandomTrees

NOVEMBER 5, 2024

Sometimes from form fill, we get only email id; so we need to find other fields such as first name, last name, and company to have the data inside Salesforce. – Import Data: One may use Salesforce’s Data Import Wizard or Data Loader for bulk imports.

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Knowledge Hut

JULY 26, 2023

Data integration and cleansing processes need to handle large-scale data effectively and account for the complexities introduced by data variety. Data Quality Assurance: Verifying the quality of data requires rigorous processes and techniques.

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