Monte Carlo

APRIL 24, 2023

Supporting high quality datasets with strong guarantees for data completeness and latency requires an extremely robust data ingestion platform that becomes particularly complex at scale. Upstream data evolution breaks pipelines. Missed Nishith’s 5 considerations?

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 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.

Metadata 52

Metadata High Quality Data Data Pipeline Datasets 52

Precisely

SEPTEMBER 9, 2024

How confident are you in the quality of your data? Across industries and business objectives, high-quality data is a must for innovation and data-driven decision-making that keeps you ahead of the competition. Can you trust it for fast, confident decision-making when you need it most?

Management 64

Management High Quality Data Structured Data Data Lake 64

Precisely

MARCH 4, 2024

From AI-generated briefs filled with inaccuracies to scandals that never were , these incidents highlight how easily inadequate data can create flawed results with significant business implications – while simultaneously demonstrating the importance of feeding your AI with trusted, high-quality data.

Amazon Web Services 72

Amazon Web Services Data Integration High Quality Data Government 72

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Precisely

AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies.

Data Governance 52

Data Governance Government High Quality Data Datasets 52

Data Engineering Weekly

AUGUST 25, 2024

Take Astro (the fully managed Airflow solution) for a test drive today and unlock a suite of features designed to simplify, optimize, and scale your data pipelines. Try For Free → Conference Alert: Data Engineering for AI/ML This is a virtual conference at the intersection of Data and AI.

Data Engineering 70

Data Engineering Data Engineer Engineering Database-centric 70

Wayne Yaddow

FEBRUARY 18, 2025

Selecting the strategies and tools for validating data transformations and data conversions in your data pipelines. Introduction Data transformations and data conversions are crucial to ensure that raw data is organized, processed, and ready for useful analysis.

Data Pipeline 52

Data Pipeline SQL Raw Data Python 52

Monte Carlo

APRIL 1, 2024

Data cleaning is an essential step to ensure your data is safe from the adage “garbage in, garbage out.” Because effective data cleaning best practices fix and remove incorrect, inaccurate, corrupted, duplicate, or incomplete data in your dataset; data cleaning removes the garbage before it enters your pipelines.

High Quality Data 52

High Quality Data Datasets Data Data Pipeline 52

Data Engineering Weekly

SEPTEMBER 15, 2024

link] Sponsored: IMPACT - Speaker Promo We know high-quality data is powerful. Napkin Math is a valuable resource for data platform engineers because it equips them with quick, intuitive calculations for estimating system performance and resource requirements. But can it predict presidential elections?

Data Engineering 67

Data Engineering Data Engineer Engineering Data Lake 67

Wayne Yaddow

FEBRUARY 6, 2025

Data Volumes & Complexity : Describes large-scale or intricate datasets that place heavy demands on storage, processing, and performance. Complex data structures (e.g., Conclusion Effectively managing data transformation challenges requires a proactive, structured approach that evolves alongside your data pipelines.

Data Pipeline 52

Data Pipeline Manufacturing High Quality Data Healthcare 52

Monte Carlo

FEBRUARY 27, 2024

Organizations need to connect LLMs with their proprietary data and business context to actually create value for their customers and employees. They need robust data pipelines, high-quality data, well-guarded privacy, and cost-effective scalability. Data engineers. Who can deliver?

Data Engineering 59

Data Engineering Data Engineer Engineering High Quality Data 59

Data Engineering Weekly

MARCH 3, 2024

Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

Data Engineering 67

Data Engineering Data Engineer Engineering Pipeline-centric 67

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Automated profiling tools can quickly detect anomalies or patterns indicating potential dataset integrity issues.

Building 52

Building Data Cleanse Data Governance Datasets 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. In some cases, organizations may benefit from adopting elements from both methodologies.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data Data Ingestion 52

Airbnb Tech

NOVEMBER 28, 2023

Enable full visibility into the quality of our offline data warehouse and individual data assets. Composing the Score Before diving into the nuances of measuring data quality, we drove alignment on the vision by defining our DQ Score guiding principles.

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. If you happen to be a user of these products, you already know about the results that high-quality data produces: more and happier customers, lower costs and higher efficiency, and compliance with complex regulations – to name just a few.

Data Integration 52

Data Integration High Quality Data BI Data 52

Monte Carlo

NOVEMBER 8, 2023

Expanding end-to-end coverage across batch, streaming, and RAG pipelines enables organizations to realize the full potential of their AI initiatives with trusted, high-quality data. Data Product Dashboard – Speed is critical to maintaining reliable data products.

Kafka 64

Kafka Database High Quality Data Cloud 64

Databand.ai

JULY 5, 2023

What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Data observability tools allow teams to detect issues such as missing values, duplicate records, or inconsistent formats early on before they affect downstream processes.

Data Pipeline 40

Data Pipeline Data Lake Data Warehouse Datasets 40

Monte Carlo

JANUARY 16, 2024

Table of Contents Solve data silos starting at the people-level Keep data governance approachable Oliver Gomes’ data governance best practices Manage and promote the value of high-quality data How will Generative AI impact data quality at Fox? The complexity of a modern data pipeline.

Government 52

Government High Quality Data Data Governance Entertainment 52

Monte Carlo

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? How Do You Maintain Data Integrity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Monte Carlo

DECEMBER 19, 2022

Here are the 7 must-have checks to improve data quality and ensure reliability for your most critical assets. Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption. The data is unique and free from duplicates.

High Quality Data 52

High Quality Data Data SQL Bytes 52

Monte Carlo

MAY 3, 2024

But even though the data landscape is evolving, many enterprise data organizations are still managing data quality the “old” way: with simple data quality monitoring. The basics haven’t changed: high-quality data is still critical to successful business operations.

High Quality Data 52

High Quality Data Data Architecture Media 52

Monte Carlo

OCTOBER 10, 2024

As the use of AI becomes more ubiquitous across data organizations and beyond, data quality rises in importance right alongside it. After all, you can’t have high-quality AI models without high-quality data feeding them. Table of Contents What Does an AI Data Quality Analyst Do?

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Monte Carlo

APRIL 26, 2022

DataOps was first spearheaded by large data-first companies such as Netflix, Uber, and Airbnb that had adopted continuous integration / continuous deployment (CI/CD) principles, even building open source tools to foster their growth for data teams. Monitor : Continuously monitoring and alerting for any anomalies in the data.

IT 64

IT Data Pipeline Data Engineering Data Engineer 64

Monte Carlo

JANUARY 10, 2024

On the other hand, “Can the marketing team easily segment the customer data for targeted communications?” usability) would be about extrinsic data quality. Use of Data Quality Tools Refresh your intrinsic data quality with data observability 1.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Monte Carlo

OCTOBER 5, 2023

Ensure data quality Even if there are no errors during the ETL process, you still have to make sure the data meets quality standards. High-quality data is crucial for accurate analysis and informed decision-making. Your data pipelines will thank you.

Data Cleanse 52

Data Cleanse ETL Tools Datasets Utilities 52

Precisely

AUGUST 15, 2024

An increasing number of GenAI tools use large language models that automate key data engineering, governance, and master data management tasks. These tools can generate automated outputs including SQL and Python code, synthetic datasets, data visualizations, and predictions – significantly streamlining your data pipeline.

Government 64

Government Business Analyst High Quality Data Data 64

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

DataKitchen

JANUARY 3, 2022

They need high-quality data in an answer-ready format to address many scenarios with minimal keyboarding. What they are getting from IT and other data sources is, in reality, poor-quality data in a format that requires manual customization. A lot of business analytic teams are constantly firefighting.

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98

Databand.ai

AUGUST 30, 2023

Data Accuracy vs Data Integrity: Key Similarities Contribution to Data Quality Data accuracy and data integrity are both essential components of data quality. As mentioned earlier, data quality encompasses a range of attributes, including accuracy, consistency, completeness, and timeliness.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Monte Carlo

FEBRUARY 8, 2024

GigaOm GigaOm’s Data Observability Radar Report covers the problem data observability tools look to solve saying, “Data observability is critical for countering, if not eliminating, data downtime, in which the results of analytics or the performance of applications are compromised because of unhealthy, inaccurate data.”

BI 59

BI Data Warehouse Data Pipeline Data 59

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Inconsistent data: Inconsistencies within a dataset can indicate inaccuracies.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Monte Carlo

AUGUST 30, 2023

A passing test means you’ve improved the trustworthiness of your data. Schedule and automate You’ll need to run schema tests continuously to keep up with your ever-changing data. If your datasets are updated or refreshed daily, you’ll want to run your schema tests on a similar schedule.

SQL 52

SQL Datasets Database High Quality Data 52

Monte Carlo

AUGUST 8, 2023

By applying rules and checks, data validation testing verifies the data meets predefined standards and business requirements to help prevent data quality issues and data downtime. From this perspective, the data validation process looks a lot like any other DataOps process.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. It can show me how it built that chart, which dataset it used, and show me the metadata.”

Database-centric 52

Database-centric Pipeline-centric Metadata Unstructured Data 52