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

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

Monte Carlo

JUNE 20, 2024

It’s the mantra for data teams, and it underlines the importance of data quality anomaly detection for any organization. The quality of the input affects the quality of the output – and in order for data teams to produce high-quality data products, they need high-quality data from the very start.

High Quality Data 52

High Quality Data Machine Learning Algorithm Datasets 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

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 Pipeline-centric Engineering 67

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

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

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

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 BI 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

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

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

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

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

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

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

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 High Quality Data 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

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

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 Pipeline Data Warehouse Data 59

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

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

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

AUGUST 9, 2023

Bad data in—bad data products out. And that puts data quality at the top of every CTO’s priority list. In this post, we’ll look at 8 of the most common data quality issues affecting data pipelines, how they happen, and what you can do to find and resolve them. As in all things, it depends.

Finance 52

Finance High Quality Data SQL Data 52

Monte Carlo

JUNE 20, 2023

So, just to be safe you take the extra time to recreate the dataset. Data products and trustworthiness According to Zhamak Dehgahi, data products should be discoverable, addressable, trustworthy, self-describing, interoperable and secure. In our experience, most data products only support one or two use cases.

Building 52

Building Data Datasets High Quality Data 52

DataKitchen

OCTOBER 2, 2023

This type of Data Journey provides a continuous monitoring framework that can be augmented by data quality checks (such as those automatically generated by DataKitchen’s TestGen product ), ensuring the quality of datasets and tables. Did each technology start and end successfully and on time?)

Data Ingestion 40

Data Ingestion Data Government Datasets 40

ProjectPro

FEBRUARY 21, 2023

Google Cloud Certified Professional Data Engineer Certifications An individual is fit for taking the GCP Data Engineering certification exam if he/she- Has more than three years of prior data engineering experience, including at least one year of solution design and management using Google Cloud.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Ascend.io

APRIL 27, 2023

Going into the Data Pipeline Automation Summit 2023, we were thrilled to connect with our customers and partners and share the innovations we’ve been working on at Ascend. The summit explored the future of data pipeline automation and the endless possibilities it presents.

Data Pipeline 52

Data Pipeline Pipeline-centric Data Validation Data Engineering 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

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52